MNIST CNN Various methods 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function, absolute_import\n",
    "\n",
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import MNIST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-3-43d207ea2cee>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    }
   ],
   "source": [
    "import ssl\n",
    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of:\n",
      "- Training-set:\t\t55000\n",
      "- Test-set:\t\t10000\n",
      "- Validation-set:\t5000\n"
     ]
    }
   ],
   "source": [
    "print(\"Size of:\")\n",
    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "28*28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "128*500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training Parameters\n",
    "learning_rate = 0.01\n",
    "num_steps = 500\n",
    "batch_size = 128\n",
    "display_step = 10\n",
    "\n",
    "# Network Parameters\n",
    "num_input = 28*28 # MNIST data input (img shape: 28*28)\n",
    "num_classes = 10 # MNIST total classes (0-9 digits)\n",
    "dropout = 0.75 # Dropout, probability to keep units\n",
    "\n",
    "# tf Graph input\n",
    "X = tf.placeholder(tf.float32, [None, num_input])\n",
    "Y = tf.placeholder(tf.float32, [None, num_classes])\n",
    "keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create some wrappers for simplicity\n",
    "def conv2d(x, W, b, strides=1):\n",
    "    # Conv2D wrapper, with bias and relu activation\n",
    "    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n",
    "    x = tf.nn.bias_add(x, b)\n",
    "    return tf.nn.relu(x)\n",
    "\n",
    "\n",
    "def maxpool2d(x, k=2):\n",
    "    # MaxPool2D wrapper\n",
    "    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],\n",
    "                          padding='SAME')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create model\n",
    "def conv_net(x, weights, biases, dropout):\n",
    "    # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n",
    "    # Reshape to match picture format [Height x Width x Channel]\n",
    "    # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n",
    "    x = tf.reshape(x, shape=[-1, 28, 28, 1])\n",
    "\n",
    "    # Convolution Layer\n",
    "    conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n",
    "    # Max Pooling (down-sampling)\n",
    "    conv1 = maxpool2d(conv1, k=2)\n",
    "\n",
    "    # Convolution Layer\n",
    "    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n",
    "    # Max Pooling (down-sampling)\n",
    "    conv2 = maxpool2d(conv2, k=2)\n",
    "\n",
    "    # Fully connected layer\n",
    "    # Reshape conv2 output to fit fully connected layer input\n",
    "    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n",
    "    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n",
    "    fc1 = tf.nn.relu(fc1)\n",
    "    # Apply Dropout\n",
    "    fc1 = tf.nn.dropout(fc1, dropout)\n",
    "\n",
    "    # Output, class prediction\n",
    "    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3136"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7*7*64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-11-eecf4f6001e3>:26: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Store layers weight & bias\n",
    "weights = {\n",
    "    # 5x5 conv, 1 input, 32 outputs\n",
    "    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),\n",
    "    # 5x5 conv, 32 inputs, 64 outputs\n",
    "    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),\n",
    "    # fully connected, 7*7*64 inputs, 1024 outputs\n",
    "    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),\n",
    "    # 1024 inputs, 10 outputs (class prediction)\n",
    "    'out': tf.Variable(tf.random_normal([1024, 10]))\n",
    "}\n",
    "\n",
    "biases = {\n",
    "    'bc1': tf.Variable(tf.random_normal([32])),\n",
    "    'bc2': tf.Variable(tf.random_normal([64])),\n",
    "    'bd1': tf.Variable(tf.random_normal([1024])),\n",
    "    'out': tf.Variable(tf.random_normal([10]))\n",
    "}\n",
    "\n",
    "# Construct model\n",
    "logits = conv_net(X, weights, biases, keep_prob)\n",
    "prediction = tf.nn.softmax(logits)\n",
    "\n",
    "# Define loss and optimizer\n",
    "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n",
    "    logits=logits, labels=Y))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "train_op = optimizer.minimize(loss_op)\n",
    "\n",
    "\n",
    "# Evaluate model\n",
    "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "\n",
    "# Initialize the variables (i.e. assign their default value)\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1, Minibatch Loss= 69305.4766, Training Accuracy= 0.195\n",
      "Step 10, Minibatch Loss= 3529.1899, Training Accuracy= 0.773\n",
      "Step 20, Minibatch Loss= 2590.7986, Training Accuracy= 0.773\n",
      "Step 30, Minibatch Loss= 873.3868, Training Accuracy= 0.844\n",
      "Step 40, Minibatch Loss= 514.4443, Training Accuracy= 0.906\n",
      "Step 50, Minibatch Loss= 361.2151, Training Accuracy= 0.914\n",
      "Step 60, Minibatch Loss= 296.4946, Training Accuracy= 0.867\n",
      "Step 70, Minibatch Loss= 131.0615, Training Accuracy= 0.922\n",
      "Step 80, Minibatch Loss= 294.8167, Training Accuracy= 0.906\n",
      "Step 90, Minibatch Loss= 231.0658, Training Accuracy= 0.922\n",
      "Step 100, Minibatch Loss= 75.5180, Training Accuracy= 0.938\n",
      "Step 110, Minibatch Loss= 85.3986, Training Accuracy= 0.953\n",
      "Step 120, Minibatch Loss= 102.5100, Training Accuracy= 0.953\n",
      "Step 130, Minibatch Loss= 169.0390, Training Accuracy= 0.945\n",
      "Step 140, Minibatch Loss= 307.9094, Training Accuracy= 0.914\n",
      "Step 150, Minibatch Loss= 153.0907, Training Accuracy= 0.930\n",
      "Step 160, Minibatch Loss= 44.3576, Training Accuracy= 0.961\n",
      "Step 170, Minibatch Loss= 47.5673, Training Accuracy= 0.961\n",
      "Step 180, Minibatch Loss= 174.0984, Training Accuracy= 0.953\n",
      "Step 190, Minibatch Loss= 236.7015, Training Accuracy= 0.961\n",
      "Step 200, Minibatch Loss= 162.8949, Training Accuracy= 0.945\n",
      "Step 210, Minibatch Loss= 192.0583, Training Accuracy= 0.961\n",
      "Step 220, Minibatch Loss= 147.5957, Training Accuracy= 0.930\n",
      "Step 230, Minibatch Loss= 241.7962, Training Accuracy= 0.938\n",
      "Step 240, Minibatch Loss= 120.7185, Training Accuracy= 0.938\n",
      "Step 250, Minibatch Loss= 105.5297, Training Accuracy= 0.953\n",
      "Step 260, Minibatch Loss= 120.5326, Training Accuracy= 0.938\n",
      "Step 270, Minibatch Loss= 18.4075, Training Accuracy= 0.984\n",
      "Step 280, Minibatch Loss= 88.3618, Training Accuracy= 0.953\n",
      "Step 290, Minibatch Loss= 72.2479, Training Accuracy= 0.969\n",
      "Step 300, Minibatch Loss= 118.2674, Training Accuracy= 0.953\n",
      "Step 310, Minibatch Loss= 104.4597, Training Accuracy= 0.961\n",
      "Step 320, Minibatch Loss= 40.4839, Training Accuracy= 0.977\n",
      "Step 330, Minibatch Loss= 106.3445, Training Accuracy= 0.953\n",
      "Step 340, Minibatch Loss= 155.1844, Training Accuracy= 0.938\n",
      "Step 350, Minibatch Loss= 82.3907, Training Accuracy= 0.961\n",
      "Step 360, Minibatch Loss= 65.1730, Training Accuracy= 0.961\n",
      "Step 370, Minibatch Loss= 66.6435, Training Accuracy= 0.961\n",
      "Step 380, Minibatch Loss= 69.5033, Training Accuracy= 0.969\n",
      "Step 390, Minibatch Loss= 137.2411, Training Accuracy= 0.945\n",
      "Step 400, Minibatch Loss= 6.9032, Training Accuracy= 0.992\n",
      "Step 410, Minibatch Loss= 165.3415, Training Accuracy= 0.945\n",
      "Step 420, Minibatch Loss= 31.9514, Training Accuracy= 0.977\n",
      "Step 430, Minibatch Loss= 67.1285, Training Accuracy= 0.953\n",
      "Step 440, Minibatch Loss= 28.2028, Training Accuracy= 0.977\n",
      "Step 450, Minibatch Loss= 1.0171, Training Accuracy= 0.992\n",
      "Step 460, Minibatch Loss= 45.5460, Training Accuracy= 0.977\n",
      "Step 470, Minibatch Loss= 45.0269, Training Accuracy= 0.953\n",
      "Step 480, Minibatch Loss= 0.2721, Training Accuracy= 0.992\n",
      "Step 490, Minibatch Loss= 73.8225, Training Accuracy= 0.953\n",
      "Step 500, Minibatch Loss= 42.9905, Training Accuracy= 0.961\n",
      "Optimization Finished!\n",
      "Testing Accuracy: 0.98046875\n"
     ]
    }
   ],
   "source": [
    "# Start training\n",
    "with tf.Session() as sess:\n",
    "\n",
    "    # Run the initializer\n",
    "    sess.run(init)\n",
    "\n",
    "    for step in range(1, num_steps+1):\n",
    "        batch_x, batch_y = data.train.next_batch(batch_size)\n",
    "        # Run optimization op (backprop)\n",
    "        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y, keep_prob: dropout})\n",
    "        if step % display_step == 0 or step == 1:\n",
    "            # Calculate batch loss and accuracy\n",
    "            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n",
    "                                                                 Y: batch_y,\n",
    "                                                                 keep_prob: 1.0})\n",
    "            print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n",
    "                  \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n",
    "                  \"{:.3f}\".format(acc))\n",
    "\n",
    "    print(\"Optimization Finished!\")\n",
    "\n",
    "    # Calculate accuracy for 256 MNIST test images\n",
    "    print(\"Testing Accuracy:\", \\\n",
    "        sess.run(accuracy, feed_dict={X: data.test.images[:256],\n",
    "                                      Y: data.test.labels[:256],\n",
    "                                      keep_prob: 1.0}))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-13-8362a9fb3a3c>:8: calling argmax (from tensorflow.python.ops.math_ops) with dimension is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use the `axis` argument instead\n"
     ]
    }
   ],
   "source": [
    "# Placeholder variable for the input images\n",
    "x = tf.placeholder(tf.float32, shape=[None, 28*28], name='X')\n",
    "# Reshape it into [num_images, img_height, img_width, num_channels]\n",
    "x_image = tf.reshape(x, [-1, 28, 28, 1])\n",
    "\n",
    "# Placeholder variable for the true labels associated with the images\n",
    "y_true = tf.placeholder(tf.float32, shape=[None, 10], name='y_true')\n",
    "y_true_cls = tf.argmax(y_true, dimension=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_conv_layer(input, num_input_channels, filter_size, num_filters, name):\n",
    "    \n",
    "    with tf.variable_scope(name) as scope:\n",
    "        # Shape of the filter-weights for the convolution\n",
    "        shape = [filter_size, filter_size, num_input_channels, num_filters]\n",
    "\n",
    "        # Create new weights (filters) with the given shape\n",
    "        weights = tf.Variable(tf.truncated_normal(shape, stddev=0.05))\n",
    "\n",
    "        # Create new biases, one for each filter\n",
    "        biases = tf.Variable(tf.constant(0.05, shape=[num_filters]))\n",
    "\n",
    "        # TensorFlow operation for convolution\n",
    "        layer = tf.nn.conv2d(input=input, filter=weights, strides=[1, 1, 1, 1], padding='SAME')\n",
    "\n",
    "        # Add the biases to the results of the convolution.\n",
    "        layer += biases\n",
    "        \n",
    "        return layer, weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_pool_layer(input, name):\n",
    "    \n",
    "    with tf.variable_scope(name) as scope:\n",
    "        # TensorFlow operation for convolution\n",
    "        layer = tf.nn.max_pool(value=input, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
    "        \n",
    "        return layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_relu_layer(input, name):\n",
    "    \n",
    "    with tf.variable_scope(name) as scope:\n",
    "        # TensorFlow operation for convolution\n",
    "        layer = tf.nn.relu(input)\n",
    "        \n",
    "        return layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_fc_layer(input, num_inputs, num_outputs, name):\n",
    "    \n",
    "    with tf.variable_scope(name) as scope:\n",
    "\n",
    "        # Create new weights and biases.\n",
    "        weights = tf.Variable(tf.truncated_normal([num_inputs, num_outputs], stddev=0.05))\n",
    "        biases = tf.Variable(tf.constant(0.05, shape=[num_outputs]))\n",
    "        \n",
    "        # Multiply the input and weights, and then add the bias-values.\n",
    "        layer = tf.matmul(input, weights) + biases\n",
    "        \n",
    "        return layer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convolutional Layer 1\n",
    "layer_conv1, weights_conv1 = new_conv_layer(input=x_image, num_input_channels=1, filter_size=5, num_filters=6, \n",
    "                                            name =\"conv1\")\n",
    "\n",
    "# Pooling Layer 1\n",
    "layer_pool1 = new_pool_layer(layer_conv1, name=\"pool1\")\n",
    "\n",
    "# RelU layer 1\n",
    "layer_relu1 = new_relu_layer(layer_pool1, name=\"relu1\")\n",
    "\n",
    "# Convolutional Layer 2\n",
    "layer_conv2, weights_conv2 = new_conv_layer(input=layer_relu1, num_input_channels=6, filter_size=5, num_filters=16, \n",
    "                                            name= \"conv2\")\n",
    "\n",
    "# Pooling Layer 2\n",
    "layer_pool2 = new_pool_layer(layer_conv2, name=\"pool2\")\n",
    "\n",
    "# RelU layer 2\n",
    "layer_relu2 = new_relu_layer(layer_pool2, name=\"relu2\")\n",
    "\n",
    "# Flatten Layer\n",
    "num_features = layer_relu2.get_shape()[1:4].num_elements()\n",
    "layer_flat = tf.reshape(layer_relu2, [-1, num_features])\n",
    "\n",
    "# Fully-Connected Layer 1\n",
    "layer_fc1 = new_fc_layer(layer_flat, num_inputs=num_features, num_outputs=128, name=\"fc1\")\n",
    "\n",
    "# RelU layer 3\n",
    "layer_relu3 = new_relu_layer(layer_fc1, name=\"relu3\")\n",
    "\n",
    "# Fully-Connected Layer 2\n",
    "layer_fc2 = new_fc_layer(input=layer_relu3, num_inputs=128, num_outputs=10, name=\"fc2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'conv1/add:0' shape=(?, 28, 28, 6) dtype=float32>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_conv1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use Softmax function to normalize the output\n",
    "with tf.variable_scope(\"Softmax\"):\n",
    "    y_pred = tf.nn.softmax(layer_fc2)\n",
    "    y_pred_cls = tf.argmax(y_pred, dimension=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use Cross entropy cost function\n",
    "with tf.name_scope(\"cross_ent\"):\n",
    "    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2, labels=y_true)\n",
    "    cost = tf.reduce_mean(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use Adam Optimizer\n",
    "with tf.name_scope(\"optimizer\"):\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy\n",
    "with tf.name_scope(\"accuracy\"):\n",
    "    correct_prediction = tf.equal(y_pred_cls, y_true_cls)\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the FileWriter\n",
    "writer = tf.summary.FileWriter(\"Training_FileWriter/\")\n",
    "writer1 = tf.summary.FileWriter(\"Validation_FileWriter/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the cost and accuracy to summary\n",
    "tf.summary.scalar('loss', cost)\n",
    "tf.summary.scalar('accuracy', accuracy)\n",
    "\n",
    "# Merge all summaries together\n",
    "merged_summary = tf.summary.merge_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 2\n",
    "batch_size = 100\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 completed : Time usage 30 seconds\n",
      "\tAccuracy:\n",
      "\t- Training Accuracy:\t0.7236909074302424\n",
      "\t- Validation Accuracy:\t0.8866000175476074\n",
      "Epoch 2 completed : Time usage 29 seconds\n",
      "\tAccuracy:\n",
      "\t- Training Accuracy:\t0.9026727293838154\n",
      "\t- Validation Accuracy:\t0.91839998960495\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    # Initialize all variables\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    # Add the model graph to TensorBoard\n",
    "    writer.add_graph(sess.graph)\n",
    "    \n",
    "    # Loop over number of epochs\n",
    "    for epoch in range(num_epochs):\n",
    "        \n",
    "        start_time = time.time()\n",
    "        train_accuracy = 0\n",
    "        \n",
    "        for batch in range(0, int(len(data.train.labels)/batch_size)):\n",
    "            \n",
    "            # Get a batch of images and labels\n",
    "            x_batch, y_true_batch = data.train.next_batch(batch_size)\n",
    "            \n",
    "            # Put the batch into a dict with the proper names for placeholder variables\n",
    "            feed_dict_train = {x: x_batch, y_true: y_true_batch}\n",
    "            \n",
    "            # Run the optimizer using this batch of training data.\n",
    "            sess.run(optimizer, feed_dict=feed_dict_train)\n",
    "            \n",
    "            # Calculate the accuracy on the batch of training data\n",
    "            train_accuracy += sess.run(accuracy, feed_dict=feed_dict_train)\n",
    "            \n",
    "            # Generate summary with the current batch of data and write to file\n",
    "            summ = sess.run(merged_summary, feed_dict=feed_dict_train)\n",
    "            writer.add_summary(summ, epoch*int(len(data.train.labels)/batch_size) + batch)\n",
    "        \n",
    "          \n",
    "        train_accuracy /= int(len(data.train.labels)/batch_size)\n",
    "        \n",
    "        # Generate summary and validate the model on the entire validation set\n",
    "        summ, vali_accuracy = sess.run([merged_summary, accuracy], feed_dict={x:data.validation.images, y_true:data.validation.labels})\n",
    "        writer1.add_summary(summ, epoch)\n",
    "        \n",
    "\n",
    "        end_time = time.time()\n",
    "        \n",
    "        print(\"Epoch \"+str(epoch+1)+\" completed : Time usage \"+str(int(end_time-start_time))+\" seconds\")\n",
    "        print(\"\\tAccuracy:\")\n",
    "        print (\"\\t- Training Accuracy:\\t{}\".format(train_accuracy))\n",
    "        print (\"\\t- Validation Accuracy:\\t{}\".format(vali_accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python optimisation variables\n",
    "learning_rate = 0.01\n",
    "epochs = 5\n",
    "batch_size = 50\n",
    "\n",
    "# declare the training data placeholders\n",
    "# input x - for 28 x 28 pixels = 784 - this is the flattened image data that is drawn from \n",
    "# mnist.train.nextbatch()\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "# dynamically reshape the input\n",
    "x_shaped = tf.reshape(x, [-1, 28, 28, 1])\n",
    "# now declare the output data placeholder - 10 digits\n",
    "y = tf.placeholder(tf.float32, [None, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Reshape_4:0' shape=(?, 28, 28, 1) dtype=float32>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_shaped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_new_conv_layer(input_data, num_input_channels, num_filters, filter_shape, pool_shape, name):\n",
    "    # setup the filter input shape for tf.nn.conv_2d\n",
    "    conv_filt_shape = [filter_shape[0], filter_shape[1], num_input_channels,\n",
    "                      num_filters]\n",
    "    \n",
    "    print (\"conv filer:\", len(conv_filt_shape))\n",
    "\n",
    "    # initialise weights and bias for the filter\n",
    "    weights = tf.Variable(tf.truncated_normal(conv_filt_shape, stddev=0.03),\n",
    "                                      name=name+'_W')\n",
    "    print(\"weights\", weights.shape)\n",
    "    bias = tf.Variable(tf.truncated_normal([num_filters]), name=name+'_b')\n",
    "\n",
    "    print(\"bias\", bias.shape)\n",
    "    # setup the convolutional layer operation\n",
    "    out_layer = tf.nn.conv2d(input_data, weights, [1, 1, 1, 1], padding='SAME')\n",
    "    print (\"output layer\", out_layer.shape )\n",
    "\n",
    "    # add the bias\n",
    "    out_layer += bias\n",
    "    print(\"output_layer\", out_layer.shape)\n",
    "\n",
    "    # apply a ReLU non-linear activation\n",
    "    out_layer = tf.nn.relu(out_layer)\n",
    "    print(\"output_layer after ReLU\", out_layer.shape)\n",
    "\n",
    "\n",
    "    # now perform max pooling\n",
    "    ksize = [1, pool_shape[0], pool_shape[1], 1]\n",
    "    strides = [1, 2, 2, 1]\n",
    "    out_layer = tf.nn.max_pool(out_layer, ksize=ksize, strides=strides, \n",
    "                               padding='SAME')\n",
    "    \n",
    "    print(\"output_layer after Max pooling\", out_layer.shape)\n",
    "    print(\"-----------\")\n",
    "\n",
    "    return out_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv filer: 4\n",
      "weights (5, 5, 1, 32)\n",
      "bias (32,)\n",
      "output layer (?, 28, 28, 32)\n",
      "output_layer (?, 28, 28, 32)\n",
      "output_layer after ReLU (?, 28, 28, 32)\n",
      "output_layer after Max pooling (?, 14, 14, 32)\n",
      "-----------\n",
      "conv filer: 4\n",
      "weights (5, 5, 32, 64)\n",
      "bias (64,)\n",
      "output layer (?, 14, 14, 64)\n",
      "output_layer (?, 14, 14, 64)\n",
      "output_layer after ReLU (?, 14, 14, 64)\n",
      "output_layer after Max pooling (?, 7, 7, 64)\n",
      "-----------\n"
     ]
    }
   ],
   "source": [
    "layer1 = create_new_conv_layer(x_shaped, 1, 32, [5, 5], [2, 2], name='layer1')\n",
    "layer2 = create_new_conv_layer(layer1, 32, 64, [5, 5], [2, 2], name='layer2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'MaxPool_22:0' shape=(?, 14, 14, 32) dtype=float32>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'MaxPool_23:0' shape=(?, 7, 7, 64) dtype=float32>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "flattened = tf.reshape(layer2, [-1, 7 * 7 * 64])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Reshape_6:0' shape=(?, 3136) dtype=float32>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flattened"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup some weights and bias values for this layer, then activate with ReLU\n",
    "wd1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1000], stddev=0.03), name='wd1')\n",
    "bd1 = tf.Variable(tf.truncated_normal([1000], stddev=0.01), name='bd1')\n",
    "dense_layer1 = tf.matmul(flattened, wd1) + bd1\n",
    "dense_layer1 = tf.nn.relu(dense_layer1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([Dimension(3136), Dimension(1000)])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wd1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([Dimension(1000)])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bd1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([Dimension(None), Dimension(1000)])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_layer1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# another layer with softmax activations\n",
    "wd2 = tf.Variable(tf.truncated_normal([1000, 10], stddev=0.03), name='wd2')\n",
    "bd2 = tf.Variable(tf.truncated_normal([10], stddev=0.01), name='bd2')\n",
    "dense_layer2 = tf.matmul(dense_layer1, wd2) + bd2\n",
    "y_ = tf.nn.softmax(dense_layer2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([Dimension(1000), Dimension(10)])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wd2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([Dimension(10)])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bd2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([Dimension(None), Dimension(10)])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense_layer2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([Dimension(None), Dimension(10)])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=dense_layer2, labels=y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Mean_2:0' shape=() dtype=float32>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: \"Adam_4\"\n",
      "op: \"NoOp\"\n",
      "input: \"^Adam_4/update_layer1_W_10/ApplyAdam\"\n",
      "input: \"^Adam_4/update_layer1_b_10/ApplyAdam\"\n",
      "input: \"^Adam_4/update_layer2_W_10/ApplyAdam\"\n",
      "input: \"^Adam_4/update_layer2_b_10/ApplyAdam\"\n",
      "input: \"^Adam_4/update_wd1/ApplyAdam\"\n",
      "input: \"^Adam_4/update_bd1/ApplyAdam\"\n",
      "input: \"^Adam_4/update_wd2/ApplyAdam\"\n",
      "input: \"^Adam_4/update_bd2/ApplyAdam\"\n",
      "input: \"^Adam_4/Assign\"\n",
      "input: \"^Adam_4/Assign_1\"\n",
      "\n",
      "Tensor(\"Equal_4:0\", shape=(?,), dtype=bool)\n",
      "Tensor(\"Mean_6:0\", shape=(), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# add an optimiser\n",
    "optimiser = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cross_entropy)\n",
    "print(optimiser)\n",
    "\n",
    "# define an accuracy assessment operation\n",
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "print(correct_prediction)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "print(accuracy)\n",
    "\n",
    "# setup the initialisation operator\n",
    "init_op = tf.global_variables_initializer()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_batch 1100\n",
      "epoch 0\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.5357256\n",
      "avg_cost 0.0023052050850608133\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 108.272\n",
      "avg_cost 0.10073429887945001\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 21.486034\n",
      "avg_cost 0.12026705741882324\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.628526\n",
      "avg_cost 0.12265662648461081\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3209367\n",
      "avg_cost 0.12476656892082907\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986202\n",
      "avg_cost 0.12685622366991908\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2926693\n",
      "avg_cost 0.12894046848470514\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2959678\n",
      "avg_cost 0.1310277119549838\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3099139\n",
      "avg_cost 0.13312763365832242\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3119698\n",
      "avg_cost 0.13522942434657703\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3188877\n",
      "avg_cost 0.13733750408346002\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035688\n",
      "avg_cost 0.13943165757439352\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3742752\n",
      "avg_cost 0.14159008958122946\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2930863\n",
      "avg_cost 0.14367471348155628\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125777\n",
      "avg_cost 0.1457770568674261\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060553\n",
      "avg_cost 0.14787347078323365\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3178976\n",
      "avg_cost 0.14998065038160846\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3329744\n",
      "avg_cost 0.1521015362306075\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2933612\n",
      "avg_cost 0.15418641003695405\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005025\n",
      "avg_cost 0.15627777598120954\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3198693\n",
      "avg_cost 0.15838674805381087\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077068\n",
      "avg_cost 0.16048466335643427\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2881193\n",
      "avg_cost 0.16256477182561707\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.284188\n",
      "avg_cost 0.16464130640029914\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3110936\n",
      "avg_cost 0.16674230055375538\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954073\n",
      "avg_cost 0.16882903445850725\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 4.0647573\n",
      "avg_cost 0.17252426841042265\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.351689\n",
      "avg_cost 0.1746621675924822\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076754\n",
      "avg_cost 0.17676005428487612\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078556\n",
      "avg_cost 0.17885810483585712\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285985\n",
      "avg_cost 0.18093627301129436\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118775\n",
      "avg_cost 0.18303797981955797\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047907\n",
      "avg_cost 0.1851332441243259\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31991\n",
      "avg_cost 0.18724225326017907\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3105361\n",
      "avg_cost 0.18934274066578266\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300466\n",
      "avg_cost 0.19143407344818122\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069296\n",
      "avg_cost 0.19353128216483384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313896\n",
      "avg_cost 0.19563482392917989\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291329\n",
      "avg_cost 0.19771785020828256\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049498\n",
      "avg_cost 0.19981325908140712\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296176\n",
      "avg_cost 0.20190069176933992\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314945\n",
      "avg_cost 0.20400518720800237\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995358\n",
      "avg_cost 0.20609567425467762\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295112\n",
      "avg_cost 0.20818213961341175\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126974\n",
      "avg_cost 0.2102845918048513\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008268\n",
      "avg_cost 0.2123762525211682\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304138\n",
      "avg_cost 0.21447092338041837\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2863624\n",
      "avg_cost 0.21654943466186535\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2850122\n",
      "avg_cost 0.21862671852111829\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117464\n",
      "avg_cost 0.22072830612009234\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117292\n",
      "avg_cost 0.22282987811348667\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.271932\n",
      "avg_cost 0.22489527073773483\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004458\n",
      "avg_cost 0.22698658509687955\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3097086\n",
      "avg_cost 0.22908632018349398\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108087\n",
      "avg_cost 0.23118705532767567\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135517\n",
      "avg_cost 0.2332902841134506\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983313\n",
      "avg_cost 0.23537967616861527\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314454\n",
      "avg_cost 0.23748372533104647\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305739\n",
      "avg_cost 0.23957985162734996\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074973\n",
      "avg_cost 0.24167757641185425\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3170767\n",
      "avg_cost 0.24378400976007647\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030605\n",
      "avg_cost 0.24587770115245483\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043563\n",
      "avg_cost 0.24797257054935812\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088348\n",
      "avg_cost 0.2500715112686158\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2878375\n",
      "avg_cost 0.25215136354619816\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069558\n",
      "avg_cost 0.2542485961047087\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3213568\n",
      "avg_cost 0.2563589204441418\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2885995\n",
      "avg_cost 0.25843946543606855\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2891815\n",
      "avg_cost 0.2605205395004967\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2826984\n",
      "avg_cost 0.26259571985764946\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3059313\n",
      "avg_cost 0.26469202106649237\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300538\n",
      "avg_cost 0.2667834193056281\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962072\n",
      "avg_cost 0.2688708803870462\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035674\n",
      "avg_cost 0.27096503257751475\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.322875\n",
      "avg_cost 0.2730767371437767\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2852416\n",
      "avg_cost 0.2751542295109143\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934263\n",
      "avg_cost 0.2772391624884173\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075018\n",
      "avg_cost 0.27933689139106066\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963116\n",
      "avg_cost 0.2814244474064221\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3332498\n",
      "avg_cost 0.28354558359492915\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970397\n",
      "avg_cost 0.28563380154696383\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3187048\n",
      "avg_cost 0.28774171504107393\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2924516\n",
      "avg_cost 0.28982576196843934\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040373\n",
      "avg_cost 0.2919203413616528\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3068733\n",
      "avg_cost 0.294017498926683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946198\n",
      "avg_cost 0.29610351692546505\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040545\n",
      "avg_cost 0.2981981119242582\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306288\n",
      "avg_cost 0.30029473738236867\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300729\n",
      "avg_cost 0.30238630923357884\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3257234\n",
      "avg_cost 0.304500603242354\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108053\n",
      "avg_cost 0.3066013353521174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2850013\n",
      "avg_cost 0.3086786092411389\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041701\n",
      "avg_cost 0.310773309360851\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2924006\n",
      "avg_cost 0.3128573099049656\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308232\n",
      "avg_cost 0.3149557026949797\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062632\n",
      "avg_cost 0.3170523056116972\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010972\n",
      "avg_cost 0.3191442121158948\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3102827\n",
      "avg_cost 0.3212444691224533\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2837589\n",
      "avg_cost 0.3233206135576423\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123147\n",
      "avg_cost 0.3254227178747005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311836\n",
      "avg_cost 0.3275243869694798\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012033\n",
      "avg_cost 0.3296163899248298\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051972\n",
      "avg_cost 0.3317120237783953\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986708\n",
      "avg_cost 0.3338017244772479\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087385\n",
      "avg_cost 0.3359005776318638\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292517\n",
      "avg_cost 0.3379846839471298\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.329031\n",
      "avg_cost 0.3401019848476758\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307861\n",
      "avg_cost 0.34220004038377255\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060958\n",
      "avg_cost 0.3442964911460878\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004198\n",
      "avg_cost 0.34638778188011876\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3131635\n",
      "avg_cost 0.3484906578063966\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294785\n",
      "avg_cost 0.3505768260088835\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013418\n",
      "avg_cost 0.35266895489259215\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035169\n",
      "avg_cost 0.35476306113329814\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095338\n",
      "avg_cost 0.35686263734644125\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3153286\n",
      "avg_cost 0.3589674815264617\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034701\n",
      "avg_cost 0.36106154528531176\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954066\n",
      "avg_cost 0.3631482785398311\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3124247\n",
      "avg_cost 0.3652504827759484\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039343\n",
      "avg_cost 0.3673449685356835\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075073\n",
      "avg_cost 0.3694427024234426\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3068051\n",
      "avg_cost 0.37153979799964226\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025281\n",
      "avg_cost 0.37363300540230504\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305616\n",
      "avg_cost 0.3757290198586205\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304371\n",
      "avg_cost 0.3778239026936619\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306772\n",
      "avg_cost 0.3799209681424229\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305829\n",
      "avg_cost 0.3820171763680199\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297539\n",
      "avg_cost 0.38410584818233157\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071427\n",
      "avg_cost 0.3862032506682657\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024523\n",
      "avg_cost 0.3882963891462847\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067431\n",
      "avg_cost 0.39039342836900204\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3000238\n",
      "avg_cost 0.39248435909097856\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041341\n",
      "avg_cost 0.3945790264823221\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026347\n",
      "avg_cost 0.3966723307696257\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046157\n",
      "avg_cost 0.3987674359841782\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047695\n",
      "avg_cost 0.40086268099871564\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3193712\n",
      "avg_cost 0.40297120029276084\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001966\n",
      "avg_cost 0.40506228815425543\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3161907\n",
      "avg_cost 0.4071679160811686\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039713\n",
      "avg_cost 0.40926243543624896\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020911\n",
      "avg_cost 0.4113552455468613\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3120255\n",
      "avg_cost 0.41345708695325\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020136\n",
      "avg_cost 0.41554982662200945\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041015\n",
      "avg_cost 0.4176444643194027\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010778\n",
      "avg_cost 0.41973635326732306\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007488\n",
      "avg_cost 0.42182794310829874\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303893\n",
      "avg_cost 0.42392239137129367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302067\n",
      "avg_cost 0.4260151795907456\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983718\n",
      "avg_cost 0.42810460849241794\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299077\n",
      "avg_cost 0.43019467852332394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990065\n",
      "avg_cost 0.43228468439795775\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980676\n",
      "avg_cost 0.43437383673407837\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303258\n",
      "avg_cost 0.43646770759062353\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301597\n",
      "avg_cost 0.43856006860733054\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982545\n",
      "avg_cost 0.4406493908708748\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3139324\n",
      "avg_cost 0.44275296579707774\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095505\n",
      "avg_cost 0.4448525571823122\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3104215\n",
      "avg_cost 0.4469529403339735\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291719\n",
      "avg_cost 0.44903632120652653\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303842\n",
      "avg_cost 0.4511307230862706\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034058\n",
      "avg_cost 0.45322472832419675\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042436\n",
      "avg_cost 0.45531949520111104\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077805\n",
      "avg_cost 0.45741747747768075\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2916393\n",
      "avg_cost 0.4595007859576834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920089\n",
      "avg_cost 0.46158443039113844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991576\n",
      "avg_cost 0.46367457368157144\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983074\n",
      "avg_cost 0.46576394406231986\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2944734\n",
      "avg_cost 0.46784982898018596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3121026\n",
      "avg_cost 0.46995174039493925\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3112144\n",
      "avg_cost 0.4720528444376861\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060007\n",
      "avg_cost 0.47414920871908034\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3013043\n",
      "avg_cost 0.4762413035739554\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3157306\n",
      "avg_cost 0.47834651318463434\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2931767\n",
      "avg_cost 0.48043121923099885\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087046\n",
      "avg_cost 0.48253004160794366\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2937503\n",
      "avg_cost 0.4846152691407639\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2872634\n",
      "avg_cost 0.4866945994984022\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30699\n",
      "avg_cost 0.488791863051328\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920654\n",
      "avg_cost 0.4908755588531496\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907724\n",
      "avg_cost 0.49295807925137625\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025928\n",
      "avg_cost 0.49505134539170714\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3111734\n",
      "avg_cost 0.49715241215445793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092594\n",
      "avg_cost 0.49925173889506963\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2967634\n",
      "avg_cost 0.5013397056406196\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981925\n",
      "avg_cost 0.5034289715506816\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3106272\n",
      "avg_cost 0.5055295417525554\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3122954\n",
      "avg_cost 0.5076316285133364\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013346\n",
      "avg_cost 0.5097237508947201\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3122272\n",
      "avg_cost 0.5118257756666705\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977154\n",
      "avg_cost 0.5139146078716627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287352\n",
      "avg_cost 0.5159940188581295\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3149743\n",
      "avg_cost 0.5180985409563239\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087647\n",
      "avg_cost 0.5201974179527977\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3061996\n",
      "avg_cost 0.5222939629988238\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306994\n",
      "avg_cost 0.5243912302364003\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977874\n",
      "avg_cost 0.5264801278981296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3121657\n",
      "avg_cost 0.528582096750086\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3048263\n",
      "avg_cost 0.5306773933497343\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952523\n",
      "avg_cost 0.5327639863707804\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004181\n",
      "avg_cost 0.5348552755876023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007035\n",
      "avg_cost 0.536946824247187\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307165\n",
      "avg_cost 0.5390442468903283\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.28274\n",
      "avg_cost 0.5411194651777095\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966223\n",
      "avg_cost 0.5432073036107151\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2942178\n",
      "avg_cost 0.5452929561788387\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036544\n",
      "avg_cost 0.5473871874809266\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2933614\n",
      "avg_cost 0.5494720615040173\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3000765\n",
      "avg_cost 0.5515630401264539\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2842052\n",
      "avg_cost 0.5536395903067156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2918115\n",
      "avg_cost 0.5557230552760039\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017673\n",
      "avg_cost 0.5578155710480431\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039162\n",
      "avg_cost 0.5599100403352218\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3136628\n",
      "avg_cost 0.5620133701237766\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309219\n",
      "avg_cost 0.5641126600178806\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082376\n",
      "avg_cost 0.5662110577930105\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312624\n",
      "avg_cost 0.5683134432272479\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2805836\n",
      "avg_cost 0.5703867010636764\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3142438\n",
      "avg_cost 0.572490559057756\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307538\n",
      "avg_cost 0.5745883209055122\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3303432\n",
      "avg_cost 0.5767068147659303\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954917\n",
      "avg_cost 0.5787936253981159\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2944508\n",
      "avg_cost 0.5808794897252865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2845855\n",
      "avg_cost 0.582956385612488\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158982\n",
      "avg_cost 0.5850617475943134\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083394\n",
      "avg_cost 0.5871602379192008\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021832\n",
      "avg_cost 0.58925313169306\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103712\n",
      "avg_cost 0.591353469111703\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312139\n",
      "avg_cost 0.5934554136883131\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045497\n",
      "avg_cost 0.5955504588647325\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289092\n",
      "avg_cost 0.5976314516500996\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010125\n",
      "avg_cost 0.5997232812101193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2921572\n",
      "avg_cost 0.6018070604584436\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3068874\n",
      "avg_cost 0.6039042308113793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309611\n",
      "avg_cost 0.6060038772496312\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037326\n",
      "avg_cost 0.6080981796438046\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2933717\n",
      "avg_cost 0.6101830629868944\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055925\n",
      "avg_cost 0.612279056202282\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920504\n",
      "avg_cost 0.6143627383492213\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996063\n",
      "avg_cost 0.6164532895521687\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3178213\n",
      "avg_cost 0.6185603997924114\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031235\n",
      "avg_cost 0.6206541484052488\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303865\n",
      "avg_cost 0.6227485710924325\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294035\n",
      "avg_cost 0.6248340574177832\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2940416\n",
      "avg_cost 0.6269195498119704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2967958\n",
      "avg_cost 0.6290075460347265\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3131125\n",
      "avg_cost 0.6311103755777535\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3059304\n",
      "avg_cost 0.6332066759196198\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030708\n",
      "avg_cost 0.6353003766319971\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302124\n",
      "avg_cost 0.637393216653304\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304319\n",
      "avg_cost 0.6394880520213737\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29367\n",
      "avg_cost 0.6415732065114111\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2829812\n",
      "avg_cost 0.643648643927141\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123813\n",
      "avg_cost 0.6457508087158207\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299404\n",
      "avg_cost 0.6478411759029739\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2959588\n",
      "avg_cost 0.6499284111369743\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078332\n",
      "avg_cost 0.652026441314004\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023102\n",
      "avg_cost 0.654119450612502\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2855768\n",
      "avg_cost 0.6561972477219324\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3081973\n",
      "avg_cost 0.6582956088672988\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313059\n",
      "avg_cost 0.6603983898596333\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298134\n",
      "avg_cost 0.6624876026673753\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2947161\n",
      "avg_cost 0.6645737082307991\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022006\n",
      "avg_cost 0.6666666178269822\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2749186\n",
      "avg_cost 0.6687347256053581\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002083\n",
      "avg_cost 0.6708258240873166\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089597\n",
      "avg_cost 0.6729248783805156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031838\n",
      "avg_cost 0.6750186818296262\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3028195\n",
      "avg_cost 0.677112154093656\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082676\n",
      "avg_cost 0.6792105791785503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311374\n",
      "avg_cost 0.6813118282231421\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007112\n",
      "avg_cost 0.68340338381854\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968817\n",
      "avg_cost 0.6854914580691948\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079684\n",
      "avg_cost 0.6875896111401648\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3143888\n",
      "avg_cost 0.6896936009146954\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135319\n",
      "avg_cost 0.6917968117107048\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072011\n",
      "avg_cost 0.6938942672989589\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051355\n",
      "avg_cost 0.6959898450157863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2948296\n",
      "avg_cost 0.6980760537494317\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2815151\n",
      "avg_cost 0.7001501584053044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983265\n",
      "avg_cost 0.7022395461255858\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3113875\n",
      "avg_cost 0.7043408075245948\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2955573\n",
      "avg_cost 0.7064276777614251\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979462\n",
      "avg_cost 0.7085167197747668\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098183\n",
      "avg_cost 0.7106165545636961\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3068867\n",
      "avg_cost 0.7127137242663995\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041968\n",
      "avg_cost 0.7148084486614579\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313022\n",
      "avg_cost 0.716911195841703\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306885\n",
      "avg_cost 0.7190083640271973\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981822\n",
      "avg_cost 0.7210976206172601\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2834513\n",
      "avg_cost 0.7231734854524791\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015442\n",
      "avg_cost 0.7252657983519819\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298611\n",
      "avg_cost 0.7273554446480496\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044302\n",
      "avg_cost 0.7294503812356433\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310625\n",
      "avg_cost 0.7315509494868196\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907593\n",
      "avg_cost 0.7336334579641174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292791\n",
      "avg_cost 0.7357178133184265\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001475\n",
      "avg_cost 0.7378088565306234\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990081\n",
      "avg_cost 0.7398988639224664\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2930584\n",
      "avg_cost 0.7419834624637262\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920012\n",
      "avg_cost 0.744067099961368\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041544\n",
      "avg_cost 0.7461617857759654\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298326\n",
      "avg_cost 0.7482511730627585\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299065\n",
      "avg_cost 0.7503412322564563\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3155909\n",
      "avg_cost 0.7524463148550559\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997918\n",
      "avg_cost 0.754537034684962\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2940474\n",
      "avg_cost 0.7566225322810092\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2914095\n",
      "avg_cost 0.7587056318196389\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2759595\n",
      "avg_cost 0.7607746859030295\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3132327\n",
      "avg_cost 0.7628776246851147\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.281516\n",
      "avg_cost 0.764951730207964\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2835903\n",
      "avg_cost 0.76702772140503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887418\n",
      "avg_cost 0.7691083957932219\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.279397\n",
      "avg_cost 0.7711805748939521\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303861\n",
      "avg_cost 0.773274993896485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2945898\n",
      "avg_cost 0.7753609845855026\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307501\n",
      "avg_cost 0.7774587128379135\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2795413\n",
      "avg_cost 0.7795310230688621\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991946\n",
      "avg_cost 0.7816211999546404\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306672\n",
      "avg_cost 0.7837181745875972\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298035\n",
      "avg_cost 0.7858072972297675\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964826\n",
      "avg_cost 0.7878950086506937\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3369389\n",
      "avg_cost 0.7900194985216321\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292037\n",
      "avg_cost 0.7921031685308983\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2747936\n",
      "avg_cost 0.7941711627353328\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3229206\n",
      "avg_cost 0.79628290869973\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311396\n",
      "avg_cost 0.7983841776847846\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082333\n",
      "avg_cost 0.8004825715585195\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956493\n",
      "avg_cost 0.8025695254585967\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.281382\n",
      "avg_cost 0.8046435091712265\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3273466\n",
      "avg_cost 0.8067592787742621\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2719333\n",
      "avg_cost 0.8088246726989753\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013773\n",
      "avg_cost 0.8109168338775642\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923925\n",
      "avg_cost 0.8130008270523772\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2876928\n",
      "avg_cost 0.8150805477662527\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004503\n",
      "avg_cost 0.8171718662435365\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306229\n",
      "avg_cost 0.8192684381658388\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031538\n",
      "avg_cost 0.8213622143051849\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310888\n",
      "avg_cost 0.8234630216251727\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2820222\n",
      "avg_cost 0.8255375872958797\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025372\n",
      "avg_cost 0.8276308029348206\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3695803\n",
      "avg_cost 0.8297849668156023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009648\n",
      "avg_cost 0.8318767530267895\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009768\n",
      "avg_cost 0.8339685500751849\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30839\n",
      "avg_cost 0.8360670863498347\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964463\n",
      "avg_cost 0.8381547648256482\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3111942\n",
      "avg_cost 0.8402558504451412\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3159869\n",
      "avg_cost 0.8423612930557951\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3358572\n",
      "avg_cost 0.8444847995584668\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3192747\n",
      "avg_cost 0.846593231071126\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042612\n",
      "avg_cost 0.8486880139871084\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992759\n",
      "avg_cost 0.8507782647826462\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3140602\n",
      "avg_cost 0.8528819558837204\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050368\n",
      "avg_cost 0.8549774438684644\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3113976\n",
      "avg_cost 0.8570787143707282\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2881107\n",
      "avg_cost 0.8591588150371212\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293834\n",
      "avg_cost 0.8612441186471427\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.32157\n",
      "avg_cost 0.8633546367558574\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3129523\n",
      "avg_cost 0.865457320646807\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3162298\n",
      "avg_cost 0.867562984119763\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026214\n",
      "avg_cost 0.8696562762693935\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2948186\n",
      "avg_cost 0.8717424750328073\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.277291\n",
      "avg_cost 0.8738127396323474\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997112\n",
      "avg_cost 0.8759033862027265\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2866263\n",
      "avg_cost 0.8779821374199617\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956543\n",
      "avg_cost 0.8800690958716663\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307071\n",
      "avg_cost 0.8821664331176075\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306624\n",
      "avg_cost 0.8842633639682433\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313067\n",
      "avg_cost 0.8863661521131353\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3150806\n",
      "avg_cost 0.8884707708792264\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957118\n",
      "avg_cost 0.8905577815662742\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.277247\n",
      "avg_cost 0.8926280060681441\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2841294\n",
      "avg_cost 0.8947044873237621\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3199563\n",
      "avg_cost 0.8968135385079828\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994504\n",
      "avg_cost 0.8989039479602478\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3175933\n",
      "avg_cost 0.9010108509930708\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291199\n",
      "avg_cost 0.9030937591466047\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3000932\n",
      "avg_cost 0.9051847529411327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3070164\n",
      "avg_cost 0.9072820405526605\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30541\n",
      "avg_cost 0.9093778677420192\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085258\n",
      "avg_cost 0.911476527560842\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045373\n",
      "avg_cost 0.9135715614665648\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950644\n",
      "avg_cost 0.9156579836932105\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035676\n",
      "avg_cost 0.9177521361004232\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089144\n",
      "avg_cost 0.9198511492122312\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2856894\n",
      "avg_cost 0.9219290486249065\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3353844\n",
      "avg_cost 0.9240521253239034\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954364\n",
      "avg_cost 0.9261388856714431\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2842174\n",
      "avg_cost 0.9282154469056572\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3264391\n",
      "avg_cost 0.9303303915804091\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017778\n",
      "avg_cost 0.9324229168891915\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3124118\n",
      "avg_cost 0.934525109421124\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2730417\n",
      "avg_cost 0.93659151098945\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995243\n",
      "avg_cost 0.9386819876324055\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287128\n",
      "avg_cost 0.9407611948793593\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098965\n",
      "avg_cost 0.942861100760374\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951162\n",
      "avg_cost 0.944947570020503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2809052\n",
      "avg_cost 0.9470211202448072\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303731\n",
      "avg_cost 0.9491154211217715\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2807748\n",
      "avg_cost 0.9511888527870186\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3129146\n",
      "avg_cost 0.9532915024323905\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117685\n",
      "avg_cost 0.9553931101885716\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125827\n",
      "avg_cost 0.9574954581260688\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954798\n",
      "avg_cost 0.9595822579210462\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023818\n",
      "avg_cost 0.9616753322427931\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3203547\n",
      "avg_cost 0.96378474560651\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307556\n",
      "avg_cost 0.9658825237100783\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3027112\n",
      "avg_cost 0.9679758975722582\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3143966\n",
      "avg_cost 0.9700798944993462\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2915008\n",
      "avg_cost 0.9721630770509903\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.28514\n",
      "avg_cost 0.9742404770851144\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302785\n",
      "avg_cost 0.9763339179212406\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3184223\n",
      "avg_cost 0.9784415745735178\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141072\n",
      "avg_cost 0.9805453083731921\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2741063\n",
      "avg_cost 0.9826126777042051\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2922287\n",
      "avg_cost 0.9846965219757783\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3330245\n",
      "avg_cost 0.9868174533410515\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985308\n",
      "avg_cost 0.9889070268110804\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971253\n",
      "avg_cost 0.9909953225742696\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3100402\n",
      "avg_cost 0.9930953591520145\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2850356\n",
      "avg_cost 0.9951726642521954\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991667\n",
      "avg_cost 0.9972628157789066\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2767668\n",
      "avg_cost 0.9993326037580326\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019915\n",
      "avg_cost 1.0014253232695849\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3027902\n",
      "avg_cost 1.0035187688740825\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961144\n",
      "avg_cost 1.0056061456420213\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017306\n",
      "avg_cost 1.0076986280354594\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019493\n",
      "avg_cost 1.0097913091832948\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002534\n",
      "avg_cost 1.0118824486299003\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2855127\n",
      "avg_cost 1.013960187435151\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2867308\n",
      "avg_cost 1.0160390335863294\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.320591\n",
      "avg_cost 1.0181486617435116\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303765\n",
      "avg_cost 1.0202429936148911\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3039298\n",
      "avg_cost 1.022337475256487\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039465\n",
      "avg_cost 1.0244319720701744\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312066\n",
      "avg_cost 1.0265338503230708\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305444\n",
      "avg_cost 1.0286297085068448\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2875063\n",
      "avg_cost 1.0307092597267848\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046548\n",
      "avg_cost 1.03280440048738\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2978802\n",
      "avg_cost 1.0348933824625886\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2905831\n",
      "avg_cost 1.03697573076595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.281501\n",
      "avg_cost 1.039049822633917\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988098\n",
      "avg_cost 1.0411396496946166\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014262\n",
      "avg_cost 1.043231855305759\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071775\n",
      "avg_cost 1.0453292894363408\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2815118\n",
      "avg_cost 1.0474033910577953\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023834\n",
      "avg_cost 1.0494964668967512\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295886\n",
      "avg_cost 1.051583636023782\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3172736\n",
      "avg_cost 1.0536902484026827\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3268049\n",
      "avg_cost 1.0558055255629804\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035357\n",
      "avg_cost 1.0578996489264751\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309067\n",
      "avg_cost 1.0599988007545473\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.280369\n",
      "avg_cost 1.0620718635212294\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084953\n",
      "avg_cost 1.0641704955967992\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306575\n",
      "avg_cost 1.0662673820148818\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2875652\n",
      "avg_cost 1.0683469867706301\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025925\n",
      "avg_cost 1.070440252694217\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2763739\n",
      "avg_cost 1.0725096834789627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3335993\n",
      "avg_cost 1.0746311374144124\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008866\n",
      "avg_cost 1.0767228525335142\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2813828\n",
      "avg_cost 1.0787968368963765\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310096\n",
      "avg_cost 1.0808969241922555\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2860782\n",
      "avg_cost 1.0829751771146603\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3096688\n",
      "avg_cost 1.0850748760049995\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075154\n",
      "avg_cost 1.0871726172620602\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2786398\n",
      "avg_cost 1.0892441079833293\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083541\n",
      "avg_cost 1.0913426117463547\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302382\n",
      "avg_cost 1.0934356862848458\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115003\n",
      "avg_cost 1.0955370502038437\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064818\n",
      "avg_cost 1.0976338518749587\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3142385\n",
      "avg_cost 1.0997377051006667\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310871\n",
      "avg_cost 1.101838496815075\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3137503\n",
      "avg_cost 1.1039419061487374\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2937422\n",
      "avg_cost 1.106027126312256\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012893\n",
      "avg_cost 1.1081192075122488\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014956\n",
      "avg_cost 1.1102114761959425\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296139\n",
      "avg_cost 1.11229887528853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.276021\n",
      "avg_cost 1.1143679852919148\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3285975\n",
      "avg_cost 1.1164848921515729\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2868552\n",
      "avg_cost 1.1185638514432044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3198037\n",
      "avg_cost 1.1206727639111609\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.327003\n",
      "avg_cost 1.122788221185858\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.28955\n",
      "avg_cost 1.1248696303367618\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2728872\n",
      "avg_cost 1.1269358914548704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310584\n",
      "avg_cost 1.1290364224260507\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2858465\n",
      "avg_cost 1.1311144646731293\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3215067\n",
      "avg_cost 1.133224925344641\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305838\n",
      "avg_cost 1.135321141806516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295958\n",
      "avg_cost 1.137408376390284\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3220837\n",
      "avg_cost 1.1395193615826695\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291325\n",
      "avg_cost 1.1416023843938656\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2897549\n",
      "avg_cost 1.1436839797280054\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081367\n",
      "avg_cost 1.1457822858203544\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30449\n",
      "avg_cost 1.147877276810733\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2998412\n",
      "avg_cost 1.1499680415066809\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001854\n",
      "avg_cost 1.1520591191811997\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317687\n",
      "avg_cost 1.1541661073944787\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118455\n",
      "avg_cost 1.1562677851590244\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3139193\n",
      "avg_cost 1.1583713481642983\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056395\n",
      "avg_cost 1.160467384078286\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292175\n",
      "avg_cost 1.1625511795824226\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934895\n",
      "avg_cost 1.1646361699971288\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3186965\n",
      "avg_cost 1.1667440759051932\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3124833\n",
      "avg_cost 1.1688463334603745\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.315516\n",
      "avg_cost 1.170951348001307\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.324127\n",
      "avg_cost 1.1730641906911679\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2942808\n",
      "avg_cost 1.1751499004797503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2969291\n",
      "avg_cost 1.1772380178624935\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306136\n",
      "avg_cost 1.179334505037828\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3119307\n",
      "avg_cost 1.18143626018004\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2834792\n",
      "avg_cost 1.183512150374326\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.325278\n",
      "avg_cost 1.1856260395050051\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317677\n",
      "avg_cost 1.1877330186150292\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29009\n",
      "avg_cost 1.189814918691462\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310559\n",
      "avg_cost 1.1919154269045051\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301216\n",
      "avg_cost 1.1940074413472959\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314939\n",
      "avg_cost 1.1961119313673543\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2955942\n",
      "avg_cost 1.1981988351995299\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095791\n",
      "avg_cost 1.2002984525940639\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307679\n",
      "avg_cost 1.2023963425376203\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.30617\n",
      "avg_cost 1.2044928607073702\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294307\n",
      "avg_cost 1.2065785943378107\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999558\n",
      "avg_cost 1.2086694632877009\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292925\n",
      "avg_cost 1.2107539404522294\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098652\n",
      "avg_cost 1.2128538179397588\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2965386\n",
      "avg_cost 1.2149415802955632\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047504\n",
      "avg_cost 1.2170368079705676\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2904642\n",
      "avg_cost 1.2191190481185916\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2880344\n",
      "avg_cost 1.2211990794268524\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062043\n",
      "avg_cost 1.2232956288077617\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069685\n",
      "avg_cost 1.225392872853713\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117602\n",
      "avg_cost 1.2274944730238484\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3028219\n",
      "avg_cost 1.2295879474553197\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313275\n",
      "avg_cost 1.2316909248178658\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117633\n",
      "avg_cost 1.2337925278056754\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974465\n",
      "avg_cost 1.2358811155232519\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037188\n",
      "avg_cost 1.2379754053462637\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906802\n",
      "avg_cost 1.2400578418644994\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.322899\n",
      "avg_cost 1.2421695683219218\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2851627\n",
      "avg_cost 1.2442469889467416\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141432\n",
      "avg_cost 1.2463507554747844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038955\n",
      "avg_cost 1.248445205905221\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036203\n",
      "avg_cost 1.2505394062128936\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303142\n",
      "avg_cost 1.2526331717317756\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300278\n",
      "avg_cost 1.2547243335030296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305253\n",
      "avg_cost 1.2568200180747293\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3261883\n",
      "avg_cost 1.258934734734622\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308722\n",
      "avg_cost 1.2610335729338906\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984846\n",
      "avg_cost 1.263123104355552\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297374\n",
      "avg_cost 1.265211626182903\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018556\n",
      "avg_cost 1.2673042221502824\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2872534\n",
      "avg_cost 1.2693835434046659\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982998\n",
      "avg_cost 1.2714729068496011\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902088\n",
      "avg_cost 1.2735549148646268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078709\n",
      "avg_cost 1.2756529792872342\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301691\n",
      "avg_cost 1.2777454257011414\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297813\n",
      "avg_cost 1.2798343465544961\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3150177\n",
      "avg_cost 1.2819389081001282\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313419\n",
      "avg_cost 1.2840420163761486\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3120499\n",
      "avg_cost 1.2861438798904419\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029478\n",
      "avg_cost 1.2882374687628313\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292775\n",
      "avg_cost 1.2903218095952815\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907789\n",
      "avg_cost 1.2924043358456005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312098\n",
      "avg_cost 1.2945062431422147\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006666\n",
      "avg_cost 1.2965977582064543\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009934\n",
      "avg_cost 1.298689570426941\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051977\n",
      "avg_cost 1.300785204713995\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049645\n",
      "avg_cost 1.3028806270252575\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297278\n",
      "avg_cost 1.304969061504711\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078883\n",
      "avg_cost 1.3070671417496422\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3065972\n",
      "avg_cost 1.3091640483249318\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957997\n",
      "avg_cost 1.3112511389905757\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3170516\n",
      "avg_cost 1.3133575495806609\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037806\n",
      "avg_cost 1.3154518955404109\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079576\n",
      "avg_cost 1.3175500388578936\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30326\n",
      "avg_cost 1.3196439116651362\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031023\n",
      "avg_cost 1.321737640987743\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296547\n",
      "avg_cost 1.3238254109295933\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3142064\n",
      "avg_cost 1.3259292348948393\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118372\n",
      "avg_cost 1.3280309050733394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3140185\n",
      "avg_cost 1.3301345582441852\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30264\n",
      "avg_cost 1.3322278672998604\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313473\n",
      "avg_cost 1.3343310245600617\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294342\n",
      "avg_cost 1.3364167900518942\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306754\n",
      "avg_cost 1.338513839244843\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309713\n",
      "avg_cost 1.3406135782328523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305845\n",
      "avg_cost 1.3427098009803082\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304758\n",
      "avg_cost 1.3448050355911259\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3113632\n",
      "avg_cost 1.3469062748822302\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087568\n",
      "avg_cost 1.3490051447261466\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960696\n",
      "avg_cost 1.3510924807461828\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299833\n",
      "avg_cost 1.353183238072829\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300184\n",
      "avg_cost 1.355274314446883\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009136\n",
      "avg_cost 1.3573660540580752\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304822\n",
      "avg_cost 1.3594613467563286\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016038\n",
      "avg_cost 1.361553713841872\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010504\n",
      "avg_cost 1.3636455778642138\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997773\n",
      "avg_cost 1.365736284472726\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970965\n",
      "avg_cost 1.3678245540098715\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050704\n",
      "avg_cost 1.3699200725555425\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310982\n",
      "avg_cost 1.3720209652727307\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2959902\n",
      "avg_cost 1.3741082291169606\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056202\n",
      "avg_cost 1.376204247474671\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024435\n",
      "avg_cost 1.378297377933156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31494\n",
      "avg_cost 1.3804018688201911\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014672\n",
      "avg_cost 1.3824941117113294\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300088\n",
      "avg_cost 1.3845851007374856\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.306071\n",
      "avg_cost 1.386681528958408\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003497\n",
      "avg_cost 1.3887727559696552\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987344\n",
      "avg_cost 1.3908625145391993\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038244\n",
      "avg_cost 1.3929569003798754\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158283\n",
      "avg_cost 1.395062198855661\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991743\n",
      "avg_cost 1.3971523573181854\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309806\n",
      "avg_cost 1.3992521810531624\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056722\n",
      "avg_cost 1.4013482466611002\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938824\n",
      "avg_cost 1.4034335942701868\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2872896\n",
      "avg_cost 1.4055129484696829\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3105178\n",
      "avg_cost 1.407613419185986\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062394\n",
      "avg_cost 1.4097100004282872\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066192\n",
      "avg_cost 1.4118069269440399\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012478\n",
      "avg_cost 1.4138989704305482\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049195\n",
      "avg_cost 1.415994351777164\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016806\n",
      "avg_cost 1.418086788654328\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314533\n",
      "avg_cost 1.420190909559077\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316762\n",
      "avg_cost 1.4222970568050044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2883663\n",
      "avg_cost 1.4243773898211398\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125656\n",
      "avg_cost 1.4264797221530574\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115716\n",
      "avg_cost 1.4285811508785602\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043323\n",
      "avg_cost 1.4306759983843031\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092382\n",
      "avg_cost 1.4327753058346844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29931\n",
      "avg_cost 1.4348655876246375\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081872\n",
      "avg_cost 1.4369639396667488\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2978313\n",
      "avg_cost 1.439052877209404\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3205948\n",
      "avg_cost 1.4411625088344928\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090925\n",
      "avg_cost 1.4432616838541905\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021076\n",
      "avg_cost 1.44535450892015\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060005\n",
      "avg_cost 1.4474508729848001\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302703\n",
      "avg_cost 1.4495442392609343\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974622\n",
      "avg_cost 1.4516328412836255\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060894\n",
      "avg_cost 1.4537292861938482\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090625\n",
      "avg_cost 1.4558284339037815\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961645\n",
      "avg_cost 1.4579158561879944\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051763\n",
      "avg_cost 1.4600114709680736\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991326\n",
      "avg_cost 1.4621015915003694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303198\n",
      "avg_cost 1.4641954079541297\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30192\n",
      "avg_cost 1.4662880624424333\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2808259\n",
      "avg_cost 1.4683615404909311\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3105378\n",
      "avg_cost 1.4704620294137438\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909293\n",
      "avg_cost 1.4725446924296297\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3187647\n",
      "avg_cost 1.4746526603265246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036556\n",
      "avg_cost 1.4767468927123335\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299135\n",
      "avg_cost 1.4788370154120711\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305413\n",
      "avg_cost 1.480932845419104\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3128302\n",
      "avg_cost 1.4830354183370422\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037372\n",
      "avg_cost 1.4851297248493547\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3100326\n",
      "avg_cost 1.4872297544912865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3099334\n",
      "avg_cost 1.4893296939676464\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956355\n",
      "avg_cost 1.491416635296562\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054714\n",
      "avg_cost 1.4935125184059148\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007367\n",
      "avg_cost 1.495604097192938\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2912042\n",
      "avg_cost 1.4976870101148436\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069687\n",
      "avg_cost 1.499784254377539\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047123\n",
      "avg_cost 1.5018794473734773\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294956\n",
      "avg_cost 1.503965770981529\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300485\n",
      "avg_cost 1.5060571208867164\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.315099\n",
      "avg_cost 1.508161756342108\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3000357\n",
      "avg_cost 1.5102526979012927\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126426\n",
      "avg_cost 1.5123551002415747\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107083\n",
      "avg_cost 1.5144557441364639\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3106253\n",
      "avg_cost 1.5165563126043844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079405\n",
      "avg_cost 1.5186544403162874\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986434\n",
      "avg_cost 1.5207441160895612\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055823\n",
      "avg_cost 1.5228400999849496\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005884\n",
      "avg_cost 1.5249315439571036\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991984\n",
      "avg_cost 1.5270217243107884\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292762\n",
      "avg_cost 1.5291060534390537\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2978463\n",
      "avg_cost 1.5311950046365912\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2930365\n",
      "avg_cost 1.533279583237388\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306603\n",
      "avg_cost 1.5353764950145377\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.281214\n",
      "avg_cost 1.5374503259225327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3099515\n",
      "avg_cost 1.539550281871449\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043613\n",
      "avg_cost 1.5416451558199797\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023899\n",
      "avg_cost 1.543738237511028\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3127596\n",
      "avg_cost 1.545840746272694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021047\n",
      "avg_cost 1.5479335687377236\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2918959\n",
      "avg_cost 1.5500171104344453\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018956\n",
      "avg_cost 1.5521097428148443\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3065712\n",
      "avg_cost 1.5542066257650202\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015368\n",
      "avg_cost 1.556298931945454\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092742\n",
      "avg_cost 1.5583982721242038\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093479\n",
      "avg_cost 1.5604976792768999\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307836\n",
      "avg_cost 1.5625957120548595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3110583\n",
      "avg_cost 1.5646966741301795\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2750964\n",
      "avg_cost 1.5667649435997009\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.296022\n",
      "avg_cost 1.5688522362709045\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090553\n",
      "avg_cost 1.5709513774785129\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2959733\n",
      "avg_cost 1.5730386259339073\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012033\n",
      "avg_cost 1.5751306288892573\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2925975\n",
      "avg_cost 1.5772148084640503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2837555\n",
      "avg_cost 1.5792909498648209\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956147\n",
      "avg_cost 1.5813778723369945\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316102\n",
      "avg_cost 1.583483419635079\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2915652\n",
      "avg_cost 1.585566660707647\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2921648\n",
      "avg_cost 1.5876504468917847\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026595\n",
      "avg_cost 1.5897437737204811\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3080864\n",
      "avg_cost 1.5918420340798118\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008304\n",
      "avg_cost 1.5939336980472911\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084507\n",
      "avg_cost 1.5960322895917025\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087466\n",
      "avg_cost 1.59813115011562\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056173\n",
      "avg_cost 1.6002271658724003\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306506\n",
      "avg_cost 1.6023239894346757\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046017\n",
      "avg_cost 1.6044190818613224\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30056\n",
      "avg_cost 1.6065105000409212\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3147962\n",
      "avg_cost 1.608614860231226\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3106363\n",
      "avg_cost 1.610715438669378\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3190856\n",
      "avg_cost 1.6128236983039161\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2905514\n",
      "avg_cost 1.6149060177803038\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081815\n",
      "avg_cost 1.6170043646205554\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305264\n",
      "avg_cost 1.6191000591624867\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990735\n",
      "avg_cost 1.62119012594223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306276\n",
      "avg_cost 1.6232867405631324\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.28593\n",
      "avg_cost 1.625364858670668\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938595\n",
      "avg_cost 1.6274501854723147\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963605\n",
      "avg_cost 1.6295377859202296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981362\n",
      "avg_cost 1.631627000678669\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006718\n",
      "avg_cost 1.63371852051128\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301651\n",
      "avg_cost 1.635810930512168\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297082\n",
      "avg_cost 1.6378991868279196\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072522\n",
      "avg_cost 1.6399966887994244\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3160617\n",
      "avg_cost 1.6421021994677454\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087668\n",
      "avg_cost 1.6442010784149166\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301312\n",
      "avg_cost 1.646293180205605\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2967827\n",
      "avg_cost 1.648381164507432\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305126\n",
      "avg_cost 1.650476733554493\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299996\n",
      "avg_cost 1.6525676389174024\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3136098\n",
      "avg_cost 1.654670920588753\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077238\n",
      "avg_cost 1.6567688512802121\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022933\n",
      "avg_cost 1.6588618451898745\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022907\n",
      "avg_cost 1.660954836715351\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018634\n",
      "avg_cost 1.663047439835288\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2912292\n",
      "avg_cost 1.6651303755153306\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956975\n",
      "avg_cost 1.6672173731977287\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303604\n",
      "avg_cost 1.6693115585500542\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285014\n",
      "avg_cost 1.6713888439265163\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125918\n",
      "avg_cost 1.6734912001002917\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300499\n",
      "avg_cost 1.6755825627933847\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296331\n",
      "avg_cost 1.6776701363650233\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2888248\n",
      "avg_cost 1.6797508861801838\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932093\n",
      "avg_cost 1.6818356219204986\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307388\n",
      "avg_cost 1.6839332474361763\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980256\n",
      "avg_cost 1.6860223616253243\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2840483\n",
      "avg_cost 1.688098769187927\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043547\n",
      "avg_cost 1.6901936370676212\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992601\n",
      "avg_cost 1.692283873558044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985435\n",
      "avg_cost 1.6943734585155137\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29512\n",
      "avg_cost 1.6964599312435493\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298087\n",
      "avg_cost 1.698549101135947\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016443\n",
      "avg_cost 1.7006415050679982\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.321599\n",
      "avg_cost 1.7027520496195008\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3198395\n",
      "avg_cost 1.7048609945990818\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2894564\n",
      "avg_cost 1.7069423185695296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2678308\n",
      "avg_cost 1.709003982977433\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287976\n",
      "avg_cost 1.711083961183374\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308035\n",
      "avg_cost 1.7131821747259655\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.277695\n",
      "avg_cost 1.7152528064901174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040974\n",
      "avg_cost 1.71734744050286\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906513\n",
      "avg_cost 1.7194298507950518\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015738\n",
      "avg_cost 1.721522190570831\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.278053\n",
      "avg_cost 1.7235931478847153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022118\n",
      "avg_cost 1.725686067667874\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082433\n",
      "avg_cost 1.7277844706448637\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044577\n",
      "avg_cost 1.7298794321580362\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098671\n",
      "avg_cost 1.731979311379519\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973185\n",
      "avg_cost 1.73406778270548\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291185\n",
      "avg_cost 1.7361506780711082\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966638\n",
      "avg_cost 1.7382385542175982\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989993\n",
      "avg_cost 1.7403285535899071\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042107\n",
      "avg_cost 1.742423290556127\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3265676\n",
      "avg_cost 1.7445383520559827\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3094032\n",
      "avg_cost 1.7466378094933246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.322354\n",
      "avg_cost 1.7487490404735908\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3012917\n",
      "avg_cost 1.7508411238410253\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950544\n",
      "avg_cost 1.7529275369644162\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.283768\n",
      "avg_cost 1.7550036896358834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2867584\n",
      "avg_cost 1.7570825609293848\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047338\n",
      "avg_cost 1.7591777734322978\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123941\n",
      "avg_cost 1.7612799499251621\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2913213\n",
      "avg_cost 1.7633629692684516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135123\n",
      "avg_cost 1.7654661622914396\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.324313\n",
      "avg_cost 1.7675791740417475\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2815237\n",
      "avg_cost 1.76965328650041\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032737\n",
      "avg_cost 1.7717471716620699\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2914367\n",
      "avg_cost 1.7738302959095342\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2667913\n",
      "avg_cost 1.7758910153128877\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013623\n",
      "avg_cost 1.7779831628365943\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961805\n",
      "avg_cost 1.7800705996426662\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.331534\n",
      "avg_cost 1.7821901759234335\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089092\n",
      "avg_cost 1.78428918426687\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299047\n",
      "avg_cost 1.7863792269880114\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2913096\n",
      "avg_cost 1.7884622357108368\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2826006\n",
      "avg_cost 1.7905373272028828\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3182256\n",
      "avg_cost 1.7926448050412256\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3284354\n",
      "avg_cost 1.794761564514853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909598\n",
      "avg_cost 1.7968442552739916\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3138282\n",
      "avg_cost 1.7989477354829955\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290569\n",
      "avg_cost 1.8010300709984512\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069384\n",
      "avg_cost 1.8031272877346378\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2818153\n",
      "avg_cost 1.8052016652714116\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2937384\n",
      "avg_cost 1.8072868819670238\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095908\n",
      "avg_cost 1.8093865099820219\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920809\n",
      "avg_cost 1.8114702198722141\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294995\n",
      "avg_cost 1.8135565790263084\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3314567\n",
      "avg_cost 1.8156760850819669\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008676\n",
      "avg_cost 1.8177677828615357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300636\n",
      "avg_cost 1.8198592701825225\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2850573\n",
      "avg_cost 1.8219365950064221\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2749462\n",
      "avg_cost 1.8240047279271208\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3152854\n",
      "avg_cost 1.8261095328764476\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923052\n",
      "avg_cost 1.828193446722897\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980902\n",
      "avg_cost 1.830282619649713\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3105047\n",
      "avg_cost 1.8323830784450872\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310164\n",
      "avg_cost 1.8344832275130527\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3142824\n",
      "avg_cost 1.8365871206196867\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130648\n",
      "avg_cost 1.8386899068138811\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2882814\n",
      "avg_cost 1.8407701626690947\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986724\n",
      "avg_cost 1.8428598648851564\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030822\n",
      "avg_cost 1.8449535760012536\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2790635\n",
      "avg_cost 1.8470254518769\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3166094\n",
      "avg_cost 1.8491314604065632\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289576\n",
      "avg_cost 1.8512128931825809\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088129\n",
      "avg_cost 1.8533118139613756\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2930017\n",
      "avg_cost 1.8553963609175246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.32182\n",
      "avg_cost 1.8575071063908661\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304449\n",
      "avg_cost 1.8596020601012488\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2937841\n",
      "avg_cost 1.8616873184117402\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3172274\n",
      "avg_cost 1.8637938887422734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2889924\n",
      "avg_cost 1.8658747909285804\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064404\n",
      "avg_cost 1.867971554886211\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3225074\n",
      "avg_cost 1.870082925232974\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015437\n",
      "avg_cost 1.8721752376989886\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3147764\n",
      "avg_cost 1.874279579899528\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2921412\n",
      "avg_cost 1.8763633446259933\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298955\n",
      "avg_cost 1.878453303683888\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3113375\n",
      "avg_cost 1.8805545195666227\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3191407\n",
      "avg_cost 1.8826628292690624\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982752\n",
      "avg_cost 1.8847521703893488\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024035\n",
      "avg_cost 1.8868452644348144\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052301\n",
      "avg_cost 1.8889409281990743\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3100255\n",
      "avg_cost 1.8910409513386812\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017225\n",
      "avg_cost 1.893133426362818\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039513\n",
      "avg_cost 1.8952279275113886\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3121583\n",
      "avg_cost 1.897329889644276\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3193185\n",
      "avg_cost 1.8994383610378613\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289174\n",
      "avg_cost 1.9015194283832204\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3167808\n",
      "avg_cost 1.9036255927519365\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3114908\n",
      "avg_cost 1.905726948001168\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2835662\n",
      "avg_cost 1.9078029173070734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074489\n",
      "avg_cost 1.9099005980925126\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3149269\n",
      "avg_cost 1.9120050770586186\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126342\n",
      "avg_cost 1.914107471812855\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906632\n",
      "avg_cost 1.9161898929422552\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085978\n",
      "avg_cost 1.918288618217815\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30841\n",
      "avg_cost 1.9203871726989745\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999482\n",
      "avg_cost 1.9224780347130515\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020916\n",
      "avg_cost 1.9245708452571522\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064861\n",
      "avg_cost 1.9266676508296618\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29613\n",
      "avg_cost 1.9287550416859711\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3154502\n",
      "avg_cost 1.9308599964055146\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303252\n",
      "avg_cost 1.9329538618434559\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051124\n",
      "avg_cost 1.9350494185360994\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004398\n",
      "avg_cost 1.9371407274766401\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30951\n",
      "avg_cost 1.939240282015367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296695\n",
      "avg_cost 1.9413281865553422\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979147\n",
      "avg_cost 1.9434171999584544\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907953\n",
      "avg_cost 1.9454997411641206\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297558\n",
      "avg_cost 1.9475884303179654\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3033237\n",
      "avg_cost 1.9496823609958995\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306794\n",
      "avg_cost 1.9517794463851235\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2910955\n",
      "avg_cost 1.9538622604716906\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3157127\n",
      "avg_cost 1.9559674538265575\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303727\n",
      "avg_cost 1.958061751018871\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2883408\n",
      "avg_cost 1.960142060843381\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049684\n",
      "avg_cost 1.9622374866225503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029509\n",
      "avg_cost 1.9643310783126138\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002014\n",
      "avg_cost 1.9664221705089917\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971833\n",
      "avg_cost 1.9685105189410124\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285283\n",
      "avg_cost 1.9705880490216343\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117554\n",
      "avg_cost 1.9726896448568865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314558\n",
      "avg_cost 1.9747937885197726\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2911527\n",
      "avg_cost 1.976876654624939\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046443\n",
      "avg_cost 1.978971785848791\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.322752\n",
      "avg_cost 1.981083378575065\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051221\n",
      "avg_cost 1.9831789441542194\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2830877\n",
      "avg_cost 1.98525447845459\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3169878\n",
      "avg_cost 1.9873608309572395\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2908416\n",
      "avg_cost 1.9894434142112734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297408\n",
      "avg_cost 1.9915319670330396\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115184\n",
      "avg_cost 1.993633347424594\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309408\n",
      "avg_cost 1.9957328091968192\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021858\n",
      "avg_cost 1.9978257053548643\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3242364\n",
      "avg_cost 1.999938647530296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2911258\n",
      "avg_cost 2.002021489143372\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018816\n",
      "avg_cost 2.004114108735865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3176413\n",
      "avg_cost 2.0062210553342648\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016186\n",
      "avg_cost 2.0083134358579464\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3163404\n",
      "avg_cost 2.010419199900194\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30115\n",
      "avg_cost 2.0125111545215955\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2862606\n",
      "avg_cost 2.014589573253285\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2855225\n",
      "avg_cost 2.016667320945046\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302659\n",
      "avg_cost 2.018760647340254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008142\n",
      "avg_cost 2.0208522965691307\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047783\n",
      "avg_cost 2.0229475496032023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983708\n",
      "avg_cost 2.0250369776378983\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308163\n",
      "avg_cost 2.027135307572105\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310645\n",
      "avg_cost 2.029235894029791\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115287\n",
      "avg_cost 2.0313372837413444\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287402\n",
      "avg_cost 2.033416740027341\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3196018\n",
      "avg_cost 2.035525468912992\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964447\n",
      "avg_cost 2.037613145871596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.28735\n",
      "avg_cost 2.0396925549073655\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2942884\n",
      "avg_cost 2.0417782716317614\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037102\n",
      "avg_cost 2.0438725536519833\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2913125\n",
      "avg_cost 2.045955564975739\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305415\n",
      "avg_cost 2.048051396716725\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986386\n",
      "avg_cost 2.0501410681551153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3142123\n",
      "avg_cost 2.0522448975389653\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115337\n",
      "avg_cost 2.0543462918021462\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004067\n",
      "avg_cost 2.0564375706152482\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123224\n",
      "avg_cost 2.05853968186812\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073401\n",
      "avg_cost 2.060637263818221\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299061\n",
      "avg_cost 2.062727319327268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2899086\n",
      "avg_cost 2.0648090544613926\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289239\n",
      "avg_cost 2.066890180761164\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008218\n",
      "avg_cost 2.0689818369258535\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3142612\n",
      "avg_cost 2.071085710742257\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002322\n",
      "avg_cost 2.073176830898632\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301046\n",
      "avg_cost 2.0752686908028344\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108253\n",
      "avg_cost 2.0773694411191075\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306244\n",
      "avg_cost 2.079466026479548\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312531\n",
      "avg_cost 2.081568327383562\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047314\n",
      "avg_cost 2.0836635377190333\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988203\n",
      "avg_cost 2.085753374316476\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902868\n",
      "avg_cost 2.087835453206843\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3142736\n",
      "avg_cost 2.089939338293943\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976396\n",
      "avg_cost 2.092028101574291\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909744\n",
      "avg_cost 2.094110805554824\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968786\n",
      "avg_cost 2.0961988769878044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3065119\n",
      "avg_cost 2.0982957059686838\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3104928\n",
      "avg_cost 2.10039615392685\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.315136\n",
      "avg_cost 2.1025008229775866\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083985\n",
      "avg_cost 2.1045993670550263\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2959235\n",
      "avg_cost 2.106686570210891\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313002\n",
      "avg_cost 2.1087892994013706\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3219001\n",
      "avg_cost 2.110900117700751\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3052413\n",
      "avg_cost 2.112995791651987\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971182\n",
      "avg_cost 2.115084080912851\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29512\n",
      "avg_cost 2.1171705536408867\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013358\n",
      "avg_cost 2.1192626771059913\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3114595\n",
      "avg_cost 2.1213640039617374\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077831\n",
      "avg_cost 2.123461988622493\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303796\n",
      "avg_cost 2.1255563486706137\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299141\n",
      "avg_cost 2.1276464767889554\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887325\n",
      "avg_cost 2.129727142724125\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306878\n",
      "avg_cost 2.1318243046240384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3173869\n",
      "avg_cost 2.1339310199564165\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2870054\n",
      "avg_cost 2.1360101157968705\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3172796\n",
      "avg_cost 2.138116733594375\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317135\n",
      "avg_cost 2.140223220044917\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067222\n",
      "avg_cost 2.142320240194148\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301926\n",
      "avg_cost 2.1444129001010555\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005028\n",
      "avg_cost 2.1465042662620553\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297187\n",
      "avg_cost 2.1485926181619828\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966743\n",
      "avg_cost 2.1506805038452157\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045683\n",
      "avg_cost 2.15277556592768\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304264\n",
      "avg_cost 2.1548703514445924\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108244\n",
      "avg_cost 2.1569711008938888\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304864\n",
      "avg_cost 2.159066431739115\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089814\n",
      "avg_cost 2.1611655057560326\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046277\n",
      "avg_cost 2.163260621807793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989361\n",
      "avg_cost 2.1653505637428987\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029199\n",
      "avg_cost 2.167444127256221\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303592\n",
      "avg_cost 2.1695383017713383\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085096\n",
      "avg_cost 2.171636946851558\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993047\n",
      "avg_cost 2.1737272238731395\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078666\n",
      "avg_cost 2.175825284394352\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994244\n",
      "avg_cost 2.1779156702215032\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123024\n",
      "avg_cost 2.180017763267865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024623\n",
      "avg_cost 2.1821109108491386\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30519\n",
      "avg_cost 2.184206538200379\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30006\n",
      "avg_cost 2.186297501867468\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934177\n",
      "avg_cost 2.1883824270421814\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058286\n",
      "avg_cost 2.19047863483429\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093598\n",
      "avg_cost 2.1925780528241945\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303194\n",
      "avg_cost 2.194671865593304\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3099957\n",
      "avg_cost 2.1967718616398906\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301235\n",
      "avg_cost 2.198863893422214\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975338\n",
      "avg_cost 2.200952560468154\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3094788\n",
      "avg_cost 2.2030520866133956\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999167\n",
      "avg_cost 2.205142920017243\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309504\n",
      "avg_cost 2.2072424691373658\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062713\n",
      "avg_cost 2.2093390794233847\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012047\n",
      "avg_cost 2.2114310836791997\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303584\n",
      "avg_cost 2.2135252510417596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956283\n",
      "avg_cost 2.21561218586835\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288806\n",
      "avg_cost 2.2176929185607217\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310024\n",
      "avg_cost 2.2197929403998637\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988539\n",
      "avg_cost 2.2218828075582335\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3116112\n",
      "avg_cost 2.2239842722632672\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956648\n",
      "avg_cost 2.226071240251715\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979615\n",
      "avg_cost 2.228160296136683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976632\n",
      "avg_cost 2.2302490808747035\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066764\n",
      "avg_cost 2.232346059409055\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025\n",
      "avg_cost 2.234439241235907\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293696\n",
      "avg_cost 2.236524419351058\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013685\n",
      "avg_cost 2.238616572510113\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.318598\n",
      "avg_cost 2.240724388902838\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2936523\n",
      "avg_cost 2.2428095273538076\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3121176\n",
      "avg_cost 2.2449114524234433\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306085\n",
      "avg_cost 2.2470078934322713\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30703\n",
      "avg_cost 2.2491051933982162\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980967\n",
      "avg_cost 2.2511943721771246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291018\n",
      "avg_cost 2.253277115821839\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2939804\n",
      "avg_cost 2.2553625525127763\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302981\n",
      "avg_cost 2.2574561715126045\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966444\n",
      "avg_cost 2.259544030102817\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039317\n",
      "avg_cost 2.2616385134783665\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306208\n",
      "avg_cost 2.263735066110438\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004463\n",
      "avg_cost 2.265826380903071\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304453\n",
      "avg_cost 2.26792133808136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017242\n",
      "avg_cost 2.270013814622706\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082743\n",
      "avg_cost 2.272112245776437\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2939184\n",
      "avg_cost 2.274197626113892\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956626\n",
      "avg_cost 2.276284592151642\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036785\n",
      "avg_cost 2.27837884534489\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294091\n",
      "avg_cost 2.280464382605119\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3148274\n",
      "avg_cost 2.282568771188909\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297595\n",
      "avg_cost 2.284657493938099\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2858806\n",
      "avg_cost 2.286735567179593\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017554\n",
      "avg_cost 2.288828072114424\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954545\n",
      "avg_cost 2.29091484893452\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297547\n",
      "avg_cost 2.293003528118133\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3032289\n",
      "avg_cost 2.2950973725318904\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2752311\n",
      "avg_cost 2.2971657644618637\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996032\n",
      "avg_cost 2.299256312847137\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084092\n",
      "avg_cost 2.301354866678064\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3191361\n",
      "avg_cost 2.3034631722623646\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29077\n",
      "avg_cost 2.3055456904931497\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984433\n",
      "avg_cost 2.307635184418071\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3178585\n",
      "avg_cost 2.309742328470403\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308318\n",
      "avg_cost 2.3118407992883157\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3144684\n",
      "avg_cost 2.3139448614553966\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3159523\n",
      "avg_cost 2.316050272638147\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075836\n",
      "avg_cost 2.318148075884038\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3154671\n",
      "avg_cost 2.320253045992417\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3091705\n",
      "avg_cost 2.322352291887456\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993605\n",
      "avg_cost 2.3244426196271717\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3176498\n",
      "avg_cost 2.3265495740283613\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029838\n",
      "avg_cost 2.328643195629119\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302701\n",
      "avg_cost 2.3307365601713\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956595\n",
      "avg_cost 2.332823523391376\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3121893\n",
      "avg_cost 2.3349255137010045\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985208\n",
      "avg_cost 2.3370150780677785\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2941985\n",
      "avg_cost 2.339100713079625\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980313\n",
      "avg_cost 2.341189832470633\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3122869\n",
      "avg_cost 2.343291911428624\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125117\n",
      "avg_cost 2.3453941947763606\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008971\n",
      "avg_cost 2.3474859194322053\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024206\n",
      "avg_cost 2.349579029083251\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952294\n",
      "avg_cost 2.3516656012968573\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985315\n",
      "avg_cost 2.3537551754171187\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2917871\n",
      "avg_cost 2.355838618278502\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2931378\n",
      "avg_cost 2.357923288995568\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986188\n",
      "avg_cost 2.360012942444193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30377\n",
      "avg_cost 2.3621072788672\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.323158\n",
      "avg_cost 2.3642192407087834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2905564\n",
      "avg_cost 2.3663015647367986\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2882202\n",
      "avg_cost 2.3683817648887624\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994294\n",
      "avg_cost 2.370472155267541\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046594\n",
      "avg_cost 2.372567300146275\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088326\n",
      "avg_cost 2.3746662389148354\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951543\n",
      "avg_cost 2.3767527428540305\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098044\n",
      "avg_cost 2.3788525650717984\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024802\n",
      "avg_cost 2.3809457289088845\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.283734\n",
      "avg_cost 2.3830218508026806\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2840617\n",
      "avg_cost 2.385098270502956\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2927117\n",
      "avg_cost 2.3871825538982034\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285924\n",
      "avg_cost 2.389260666587135\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045163\n",
      "avg_cost 2.3913556814193715\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30151\n",
      "avg_cost 2.393447963324459\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2998507\n",
      "avg_cost 2.3955387366901735\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088768\n",
      "avg_cost 2.397637715556404\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2893574\n",
      "avg_cost 2.3997189495780242\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3170724\n",
      "avg_cost 2.4018253790248516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989025\n",
      "avg_cost 2.4039152903990306\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049104\n",
      "avg_cost 2.406010663509368\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976296\n",
      "avg_cost 2.4080994176864614\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007274\n",
      "avg_cost 2.4101909880204624\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.315858\n",
      "avg_cost 2.4122963133725244\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3151588\n",
      "avg_cost 2.414401003230701\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3111768\n",
      "avg_cost 2.41650207302787\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074975\n",
      "avg_cost 2.4185997980291183\n",
      "---------------------\n",
      "Epoch: 1 cost = 2.419 test accuracy: 0.113\n",
      "====================================\n",
      "epoch 1\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.282829\n",
      "avg_cost 0.0020752991329539908\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963357\n",
      "avg_cost 0.0041628770394758745\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.279568\n",
      "avg_cost 0.006235211545770818\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034022\n",
      "avg_cost 0.008329213532534512\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314324\n",
      "avg_cost 0.010433144352652808\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968245\n",
      "avg_cost 0.012521166584708473\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.320875\n",
      "avg_cost 0.014631052884188565\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292939\n",
      "avg_cost 0.016715542836622757\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035946\n",
      "avg_cost 0.018809719735925847\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2783494\n",
      "avg_cost 0.02088094646280462\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973354\n",
      "avg_cost 0.022969433177601205\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30697\n",
      "avg_cost 0.02506667852401733\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.315097\n",
      "avg_cost 0.027171312245455652\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130028\n",
      "avg_cost 0.029274042086167767\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3200712\n",
      "avg_cost 0.031383197741074995\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043883\n",
      "avg_cost 0.03347809618169611\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107173\n",
      "avg_cost 0.03557874831286344\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980518\n",
      "avg_cost 0.0376678863438693\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018732\n",
      "avg_cost 0.03976049835031683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2942579\n",
      "avg_cost 0.04184618733145974\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3180094\n",
      "avg_cost 0.043953468582846905\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312587\n",
      "avg_cost 0.046055820421739065\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098452\n",
      "avg_cost 0.048155679702758795\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3192801\n",
      "avg_cost 0.05026411620053379\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2986944\n",
      "avg_cost 0.052353838357058445\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018332\n",
      "avg_cost 0.054446413950486626\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003237\n",
      "avg_cost 0.056537617336619994\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2948194\n",
      "avg_cost 0.058623816750266344\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3048975\n",
      "avg_cost 0.060719178156419244\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290472\n",
      "avg_cost 0.06280142545700074\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064802\n",
      "avg_cost 0.06489822561090643\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088202\n",
      "avg_cost 0.06699715310877019\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296612\n",
      "avg_cost 0.06908498222177678\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3180332\n",
      "avg_cost 0.07119228514758022\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005972\n",
      "avg_cost 0.07328373713926833\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2870758\n",
      "avg_cost 0.07536289691925047\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.325324\n",
      "avg_cost 0.07747682788155294\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996485\n",
      "avg_cost 0.0795674174482172\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2821703\n",
      "avg_cost 0.08164211771704932\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060296\n",
      "avg_cost 0.08373850822448729\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007565\n",
      "avg_cost 0.08583010500127618\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3200197\n",
      "avg_cost 0.08793921383944424\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995913\n",
      "avg_cost 0.09002975138750942\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951987\n",
      "avg_cost 0.09211629564111881\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006682\n",
      "avg_cost 0.09420781222256745\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297088\n",
      "avg_cost 0.09629607395692304\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887127\n",
      "avg_cost 0.09837672190232709\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29898\n",
      "avg_cost 0.10046670371835881\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2745984\n",
      "avg_cost 0.10253452040932394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296235\n",
      "avg_cost 0.10462200684980912\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2861896\n",
      "avg_cost 0.10670036099173805\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008933\n",
      "avg_cost 0.1087920821796764\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015792\n",
      "avg_cost 0.11088442694057117\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2763145\n",
      "avg_cost 0.11295380375602028\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115263\n",
      "avg_cost 0.11505519130013205\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296973\n",
      "avg_cost 0.11714334856380115\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024354\n",
      "avg_cost 0.11923647165298461\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311649\n",
      "avg_cost 0.12133797082034023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310626\n",
      "avg_cost 0.1234385399384932\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991817\n",
      "avg_cost 0.12552870512008665\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3200915\n",
      "avg_cost 0.1276378791982477\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2935352\n",
      "avg_cost 0.1297229112278331\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019028\n",
      "avg_cost 0.13181555011055685\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979434\n",
      "avg_cost 0.13390458952296863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992382\n",
      "avg_cost 0.1359948060729287\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977467\n",
      "avg_cost 0.13808366667140615\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3205233\n",
      "avg_cost 0.1401932332732461\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938387\n",
      "avg_cost 0.14227854121815076\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009539\n",
      "avg_cost 0.14437031745910645\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2941756\n",
      "avg_cost 0.14645593166351317\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962997\n",
      "avg_cost 0.14854347684166647\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.284875\n",
      "avg_cost 0.1506206358562816\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2879076\n",
      "avg_cost 0.15270055185664783\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141234\n",
      "avg_cost 0.15480430039492518\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301184\n",
      "avg_cost 0.156896285793998\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3109608\n",
      "avg_cost 0.15899715922095556\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3105173\n",
      "avg_cost 0.16109762950377027\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043609\n",
      "avg_cost 0.16319250301881266\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3187437\n",
      "avg_cost 0.1653004518422213\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3186839\n",
      "avg_cost 0.1674083462628451\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980301\n",
      "avg_cost 0.16949746457013212\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289288\n",
      "avg_cost 0.17157863551920105\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3218691\n",
      "avg_cost 0.1736894256418401\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.330295\n",
      "avg_cost 0.17580787571993733\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042817\n",
      "avg_cost 0.17790267727591766\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311629\n",
      "avg_cost 0.1800041582367636\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3116782\n",
      "avg_cost 0.1821056838469071\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303229\n",
      "avg_cost 0.1841995284774086\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2948098\n",
      "avg_cost 0.18628571922128842\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3061354\n",
      "avg_cost 0.18838220596313468\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009722\n",
      "avg_cost 0.19047399889339092\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025277\n",
      "avg_cost 0.1925672058625654\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012667\n",
      "avg_cost 0.1946592664718627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2941713\n",
      "avg_cost 0.1967448767748745\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291854\n",
      "avg_cost 0.19882838032462372\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055093\n",
      "avg_cost 0.2009242978962984\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3152246\n",
      "avg_cost 0.20302904757586382\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991188\n",
      "avg_cost 0.2051191555369983\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993495\n",
      "avg_cost 0.2072094733064824\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088408\n",
      "avg_cost 0.20930841944434417\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007376\n",
      "avg_cost 0.2113999990983442\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994883\n",
      "avg_cost 0.21349044301293105\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987065\n",
      "avg_cost 0.215580176223408\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984276\n",
      "avg_cost 0.21766965584321446\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980554\n",
      "avg_cost 0.21975879712538277\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3146276\n",
      "avg_cost 0.22186300407756449\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976065\n",
      "avg_cost 0.2239517372304742\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991576\n",
      "avg_cost 0.22604188052090723\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2892878\n",
      "avg_cost 0.228123051253232\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294983\n",
      "avg_cost 0.23020939935337406\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2926404\n",
      "avg_cost 0.2322936179421164\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943676\n",
      "avg_cost 0.2343794066255742\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2891054\n",
      "avg_cost 0.2364604115486144\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3114202\n",
      "avg_cost 0.23856170264157373\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3096352\n",
      "avg_cost 0.240661370970986\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309781\n",
      "avg_cost 0.24276117194782593\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299547\n",
      "avg_cost 0.24485166918147683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314233\n",
      "avg_cost 0.2469555174220691\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3140497\n",
      "avg_cost 0.24905919898640014\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3000164\n",
      "avg_cost 0.2511501229893076\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3136556\n",
      "avg_cost 0.25325344627553753\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088384\n",
      "avg_cost 0.2553523902459577\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3104918\n",
      "avg_cost 0.25745283733714697\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309454\n",
      "avg_cost 0.2595523409409955\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3131762\n",
      "avg_cost 0.261655228354714\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022923\n",
      "avg_cost 0.2637482213973998\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2908065\n",
      "avg_cost 0.2658307727900417\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298687\n",
      "avg_cost 0.2679204882274973\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943814\n",
      "avg_cost 0.2700062894821166\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291845\n",
      "avg_cost 0.2720897850123318\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014991\n",
      "avg_cost 0.27418205694718784\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2904932\n",
      "avg_cost 0.27626432353799985\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024108\n",
      "avg_cost 0.2783574243025345\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962773\n",
      "avg_cost 0.2804449491067365\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005495\n",
      "avg_cost 0.28253635774959207\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3193047\n",
      "avg_cost 0.28464481657201585\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298213\n",
      "avg_cost 0.28673410112207576\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2998421\n",
      "avg_cost 0.28882486668500024\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095832\n",
      "avg_cost 0.290924487764185\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2998629\n",
      "avg_cost 0.29301527218385165\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2878072\n",
      "avg_cost 0.2950950969349253\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985103\n",
      "avg_cost 0.2971846517649563\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003476\n",
      "avg_cost 0.29927587682550594\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988265\n",
      "avg_cost 0.3013657190582968\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060534\n",
      "avg_cost 0.30346213124015103\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022714\n",
      "avg_cost 0.3055551052093505\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909935\n",
      "avg_cost 0.30763782652941607\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3112075\n",
      "avg_cost 0.3097389242865822\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2922225\n",
      "avg_cost 0.3118227629228071\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3177848\n",
      "avg_cost 0.3139298400011929\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909079\n",
      "avg_cost 0.316012483510104\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999625\n",
      "avg_cost 0.3181033585288307\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032572\n",
      "avg_cost 0.3201972287351434\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063946\n",
      "avg_cost 0.32229395107789466\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309445\n",
      "avg_cost 0.32439344644546503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063955\n",
      "avg_cost 0.3264901696551929\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2901511\n",
      "avg_cost 0.32857212521813123\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024502\n",
      "avg_cost 0.3306652617454528\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012366\n",
      "avg_cost 0.3327572950449856\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046808\n",
      "avg_cost 0.33485245943069447\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311006\n",
      "avg_cost 0.33695337403904296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304632\n",
      "avg_cost 0.3390484939921985\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934582\n",
      "avg_cost 0.3411334560134193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291092\n",
      "avg_cost 0.34321626684882417\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2897835\n",
      "avg_cost 0.3452978881922634\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130927\n",
      "avg_cost 0.3474006997455249\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29778\n",
      "avg_cost 0.34948959068818514\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310491\n",
      "avg_cost 0.35159003712914194\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3161051\n",
      "avg_cost 0.3536955872449007\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019984\n",
      "avg_cost 0.3557883130420337\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982464\n",
      "avg_cost 0.3578776279362764\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2918255\n",
      "avg_cost 0.35996110569347023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2841623\n",
      "avg_cost 0.3620376168597827\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.323192\n",
      "avg_cost 0.3641496094790371\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313439\n",
      "avg_cost 0.366252735744823\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015091\n",
      "avg_cost 0.3683450167829339\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2870126\n",
      "avg_cost 0.3704241191257129\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987952\n",
      "avg_cost 0.3725139329650184\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043816\n",
      "avg_cost 0.37460882533680295\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3199992\n",
      "avg_cost 0.376717915534973\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960258\n",
      "avg_cost 0.37880521167408326\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2940776\n",
      "avg_cost 0.3808907367966391\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31209\n",
      "avg_cost 0.3829926367239518\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956674\n",
      "avg_cost 0.3850796070965853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108687\n",
      "avg_cost 0.387180396860296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3121164\n",
      "avg_cost 0.3892823208462107\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316802\n",
      "avg_cost 0.3913885045051574\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074806\n",
      "avg_cost 0.3934862141175703\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2890139\n",
      "avg_cost 0.39556713581085196\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2768173\n",
      "avg_cost 0.39763696973974044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3000278\n",
      "avg_cost 0.3997279041463678\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2945838\n",
      "avg_cost 0.40181388941678126\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092577\n",
      "avg_cost 0.4039132146401838\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3213205\n",
      "avg_cost 0.4060235060345042\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2834623\n",
      "avg_cost 0.4080993808399547\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290777\n",
      "avg_cost 0.41018190535632043\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29815\n",
      "avg_cost 0.41227113268592136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032267\n",
      "avg_cost 0.41436497514898124\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963214\n",
      "avg_cost 0.41645254005085336\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2904947\n",
      "avg_cost 0.4185348079421303\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.323297\n",
      "avg_cost 0.4206468961455605\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291404\n",
      "avg_cost 0.42272999069907446\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018775\n",
      "avg_cost 0.42482260660691695\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303393\n",
      "avg_cost 0.4269166001406583\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31052\n",
      "avg_cost 0.42901707280765883\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946663\n",
      "avg_cost 0.4311031330715526\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2915797\n",
      "avg_cost 0.43318638736551457\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022192\n",
      "avg_cost 0.4352793138677424\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308165\n",
      "avg_cost 0.4373776457526467\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977228\n",
      "avg_cost 0.43946648467670785\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2933385\n",
      "avg_cost 0.44155133789235895\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302668\n",
      "avg_cost 0.4436446725238453\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.270847\n",
      "avg_cost 0.44570907896215267\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2929833\n",
      "avg_cost 0.44779360922900113\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044572\n",
      "avg_cost 0.4498885703086853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.320189\n",
      "avg_cost 0.45199783303520896\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055253\n",
      "avg_cost 0.45409376512874255\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038287\n",
      "avg_cost 0.45618815487081354\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2929413\n",
      "avg_cost 0.45827264699068937\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2849827\n",
      "avg_cost 0.4603499039736661\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2885356\n",
      "avg_cost 0.4624303908781572\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082526\n",
      "avg_cost 0.4645288023081693\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31713\n",
      "avg_cost 0.46663528420708394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3184063\n",
      "avg_cost 0.4687429263375022\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956343\n",
      "avg_cost 0.47082986658269704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2969155\n",
      "avg_cost 0.4729179716110229\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302723\n",
      "avg_cost 0.47501135609366674\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044872\n",
      "avg_cost 0.4771063444831154\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308173\n",
      "avg_cost 0.4792046835205771\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976773\n",
      "avg_cost 0.48129348104650316\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303132\n",
      "avg_cost 0.48338723746213036\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29008\n",
      "avg_cost 0.4854691284353082\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044262\n",
      "avg_cost 0.4875640613382512\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308551\n",
      "avg_cost 0.48966274413195515\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293679\n",
      "avg_cost 0.49174790685827074\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.282871\n",
      "avg_cost 0.4938232441381974\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2914054\n",
      "avg_cost 0.4959063399921763\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3219109\n",
      "avg_cost 0.49801716804504387\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996218\n",
      "avg_cost 0.5001077333363619\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2905777\n",
      "avg_cost 0.5021900766546076\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064566\n",
      "avg_cost 0.5042868553508412\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2903726\n",
      "avg_cost 0.5063690122691068\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3202283\n",
      "avg_cost 0.5084783107584173\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.32646\n",
      "avg_cost 0.5105932742899114\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996688\n",
      "avg_cost 0.5126838822798295\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095503\n",
      "avg_cost 0.5147834734483199\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024132\n",
      "avg_cost 0.5168765763802962\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079252\n",
      "avg_cost 0.5189746902205727\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047783\n",
      "avg_cost 0.5210699432546443\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092124\n",
      "avg_cost 0.5231692272966559\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075247\n",
      "avg_cost 0.525266977006739\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303563\n",
      "avg_cost 0.5273611252958126\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003242\n",
      "avg_cost 0.5294523291154343\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3197658\n",
      "avg_cost 0.5315612071210689\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311146\n",
      "avg_cost 0.533662248958241\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968428\n",
      "avg_cost 0.5357502878795972\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2888496\n",
      "avg_cost 0.5378310602361507\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300836\n",
      "avg_cost 0.5399227294054899\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887108\n",
      "avg_cost 0.5420033756169407\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092337\n",
      "avg_cost 0.5441026789491827\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988434\n",
      "avg_cost 0.5461925365708091\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301341\n",
      "avg_cost 0.5482846648042853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2918465\n",
      "avg_cost 0.5503681616349654\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301116\n",
      "avg_cost 0.5524600852619518\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297718\n",
      "avg_cost 0.5545489198511296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979195\n",
      "avg_cost 0.5566379375891252\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298044\n",
      "avg_cost 0.5587270684675737\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2929602\n",
      "avg_cost 0.5608115777102384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.283508\n",
      "avg_cost 0.562887494130568\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2898314\n",
      "avg_cost 0.564969159039584\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308165\n",
      "avg_cost 0.5670674909244884\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956219\n",
      "avg_cost 0.5691544198989867\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312948\n",
      "avg_cost 0.5712570998885415\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3290076\n",
      "avg_cost 0.5733743795481595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044705\n",
      "avg_cost 0.5754693527655168\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938747\n",
      "avg_cost 0.5775546934387901\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313064\n",
      "avg_cost 0.579657478982752\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2947729\n",
      "avg_cost 0.5817436361312865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071818\n",
      "avg_cost 0.5838410741632635\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2739608\n",
      "avg_cost 0.5859083112803373\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306024\n",
      "avg_cost 0.5880046968026595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021035\n",
      "avg_cost 0.5900975181839684\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2880442\n",
      "avg_cost 0.5921775583787399\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3106217\n",
      "avg_cost 0.5942781235954979\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3233597\n",
      "avg_cost 0.5963902688026429\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960198\n",
      "avg_cost 0.5984775595231491\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3248093\n",
      "avg_cost 0.600591022534804\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309356\n",
      "avg_cost 0.6026904370568016\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125243\n",
      "avg_cost 0.6047927318919789\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302811\n",
      "avg_cost 0.6068861963532188\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29561\n",
      "avg_cost 0.6089731144905091\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29134\n",
      "avg_cost 0.6110561509565874\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316018\n",
      "avg_cost 0.6131616219607267\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304819\n",
      "avg_cost 0.61525691205805\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920737\n",
      "avg_cost 0.6173406154459173\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311261\n",
      "avg_cost 0.6194417617537759\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3100402\n",
      "avg_cost 0.6215417983315208\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289412\n",
      "avg_cost 0.6236230819875543\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3139658\n",
      "avg_cost 0.6257266872579401\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980688\n",
      "avg_cost 0.6278158406777815\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3033748\n",
      "avg_cost 0.6299098177389665\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067825\n",
      "avg_cost 0.6320068927244706\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.274033\n",
      "avg_cost 0.6340741955150258\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2735322\n",
      "avg_cost 0.6361410429260947\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991061\n",
      "avg_cost 0.6382311393997886\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3195455\n",
      "avg_cost 0.6403398171338168\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952127\n",
      "avg_cost 0.6424263741753319\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108141\n",
      "avg_cost 0.6445271143046293\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2936819\n",
      "avg_cost 0.6466122796318748\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058398\n",
      "avg_cost 0.6487084976109592\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3091838\n",
      "avg_cost 0.6508077556436712\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098507\n",
      "avg_cost 0.6529076199098067\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997894\n",
      "avg_cost 0.6549983375722712\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2931342\n",
      "avg_cost 0.6570830050381747\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2877026\n",
      "avg_cost 0.6591627346385609\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3138924\n",
      "avg_cost 0.6612662731517445\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126469\n",
      "avg_cost 0.6633686793934215\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2926328\n",
      "avg_cost 0.6654528910463506\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302303\n",
      "avg_cost 0.6675458938425237\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2845812\n",
      "avg_cost 0.6696227858283302\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2912226\n",
      "avg_cost 0.6717057154395363\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300661\n",
      "avg_cost 0.6737972255186601\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2873104\n",
      "avg_cost 0.6758765985748985\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045585\n",
      "avg_cost 0.6779716517708518\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977293\n",
      "avg_cost 0.6800604965470054\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2917075\n",
      "avg_cost 0.6821438670158386\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303422\n",
      "avg_cost 0.6842378869923679\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982233\n",
      "avg_cost 0.6863271808624268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034103\n",
      "avg_cost 0.688421190218492\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954142\n",
      "avg_cost 0.6905079304088246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3208458\n",
      "avg_cost 0.6926177902655168\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3222377\n",
      "avg_cost 0.6947289154746316\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297565\n",
      "avg_cost 0.6968176109140569\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058624\n",
      "avg_cost 0.6989138494838367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2823713\n",
      "avg_cost 0.7009887324679981\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2917476\n",
      "avg_cost 0.7030721393498507\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313262\n",
      "avg_cost 0.7051751047914678\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085446\n",
      "avg_cost 0.7072737817330794\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011115\n",
      "avg_cost 0.7093657012419268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.330662\n",
      "avg_cost 0.7114844848892906\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130624\n",
      "avg_cost 0.7135872689160434\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287855\n",
      "avg_cost 0.7156671370159496\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977338\n",
      "avg_cost 0.7177559859102423\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979472\n",
      "avg_cost 0.7198450287905607\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285153\n",
      "avg_cost 0.7219224405288697\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301283\n",
      "avg_cost 0.72401451587677\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3220603\n",
      "avg_cost 0.7261254798282276\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141274\n",
      "avg_cost 0.7282292320511558\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055003\n",
      "avg_cost 0.7303251413865522\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3249106\n",
      "avg_cost 0.7324386965144764\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308324\n",
      "avg_cost 0.7345371729677372\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968514\n",
      "avg_cost 0.7366252196918833\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301126\n",
      "avg_cost 0.7387171524221245\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3217666\n",
      "avg_cost 0.7408278493447736\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288926\n",
      "avg_cost 0.7429086910594592\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2969875\n",
      "avg_cost 0.7449968615445223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3080971\n",
      "avg_cost 0.7470951316573402\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2965658\n",
      "avg_cost 0.7491829187219792\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053076\n",
      "avg_cost 0.7512786529280921\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309349\n",
      "avg_cost 0.753378061164509\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038213\n",
      "avg_cost 0.7554724441875109\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306798\n",
      "avg_cost 0.7575695332613857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2905548\n",
      "avg_cost 0.7596518557721916\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2843668\n",
      "avg_cost 0.7617285529049959\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038573\n",
      "avg_cost 0.7638229686563663\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015258\n",
      "avg_cost 0.7659152648665687\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2856317\n",
      "avg_cost 0.7679931118271566\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074355\n",
      "avg_cost 0.7700907804749226\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295339\n",
      "avg_cost 0.7721774523908439\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952359\n",
      "avg_cost 0.7742640304565427\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973826\n",
      "avg_cost 0.7763525600866835\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002405\n",
      "avg_cost 0.7784436878291041\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29453\n",
      "avg_cost 0.7805296241153368\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087575\n",
      "avg_cost 0.7826284946094857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053946\n",
      "avg_cost 0.784724307927218\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039644\n",
      "avg_cost 0.7868188209967176\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906344\n",
      "avg_cost 0.788901215900074\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084939\n",
      "avg_cost 0.7909998466751789\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3059714\n",
      "avg_cost 0.7930961842970411\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301762\n",
      "avg_cost 0.7951886953007088\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3195102\n",
      "avg_cost 0.7972973409566009\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2903695\n",
      "avg_cost 0.7993794950571924\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3264124\n",
      "avg_cost 0.8014944154565982\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2886171\n",
      "avg_cost 0.8035749764875929\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085601\n",
      "avg_cost 0.8056736675175751\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3132243\n",
      "avg_cost 0.8077765987136144\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036385\n",
      "avg_cost 0.8098708154938434\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2832494\n",
      "avg_cost 0.8119464967467565\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3267028\n",
      "avg_cost 0.8140616811405526\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303775\n",
      "avg_cost 0.8161560221151869\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299381\n",
      "avg_cost 0.8182463684949005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2978144\n",
      "avg_cost 0.8203352906487202\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003523\n",
      "avg_cost 0.8224265200441532\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012855\n",
      "avg_cost 0.8245185977762394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2905471\n",
      "avg_cost 0.8266009133512322\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2978022\n",
      "avg_cost 0.8286898244510996\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2742088\n",
      "avg_cost 0.8307572869821027\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025646\n",
      "avg_cost 0.8328505275466225\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125107\n",
      "avg_cost 0.8349528100273825\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973177\n",
      "avg_cost 0.8370412807031111\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001008\n",
      "avg_cost 0.8391322814334523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021429\n",
      "avg_cost 0.841225138577548\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.319182\n",
      "avg_cost 0.843333485776728\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074496\n",
      "avg_cost 0.8454311672123996\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3122618\n",
      "avg_cost 0.8475332234122537\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053415\n",
      "avg_cost 0.8496289883960377\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072937\n",
      "avg_cost 0.851726528081027\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290766\n",
      "avg_cost 0.8538090426271613\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3136487\n",
      "avg_cost 0.8559123596278104\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3164005\n",
      "avg_cost 0.8580181782895869\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997544\n",
      "avg_cost 0.8601088640906595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2838595\n",
      "avg_cost 0.8621850999918852\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962017\n",
      "avg_cost 0.8642725560881875\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2874\n",
      "avg_cost 0.8663520106402312\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045228\n",
      "avg_cost 0.8684470313245601\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2965631\n",
      "avg_cost 0.8705348160050134\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993493\n",
      "avg_cost 0.8726251335577533\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932749\n",
      "avg_cost 0.8747099289027129\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2796087\n",
      "avg_cost 0.8767823004722597\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084083\n",
      "avg_cost 0.8788808534362101\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2839324\n",
      "avg_cost 0.8809571556611496\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3100953\n",
      "avg_cost 0.8830572423067962\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049786\n",
      "avg_cost 0.8851526774059644\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2919812\n",
      "avg_cost 0.8872362966970965\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984948\n",
      "avg_cost 0.8893258374387568\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062437\n",
      "avg_cost 0.891422422582453\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292138\n",
      "avg_cost 0.8935061844912442\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.329375\n",
      "avg_cost 0.8956237981536171\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3131607\n",
      "avg_cost 0.897726671478965\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304215\n",
      "avg_cost 0.8998214123465798\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3094904\n",
      "avg_cost 0.9019209491122852\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036747\n",
      "avg_cost 0.9040151988376269\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084073\n",
      "avg_cost 0.9061137509346007\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30171\n",
      "avg_cost 0.9082062144712967\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295507\n",
      "avg_cost 0.9102930389751086\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297267\n",
      "avg_cost 0.9123814634843305\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002532\n",
      "avg_cost 0.9144726027141916\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3120275\n",
      "avg_cost 0.9165744458545336\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022044\n",
      "avg_cost 0.9186673589186233\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2904108\n",
      "avg_cost 0.920749550515955\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301831\n",
      "avg_cost 0.9228421241586857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962651\n",
      "avg_cost 0.9249296379089355\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2908418\n",
      "avg_cost 0.9270122213797135\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3217149\n",
      "avg_cost 0.9291228712688792\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083866\n",
      "avg_cost 0.9312214045091107\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3112268\n",
      "avg_cost 0.933322519822554\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293581\n",
      "avg_cost 0.9354075934670186\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310977\n",
      "avg_cost 0.9375084816325793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2917619\n",
      "avg_cost 0.9395919015190817\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041418\n",
      "avg_cost 0.9416865758462384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013344\n",
      "avg_cost 0.943778698010878\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005898\n",
      "avg_cost 0.9458701432834971\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3187478\n",
      "avg_cost 0.9479780957915565\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301735\n",
      "avg_cost 0.9500705820863895\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3295956\n",
      "avg_cost 0.952188396237113\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130264\n",
      "avg_cost 0.9542911475354973\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292785\n",
      "avg_cost 0.9563754974712023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032453\n",
      "avg_cost 0.9584693568403069\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058534\n",
      "avg_cost 0.9605655871738085\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.286882\n",
      "avg_cost 0.9626445707407862\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053575\n",
      "avg_cost 0.9647403502464292\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934372\n",
      "avg_cost 0.9668252931941638\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297452\n",
      "avg_cost 0.968913885896856\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014739\n",
      "avg_cost 0.9710061348568308\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041193\n",
      "avg_cost 0.9731007888100363\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046958\n",
      "avg_cost 0.9751959668506274\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032\n",
      "avg_cost 0.977289785038341\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3059325\n",
      "avg_cost 0.9793860873309047\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303539\n",
      "avg_cost 0.9814802137288179\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081193\n",
      "avg_cost 0.983578503998843\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049698\n",
      "avg_cost 0.9856739310784772\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289733\n",
      "avg_cost 0.9877555064721539\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3112812\n",
      "avg_cost 0.9898566712032664\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123252\n",
      "avg_cost 0.9919587850570678\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158622\n",
      "avg_cost 0.9940641143105247\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976022\n",
      "avg_cost 0.9961528435620395\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297475\n",
      "avg_cost 0.9982414572889154\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999144\n",
      "avg_cost 1.0003322885253212\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981443\n",
      "avg_cost 1.0024215106530623\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308514\n",
      "avg_cost 1.004520159851421\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301252\n",
      "avg_cost 1.0066122070225803\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314345\n",
      "avg_cost 1.0087161569161849\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2969744\n",
      "avg_cost 1.010804315480319\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300789\n",
      "avg_cost 1.0128959419510581\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290062\n",
      "avg_cost 1.0149778164516796\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297021\n",
      "avg_cost 1.0170660172809254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3209944\n",
      "avg_cost 1.019176012169231\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3331573\n",
      "avg_cost 1.0212970642610029\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299641\n",
      "avg_cost 1.023387646891854\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2911127\n",
      "avg_cost 1.0254704765840008\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3112075\n",
      "avg_cost 1.0275715743411669\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047836\n",
      "avg_cost 1.02966683214361\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2931426\n",
      "avg_cost 1.0317515071955592\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307868\n",
      "avg_cost 1.0338495690172367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291786\n",
      "avg_cost 1.0359330107948994\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994814\n",
      "avg_cost 1.0380234484239055\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043623\n",
      "avg_cost 1.040118323239413\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970552\n",
      "avg_cost 1.042206555279818\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053787\n",
      "avg_cost 1.0443023540756915\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030093\n",
      "avg_cost 1.0463959988680749\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296419\n",
      "avg_cost 1.0484836524183097\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007019\n",
      "avg_cost 1.0505751995606853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308334\n",
      "avg_cost 1.0526736851172012\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060555\n",
      "avg_cost 1.0547700992497528\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042557\n",
      "avg_cost 1.0568648771806195\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308139\n",
      "avg_cost 1.05896318544041\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309799\n",
      "avg_cost 1.061063002673062\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071597\n",
      "avg_cost 1.0631604205478318\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966654\n",
      "avg_cost 1.0652482982115308\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3057837\n",
      "avg_cost 1.067344465255737\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985232\n",
      "avg_cost 1.0694340317899527\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310432\n",
      "avg_cost 1.0715344244783571\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302301\n",
      "avg_cost 1.0736274253238327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972565\n",
      "avg_cost 1.0757158402963114\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30234\n",
      "avg_cost 1.0778088766878298\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297631\n",
      "avg_cost 1.0798976321653881\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064413\n",
      "avg_cost 1.0819943969899952\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305443\n",
      "avg_cost 1.0840902543067927\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017561\n",
      "avg_cost 1.0861827598918563\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304553\n",
      "avg_cost 1.088277808102694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972817\n",
      "avg_cost 1.090366246050054\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2901113\n",
      "avg_cost 1.0924481654167173\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.323698\n",
      "avg_cost 1.0945606181838294\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2935965\n",
      "avg_cost 1.0966457059166648\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973044\n",
      "avg_cost 1.0987341644547202\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058784\n",
      "avg_cost 1.1008304175463588\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3100543\n",
      "avg_cost 1.1029304669120092\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005779\n",
      "avg_cost 1.1050219013474203\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.32126\n",
      "avg_cost 1.1071321376887233\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963943\n",
      "avg_cost 1.1092197689143093\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3139846\n",
      "avg_cost 1.1113233913074838\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2969751\n",
      "avg_cost 1.1134115505218505\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974372\n",
      "avg_cost 1.1155001297864047\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3276854\n",
      "avg_cost 1.1176162073828957\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.315215\n",
      "avg_cost 1.1197209483926946\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043063\n",
      "avg_cost 1.1218157722733237\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024642\n",
      "avg_cost 1.123908921588551\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005\n",
      "avg_cost 1.1260002851486206\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036177\n",
      "avg_cost 1.1280944830721076\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298925\n",
      "avg_cost 1.1301844148202378\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3066528\n",
      "avg_cost 1.1322813718969174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3099139\n",
      "avg_cost 1.134381293600256\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064206\n",
      "avg_cost 1.1364780395681209\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073874\n",
      "avg_cost 1.138575664433566\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2953339\n",
      "avg_cost 1.1406623315811157\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285317\n",
      "avg_cost 1.1427398924394088\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297394\n",
      "avg_cost 1.1448284324732694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3096597\n",
      "avg_cost 1.1469281231273305\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980225\n",
      "avg_cost 1.1490172344988043\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3027222\n",
      "avg_cost 1.1511106183312156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079314\n",
      "avg_cost 1.1532087378068405\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983396\n",
      "avg_cost 1.1552981374480509\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303563\n",
      "avg_cost 1.1573922857371244\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029451\n",
      "avg_cost 1.1594858722253278\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3101637\n",
      "avg_cost 1.1615860210765492\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932637\n",
      "avg_cost 1.1636708062345331\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032632\n",
      "avg_cost 1.1657646818594498\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2915936\n",
      "avg_cost 1.1678479487245732\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297919\n",
      "avg_cost 1.1699369660290804\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075652\n",
      "avg_cost 1.1720347525856711\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302084\n",
      "avg_cost 1.1741275561939586\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2911117\n",
      "avg_cost 1.176210385019129\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081584\n",
      "avg_cost 1.1783087108351968\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039992\n",
      "avg_cost 1.1804032555493442\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040988\n",
      "avg_cost 1.1824978908625516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902956\n",
      "avg_cost 1.1845799777724526\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020196\n",
      "avg_cost 1.1866727228598162\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298593\n",
      "avg_cost 1.1887623529000717\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2941387\n",
      "avg_cost 1.1908479335091333\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2917573\n",
      "avg_cost 1.1929313492774967\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3168\n",
      "avg_cost 1.19503753120249\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.323348\n",
      "avg_cost 1.197149665789171\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064013\n",
      "avg_cost 1.1992463942007587\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.282809\n",
      "avg_cost 1.2013216751272031\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300778\n",
      "avg_cost 1.2034132914109668\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973657\n",
      "avg_cost 1.205501805652272\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957187\n",
      "avg_cost 1.2075888226249005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008738\n",
      "avg_cost 1.2096805260398176\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.321273\n",
      "avg_cost 1.2117907743020495\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107462\n",
      "avg_cost 1.2138914526592606\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985024\n",
      "avg_cost 1.2159810003367342\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943282\n",
      "avg_cost 1.2180667532574052\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292373\n",
      "avg_cost 1.2201507286591968\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013678\n",
      "avg_cost 1.2222428811680193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3162332\n",
      "avg_cost 1.2243485476753935\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2941701\n",
      "avg_cost 1.2264341568946844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980769\n",
      "avg_cost 1.2285233176838273\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304712\n",
      "avg_cost 1.2306185104630214\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078809\n",
      "avg_cost 1.2327165839888836\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2862334\n",
      "avg_cost 1.2347949780117384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311613\n",
      "avg_cost 1.2368964444507256\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2936242\n",
      "avg_cost 1.2389815573258838\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951677\n",
      "avg_cost 1.241068073402752\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095243\n",
      "avg_cost 1.2431676409461285\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.318196\n",
      "avg_cost 1.2452750919081952\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307358\n",
      "avg_cost 1.2473726901141085\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993019\n",
      "avg_cost 1.24946296453476\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300854\n",
      "avg_cost 1.2515546499599113\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902045\n",
      "avg_cost 1.2536366540735422\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3252637\n",
      "avg_cost 1.2557505301995715\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2910256\n",
      "avg_cost 1.2578332807800991\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3144102\n",
      "avg_cost 1.2599372900616044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053148\n",
      "avg_cost 1.2620330307700423\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305297\n",
      "avg_cost 1.2641287552226679\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293935\n",
      "avg_cost 1.2662141507322144\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2885\n",
      "avg_cost 1.2682946053418254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046553\n",
      "avg_cost 1.2703897465359089\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050296\n",
      "avg_cost 1.2724852280183279\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304281\n",
      "avg_cost 1.2745800289240756\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3142793\n",
      "avg_cost 1.2766839192130355\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971036\n",
      "avg_cost 1.2787721952525057\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299321\n",
      "avg_cost 1.2808624870126903\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296115\n",
      "avg_cost 1.2829498642141175\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299846\n",
      "avg_cost 1.2850406332449487\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3149831\n",
      "avg_cost 1.2871451633626771\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303621\n",
      "avg_cost 1.2892393643205824\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310742\n",
      "avg_cost 1.2913400387763985\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2941961\n",
      "avg_cost 1.2934256716208032\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045216\n",
      "avg_cost 1.2955206912214114\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2904181\n",
      "avg_cost 1.2976028895378122\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017995\n",
      "avg_cost 1.2996954345703133\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2884037\n",
      "avg_cost 1.3017758016152823\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964263\n",
      "avg_cost 1.3038634618845861\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311362\n",
      "avg_cost 1.3059647000919696\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.274757\n",
      "avg_cost 1.308032660917803\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302262\n",
      "avg_cost 1.31012562643398\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3072114\n",
      "avg_cost 1.312223091342233\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316208\n",
      "avg_cost 1.314328734874726\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301994\n",
      "avg_cost 1.316421456770464\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3151586\n",
      "avg_cost 1.3185261464118962\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312952\n",
      "avg_cost 1.3206288300861018\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2848327\n",
      "avg_cost 1.322705950737\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2788813\n",
      "avg_cost 1.324777661020106\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066506\n",
      "avg_cost 1.3268746161460883\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298568\n",
      "avg_cost 1.3289642234282066\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2858515\n",
      "avg_cost 1.3310422702269127\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961972\n",
      "avg_cost 1.333129722205076\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973673\n",
      "avg_cost 1.3352182379635904\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975829\n",
      "avg_cost 1.337306949658828\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314657\n",
      "avg_cost 1.3394111832705418\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011322\n",
      "avg_cost 1.3415031216361313\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2883759\n",
      "avg_cost 1.3435834633220334\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974195\n",
      "avg_cost 1.3456720265475195\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2896097\n",
      "avg_cost 1.347753489884464\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980502\n",
      "avg_cost 1.3498426263982608\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2903974\n",
      "avg_cost 1.3519248058579194\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3099248\n",
      "avg_cost 1.3540247375314896\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3156133\n",
      "avg_cost 1.3561298405040403\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3169456\n",
      "avg_cost 1.3582361546429729\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287671\n",
      "avg_cost 1.3603158556331296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306462\n",
      "avg_cost 1.3624126393144789\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2850783\n",
      "avg_cost 1.3644899832118649\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2820709\n",
      "avg_cost 1.3665645930983812\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29615\n",
      "avg_cost 1.3686520021612003\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046737\n",
      "avg_cost 1.3707471600445844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037956\n",
      "avg_cost 1.3728415196592167\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076303\n",
      "avg_cost 1.374939365386964\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3138645\n",
      "avg_cost 1.3770428785410804\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.324588\n",
      "avg_cost 1.3791561404141524\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3207889\n",
      "avg_cost 1.3812659484689898\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302755\n",
      "avg_cost 1.3833593622120957\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087523\n",
      "avg_cost 1.385458227937873\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3129754\n",
      "avg_cost 1.3875609328530065\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3259861\n",
      "avg_cost 1.389675465713849\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2862775\n",
      "avg_cost 1.3917538998343741\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158834\n",
      "avg_cost 1.3938592483780614\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970269\n",
      "avg_cost 1.3959474546259114\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3165746\n",
      "avg_cost 1.3980534315109268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299586\n",
      "avg_cost 1.4001439642906204\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069618\n",
      "avg_cost 1.402241202267735\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040376\n",
      "avg_cost 1.4043357818776927\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055902\n",
      "avg_cost 1.4064317729256386\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2921088\n",
      "avg_cost 1.408515508174898\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.28814\n",
      "avg_cost 1.410595635500823\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040376\n",
      "avg_cost 1.4126902151107805\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2917857\n",
      "avg_cost 1.414773656671699\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046174\n",
      "avg_cost 1.4168687634034607\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29465\n",
      "avg_cost 1.4189548089287514\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118684\n",
      "avg_cost 1.4210565075007369\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047857\n",
      "avg_cost 1.4231517672538774\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2880833\n",
      "avg_cost 1.4252318429946915\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2918646\n",
      "avg_cost 1.427315356297928\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064814\n",
      "avg_cost 1.4294121575355545\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086135\n",
      "avg_cost 1.4315108971162291\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3027427\n",
      "avg_cost 1.4336042995886384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043942\n",
      "avg_cost 1.4356992034478637\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081164\n",
      "avg_cost 1.4377974911169589\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3136542\n",
      "avg_cost 1.4399008131027238\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015172\n",
      "avg_cost 1.4419931015101362\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075686\n",
      "avg_cost 1.4440908911011452\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042443\n",
      "avg_cost 1.446185658628292\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3102798\n",
      "avg_cost 1.4482859130339205\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003662\n",
      "avg_cost 1.450377155000515\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018022\n",
      "avg_cost 1.452469702417202\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.276375\n",
      "avg_cost 1.4545391342856684\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2931776\n",
      "avg_cost 1.4566238411990096\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956228\n",
      "avg_cost 1.4587107710404845\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298346\n",
      "avg_cost 1.4608001765337872\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014975\n",
      "avg_cost 1.4628924469514342\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046\n",
      "avg_cost 1.4649875378608719\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095899\n",
      "avg_cost 1.4670871650088932\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.283803\n",
      "avg_cost 1.4691633495417524\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971728\n",
      "avg_cost 1.47125168843703\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307386\n",
      "avg_cost 1.4733493120020102\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2940166\n",
      "avg_cost 1.4754347816380604\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2925994\n",
      "avg_cost 1.4775189629468068\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308365\n",
      "avg_cost 1.4796174766800638\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038154\n",
      "avg_cost 1.4817118542844616\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021922\n",
      "avg_cost 1.483804756294599\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002083\n",
      "avg_cost 1.4858958547765577\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108594\n",
      "avg_cost 1.487996636087246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.29621\n",
      "avg_cost 1.490084099769594\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290846\n",
      "avg_cost 1.4921666871417671\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2843695\n",
      "avg_cost 1.4942433866587572\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024797\n",
      "avg_cost 1.4963365500623549\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981875\n",
      "avg_cost 1.4984258114207893\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307026\n",
      "avg_cost 1.5005231077020837\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076942\n",
      "avg_cost 1.5026210115172665\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2941935\n",
      "avg_cost 1.5047066419774855\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2874634\n",
      "avg_cost 1.5067861541834764\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003929\n",
      "avg_cost 1.508877420425417\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981246\n",
      "avg_cost 1.5109666245633924\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3129344\n",
      "avg_cost 1.5130692921985298\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2759092\n",
      "avg_cost 1.515138300548902\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304806\n",
      "avg_cost 1.5172335787252964\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2908347\n",
      "avg_cost 1.5193161556937496\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084176\n",
      "avg_cost 1.5214147171107224\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072631\n",
      "avg_cost 1.5235122290524588\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010912\n",
      "avg_cost 1.5256041301380523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292444\n",
      "avg_cost 1.5276881701296046\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064246\n",
      "avg_cost 1.5297849197821203\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158164\n",
      "avg_cost 1.531890207420698\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974775\n",
      "avg_cost 1.5339788233150156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2893434\n",
      "avg_cost 1.5360600445487302\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971168\n",
      "avg_cost 1.5381483325091294\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085356\n",
      "avg_cost 1.5402470012144627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3295536\n",
      "avg_cost 1.5423647772182136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3212519\n",
      "avg_cost 1.544475006190215\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2978897\n",
      "avg_cost 1.5465639968351903\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.32088\n",
      "avg_cost 1.5486738876862978\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092268\n",
      "avg_cost 1.550773184732959\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067493\n",
      "avg_cost 1.5528702295910246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981036\n",
      "avg_cost 1.554959414655514\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982516\n",
      "avg_cost 1.5570487343181283\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3210936\n",
      "avg_cost 1.5591588193720056\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125753\n",
      "avg_cost 1.5612611605904338\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934232\n",
      "avg_cost 1.5633460907502628\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312874\n",
      "avg_cost 1.565448703549127\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3257084\n",
      "avg_cost 1.5675629839030198\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3114448\n",
      "avg_cost 1.5696642973206278\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074322\n",
      "avg_cost 1.5717619629339756\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074255\n",
      "avg_cost 1.5738596224784869\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934387\n",
      "avg_cost 1.5759445667266863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2905478\n",
      "avg_cost 1.5780268829519115\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072405\n",
      "avg_cost 1.5801243743029525\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3192794\n",
      "avg_cost 1.582232810150495\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297765\n",
      "avg_cost 1.584321687438273\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044815\n",
      "avg_cost 1.5864166706258618\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3134143\n",
      "avg_cost 1.588519774566999\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108344\n",
      "avg_cost 1.5906205331195502\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039243\n",
      "avg_cost 1.5927150097760305\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2892962\n",
      "avg_cost 1.594796188094401\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305987\n",
      "avg_cost 1.5968925398046339\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2868168\n",
      "avg_cost 1.598971464200455\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081412\n",
      "avg_cost 1.6010697744109432\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2845974\n",
      "avg_cost 1.6031466811353527\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2904193\n",
      "avg_cost 1.6052288805354742\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.318972\n",
      "avg_cost 1.6073370369997908\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303313\n",
      "avg_cost 1.6094309579242376\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3137658\n",
      "avg_cost 1.6115343813462708\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306681\n",
      "avg_cost 1.6136313639987616\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2883651\n",
      "avg_cost 1.6157116959311764\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2869573\n",
      "avg_cost 1.6177907479893097\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313831\n",
      "avg_cost 1.6198942307992434\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971842\n",
      "avg_cost 1.6219825800982408\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158982\n",
      "avg_cost 1.6240879420800662\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3120687\n",
      "avg_cost 1.6261898227171485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979057\n",
      "avg_cost 1.6282788278839826\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002386\n",
      "avg_cost 1.6303699538924499\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293381\n",
      "avg_cost 1.632454845688562\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2929547\n",
      "avg_cost 1.634539349946111\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031678\n",
      "avg_cost 1.6366331388733626\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297021\n",
      "avg_cost 1.6387213397026084\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066719\n",
      "avg_cost 1.640818314118821\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3142462\n",
      "avg_cost 1.6429221742803424\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312695\n",
      "avg_cost 1.6450246243043403\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2935328\n",
      "avg_cost 1.647109654166484\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.286574\n",
      "avg_cost 1.6491883577000035\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055005\n",
      "avg_cost 1.6512842672521442\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073874\n",
      "avg_cost 1.6533818921175893\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019114\n",
      "avg_cost 1.655474538803103\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044958\n",
      "avg_cost 1.6575695349953414\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2838337\n",
      "avg_cost 1.6596457474881976\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3152823\n",
      "avg_cost 1.6617505496198504\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095362\n",
      "avg_cost 1.663850128000435\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952082\n",
      "avg_cost 1.665936680923811\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301617\n",
      "avg_cost 1.6680290599302834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3035314\n",
      "avg_cost 1.6701231793923834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2883904\n",
      "avg_cost 1.6722035342996793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30666\n",
      "avg_cost 1.6743004978786837\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297136\n",
      "avg_cost 1.6763888033953602\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001513\n",
      "avg_cost 1.6784798500754639\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995079\n",
      "avg_cost 1.6805703117630721\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300748\n",
      "avg_cost 1.6826619009538153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3094425\n",
      "avg_cost 1.684761394153944\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024292\n",
      "avg_cost 1.6868545116077793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291586\n",
      "avg_cost 1.6889377715370895\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090754\n",
      "avg_cost 1.6910369309512074\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980635\n",
      "avg_cost 1.6931260796026772\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988558\n",
      "avg_cost 1.6952159484950002\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3124087\n",
      "avg_cost 1.6973181382092586\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997057\n",
      "avg_cost 1.6994087797945219\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972665\n",
      "avg_cost 1.7014972038702554\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2944367\n",
      "avg_cost 1.7035830554095204\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2873466\n",
      "avg_cost 1.7056624614108715\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093486\n",
      "avg_cost 1.7077618692138001\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973368\n",
      "avg_cost 1.7098503572290618\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960923\n",
      "avg_cost 1.7119377138397933\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071766\n",
      "avg_cost 1.7140351471033985\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123472\n",
      "avg_cost 1.716137280897663\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301116\n",
      "avg_cost 1.7182292045246494\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3122523\n",
      "avg_cost 1.720331252054737\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961473\n",
      "avg_cost 1.72241865873337\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2856762\n",
      "avg_cost 1.7244965462251165\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038533\n",
      "avg_cost 1.7265909582918362\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2878785\n",
      "avg_cost 1.7286708478494146\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054214\n",
      "avg_cost 1.7307666854424932\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887926\n",
      "avg_cost 1.732847405997192\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300206\n",
      "avg_cost 1.7349385023117088\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308779\n",
      "avg_cost 1.7370373923128324\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018827\n",
      "avg_cost 1.7391300129890463\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310663\n",
      "avg_cost 1.7412306157025446\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303211\n",
      "avg_cost 1.7433244438604896\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946105\n",
      "avg_cost 1.7454104534062493\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308481\n",
      "avg_cost 1.7475090724771694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311983\n",
      "avg_cost 1.749610875303097\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.284986\n",
      "avg_cost 1.751688135320492\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013108\n",
      "avg_cost 1.7537802360274595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.315606\n",
      "avg_cost 1.7558853324976853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062985\n",
      "avg_cost 1.757981967492539\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981334\n",
      "avg_cost 1.7600711796500486\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2855058\n",
      "avg_cost 1.7621489121697185\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3140743\n",
      "avg_cost 1.7642526160586982\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042512\n",
      "avg_cost 1.7663473898714257\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024833\n",
      "avg_cost 1.7684405565261858\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089325\n",
      "avg_cost 1.7705395861105502\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294005\n",
      "avg_cost 1.7726250451261363\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3097062\n",
      "avg_cost 1.774724778045309\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3167784\n",
      "avg_cost 1.7768309402465836\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3167508\n",
      "avg_cost 1.7789370773055353\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970936\n",
      "avg_cost 1.7810253442417507\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3201704\n",
      "avg_cost 1.7831345900622297\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29701\n",
      "avg_cost 1.785222780921244\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920005\n",
      "avg_cost 1.7873064177686533\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298788\n",
      "avg_cost 1.789396225105634\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3214588\n",
      "avg_cost 1.7915066422115686\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296616\n",
      "avg_cost 1.793594475009226\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.283876\n",
      "avg_cost 1.7956707258657991\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988076\n",
      "avg_cost 1.7977605509758012\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2922459\n",
      "avg_cost 1.799844410852954\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3127973\n",
      "avg_cost 1.8019469538601978\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056946\n",
      "avg_cost 1.804043039842087\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3171897\n",
      "avg_cost 1.8061495759270425\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030603\n",
      "avg_cost 1.8082432671026767\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30407\n",
      "avg_cost 1.8103378761898405\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964337\n",
      "avg_cost 1.8124255431782132\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3187132\n",
      "avg_cost 1.814533464258369\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135338\n",
      "avg_cost 1.8166366767883317\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290843\n",
      "avg_cost 1.8187192613428307\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317995\n",
      "avg_cost 1.820826529589568\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309866\n",
      "avg_cost 1.82292640772733\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126144\n",
      "avg_cost 1.8250287844918007\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3224046\n",
      "avg_cost 1.8271400614218294\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3133223\n",
      "avg_cost 1.8292430816997196\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2865164\n",
      "avg_cost 1.8313217329978957\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038523\n",
      "avg_cost 1.8334161441976389\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302549\n",
      "avg_cost 1.8355093704570438\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008518\n",
      "avg_cost 1.8376010539314978\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014877\n",
      "avg_cost 1.839693315462634\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946253\n",
      "avg_cost 1.8417793384465317\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2899075\n",
      "avg_cost 1.8438610724969358\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115146\n",
      "avg_cost 1.8459624494205835\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039072\n",
      "avg_cost 1.8480569104714841\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991579\n",
      "avg_cost 1.8501470539786613\n",
      "---------------------\n",
      "batch_x (50, 784)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_y (50, 10)\n",
      "c 2.2909348\n",
      "avg_cost 1.8522297219796628\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952957\n",
      "avg_cost 1.8543163544481465\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299941\n",
      "avg_cost 1.8564072099598985\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2933025\n",
      "avg_cost 1.858492030447181\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2998538\n",
      "avg_cost 1.8605828066305694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292535\n",
      "avg_cost 1.8626669294183917\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956383\n",
      "avg_cost 1.8647538733482374\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053112\n",
      "avg_cost 1.8668496108055128\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024917\n",
      "avg_cost 1.8689427850463187\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036532\n",
      "avg_cost 1.8710370152646858\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983172\n",
      "avg_cost 1.873126394531945\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307482\n",
      "avg_cost 1.8752241054448229\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3000143\n",
      "avg_cost 1.877315027497033\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299114\n",
      "avg_cost 1.8794051311232842\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972708\n",
      "avg_cost 1.8814935591004127\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995856\n",
      "avg_cost 1.883584091446618\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053777\n",
      "avg_cost 1.885679889375515\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3099318\n",
      "avg_cost 1.8877798273346658\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303151\n",
      "avg_cost 1.8898736008730819\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3070178\n",
      "avg_cost 1.8919708897850747\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090088\n",
      "avg_cost 1.8940699887275712\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2877426\n",
      "avg_cost 1.8961497547409767\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3155942\n",
      "avg_cost 1.8982548403739945\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313646\n",
      "avg_cost 1.9003581549904578\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997823\n",
      "avg_cost 1.9024488661505974\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135302\n",
      "avg_cost 1.9045520754293976\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309546\n",
      "avg_cost 1.906651662696493\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3061476\n",
      "avg_cost 1.9087481604922916\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3091257\n",
      "avg_cost 1.9108473656394278\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3176022\n",
      "avg_cost 1.9129542766917849\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2766562\n",
      "avg_cost 1.9150239641016193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973685\n",
      "avg_cost 1.9171124809438547\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996044\n",
      "avg_cost 1.919203030412849\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954574\n",
      "avg_cost 1.9212898098338749\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298954\n",
      "avg_cost 1.9233797680247928\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103423\n",
      "avg_cost 1.925480079217392\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2863069\n",
      "avg_cost 1.927558539997449\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024795\n",
      "avg_cost 1.9296517031843026\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078032\n",
      "avg_cost 1.9317497060515678\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074563\n",
      "avg_cost 1.933847393556076\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.319298\n",
      "avg_cost 1.9359558463096633\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308232\n",
      "avg_cost 1.9380542390996773\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3196223\n",
      "avg_cost 1.940162986625326\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081205\n",
      "avg_cost 1.942261277979072\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002589\n",
      "avg_cost 1.944352422410793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017492\n",
      "avg_cost 1.946444921710276\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2803237\n",
      "avg_cost 1.948517943295567\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050926\n",
      "avg_cost 1.950613481998445\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3150587\n",
      "avg_cost 1.9527180808240732\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993784\n",
      "avg_cost 1.954808424819601\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966883\n",
      "avg_cost 1.9568963232907397\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037472\n",
      "avg_cost 1.958990638906307\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964897\n",
      "avg_cost 1.961078356829558\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3116915\n",
      "avg_cost 1.9631798945773746\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30629\n",
      "avg_cost 1.9652765217694383\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046591\n",
      "avg_cost 1.9673716664314285\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066432\n",
      "avg_cost 1.9694686148383416\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041048\n",
      "avg_cost 1.971563255570153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946622\n",
      "avg_cost 1.973649312149396\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045897\n",
      "avg_cost 1.9757443937388348\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085632\n",
      "avg_cost 1.977843087586491\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297727\n",
      "avg_cost 1.9799319304119471\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949793\n",
      "avg_cost 1.9820182752609268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971418\n",
      "avg_cost 1.984106585979463\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301087\n",
      "avg_cost 1.9861984831636617\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.286414\n",
      "avg_cost 1.9882770412618478\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081298\n",
      "avg_cost 1.990375341068616\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988403\n",
      "avg_cost 1.9924651958725683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306421\n",
      "avg_cost 1.9945619422739216\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992032\n",
      "avg_cost 1.9966521269624897\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994223\n",
      "avg_cost 1.9987425108389434\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062005\n",
      "avg_cost 2.0008390567519463\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053515\n",
      "avg_cost 2.0029348308389854\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301651\n",
      "avg_cost 2.005027240839873\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985353\n",
      "avg_cost 2.0071168184280412\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316134\n",
      "avg_cost 2.0092223947698438\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296563\n",
      "avg_cost 2.011310179233553\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2873952\n",
      "avg_cost 2.013389629450713\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115346\n",
      "avg_cost 2.0154910245808706\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046088\n",
      "avg_cost 2.0175861235098425\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054442\n",
      "avg_cost 2.019681981910361\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923563\n",
      "avg_cost 2.0217659421400613\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983427\n",
      "avg_cost 2.023855344598946\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2746568\n",
      "avg_cost 2.025923214392231\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952824\n",
      "avg_cost 2.0280098347230417\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307044\n",
      "avg_cost 2.0301071474768926\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3011558\n",
      "avg_cost 2.0321991073001544\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3308551\n",
      "avg_cost 2.03431806651029\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038468\n",
      "avg_cost 2.0364124727249173\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3164463\n",
      "avg_cost 2.0385183330015733\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2855015\n",
      "avg_cost 2.040596061619848\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2899287\n",
      "avg_cost 2.0426778149604825\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308371\n",
      "avg_cost 2.0447763341123437\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306546\n",
      "avg_cost 2.0468731940876386\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3109255\n",
      "avg_cost 2.04897403543646\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3225214\n",
      "avg_cost 2.0510854185711285\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2953148\n",
      "avg_cost 2.053172068379145\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299435\n",
      "avg_cost 2.0552624637430394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999418\n",
      "avg_cost 2.057353319905024\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964392\n",
      "avg_cost 2.059440991878512\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3129294\n",
      "avg_cost 2.0615436549620223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032923\n",
      "avg_cost 2.0636375570297267\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902606\n",
      "avg_cost 2.0657196120782357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012319\n",
      "avg_cost 2.0678116410428853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29848\n",
      "avg_cost 2.0699011683464077\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3170407\n",
      "avg_cost 2.0720075689662614\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2915452\n",
      "avg_cost 2.0740907918323197\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982547\n",
      "avg_cost 2.076180114312608\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3131337\n",
      "avg_cost 2.0782829631458655\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287934\n",
      "avg_cost 2.0803629032048336\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130846\n",
      "avg_cost 2.0824657073887938\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067532\n",
      "avg_cost 2.084562755714766\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3102283\n",
      "avg_cost 2.086662963303655\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946436\n",
      "avg_cost 2.0887490029768534\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018599\n",
      "avg_cost 2.090841602845628\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3162072\n",
      "avg_cost 2.0929472457278884\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2925153\n",
      "avg_cost 2.0950313505259452\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975543\n",
      "avg_cost 2.097120036211883\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973037\n",
      "avg_cost 2.099208494099706\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014817\n",
      "avg_cost 2.1013007502122383\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951438\n",
      "avg_cost 2.1033872446146904\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016233\n",
      "avg_cost 2.105479629473255\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305045\n",
      "avg_cost 2.1075751248273007\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3119535\n",
      "avg_cost 2.109676900776952\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2819393\n",
      "avg_cost 2.1117513910206904\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050125\n",
      "avg_cost 2.11384685689753\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970772\n",
      "avg_cost 2.115935108878398\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30401\n",
      "avg_cost 2.1180296633460327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3096323\n",
      "avg_cost 2.120129329074515\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3129466\n",
      "avg_cost 2.1222320077636048\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012507\n",
      "avg_cost 2.1243240538510433\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038657\n",
      "avg_cost 2.1264184771884596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036137\n",
      "avg_cost 2.1285126714272957\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951963\n",
      "avg_cost 2.1305992135134635\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003929\n",
      "avg_cost 2.132690479755404\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125222\n",
      "avg_cost 2.134792772639884\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294346\n",
      "avg_cost 2.136878541816367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956944\n",
      "avg_cost 2.138965536681091\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294326\n",
      "avg_cost 2.1410512876510643\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2876914\n",
      "avg_cost 2.1431310070644747\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971158\n",
      "avg_cost 2.145219294157897\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995577\n",
      "avg_cost 2.1473098011450356\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2941153\n",
      "avg_cost 2.1493953605131693\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306313\n",
      "avg_cost 2.151492008729417\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317147\n",
      "avg_cost 2.153598506017167\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964277\n",
      "avg_cost 2.1556861675869357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3140707\n",
      "avg_cost 2.157789868224753\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990687\n",
      "avg_cost 2.1598799306696135\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051262\n",
      "avg_cost 2.1619754999334186\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3119025\n",
      "avg_cost 2.1640772294998194\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064404\n",
      "avg_cost 2.1661739934574498\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2915325\n",
      "avg_cost 2.1682572048360673\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30272\n",
      "avg_cost 2.170350586717781\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3120532\n",
      "avg_cost 2.1724524532664926\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987366\n",
      "avg_cost 2.1745422137867343\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2884946\n",
      "avg_cost 2.1766226634112296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961597\n",
      "avg_cost 2.1787100813605593\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980447\n",
      "avg_cost 2.1807992128892404\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103685\n",
      "avg_cost 2.1828995479236974\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309974\n",
      "avg_cost 2.184999524246565\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077598\n",
      "avg_cost 2.1870974876663927\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288835\n",
      "avg_cost 2.189178246801552\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2908964\n",
      "avg_cost 2.1912608799067437\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991111\n",
      "avg_cost 2.193350980932065\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050425\n",
      "avg_cost 2.195446474118669\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310085\n",
      "avg_cost 2.1975465514443164\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291712\n",
      "avg_cost 2.199629926031289\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055227\n",
      "avg_cost 2.2017258557406367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040812\n",
      "avg_cost 2.2038204750147763\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2925794\n",
      "avg_cost 2.205904638117013\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2936163\n",
      "avg_cost 2.2079897438396134\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989285\n",
      "avg_cost 2.210079678838906\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.29367\n",
      "avg_cost 2.212164833328943\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317829\n",
      "avg_cost 2.2142719505049993\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125696\n",
      "avg_cost 2.2163742865215674\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29483\n",
      "avg_cost 2.218460495688701\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125734\n",
      "avg_cost 2.220562835173176\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141415\n",
      "avg_cost 2.22266660018401\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3206806\n",
      "avg_cost 2.2247763098369973\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3068225\n",
      "avg_cost 2.2268734212355206\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3254633\n",
      "avg_cost 2.228987478776414\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031623\n",
      "avg_cost 2.23108126271855\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016555\n",
      "avg_cost 2.233173676837577\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3178272\n",
      "avg_cost 2.2352807924964235\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307618\n",
      "avg_cost 2.237378626953474\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2855139\n",
      "avg_cost 2.2394563668424454\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3101847\n",
      "avg_cost 2.241556534767153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307071\n",
      "avg_cost 2.243653872013094\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293191\n",
      "avg_cost 2.245738591064108\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2930665\n",
      "avg_cost 2.2478231969746694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293943\n",
      "avg_cost 2.249908599636773\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2947721\n",
      "avg_cost 2.2519947561350753\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037946\n",
      "avg_cost 2.254089114882731\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291668\n",
      "avg_cost 2.256172449372033\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087263\n",
      "avg_cost 2.258271291472697\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287137\n",
      "avg_cost 2.2603505069559287\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031206\n",
      "avg_cost 2.262444252967836\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291074\n",
      "avg_cost 2.2645270475474284\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3116097\n",
      "avg_cost 2.266628510951997\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989047\n",
      "avg_cost 2.2687184242768734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984226\n",
      "avg_cost 2.2708078993450527\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2814248\n",
      "avg_cost 2.2728819218548875\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305508\n",
      "avg_cost 2.274977838126097\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294488\n",
      "avg_cost 2.277063736265357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3194065\n",
      "avg_cost 2.2791722876375387\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290663\n",
      "avg_cost 2.2812547085501946\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089805\n",
      "avg_cost 2.2833537817001357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296854\n",
      "avg_cost 2.2854418308084674\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2965577\n",
      "avg_cost 2.287529610503805\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3184583\n",
      "avg_cost 2.2896372998844505\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023694\n",
      "avg_cost 2.291730362935501\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067224\n",
      "avg_cost 2.293827383301476\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3065581\n",
      "avg_cost 2.295924254330723\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297214\n",
      "avg_cost 2.2980126307227406\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3140488\n",
      "avg_cost 2.300116311420095\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009787\n",
      "avg_cost 2.302208110202444\n",
      "---------------------\n",
      "Epoch: 2 cost = 2.302 test accuracy: 0.113\n",
      "====================================\n",
      "epoch 2\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288938\n",
      "avg_cost 0.0020808527686379174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2900205\n",
      "avg_cost 0.004162689555775036\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976637\n",
      "avg_cost 0.006251474727283825\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2911336\n",
      "avg_cost 0.008334323492917147\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310708\n",
      "avg_cost 0.010434967171062123\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302596\n",
      "avg_cost 0.012528236345811324\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982938\n",
      "avg_cost 0.014617594372142445\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293615\n",
      "avg_cost 0.016702699011022393\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946649\n",
      "avg_cost 0.018788757974451238\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2922797\n",
      "avg_cost 0.020872648629275237\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076932\n",
      "avg_cost 0.022970551577481357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997472\n",
      "avg_cost 0.025061230876229027\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984202\n",
      "avg_cost 0.027150703776966444\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996578\n",
      "avg_cost 0.029241301796653057\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301643\n",
      "avg_cost 0.0313337044282393\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296403\n",
      "avg_cost 0.0334213434566151\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950683\n",
      "avg_cost 0.035507769151167434\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966912\n",
      "avg_cost 0.03759567022323608\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990053\n",
      "avg_cost 0.039685675014149054\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002617\n",
      "avg_cost 0.04177682204680009\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.276612\n",
      "avg_cost 0.0438464693589644\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906432\n",
      "avg_cost 0.0459288722818548\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2863028\n",
      "avg_cost 0.04800732937726107\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970953\n",
      "avg_cost 0.0500955978306857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3165267\n",
      "avg_cost 0.05220153115012428\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141775\n",
      "avg_cost 0.05430532888932661\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995293\n",
      "avg_cost 0.05639581008390947\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3104217\n",
      "avg_cost 0.058496193452314896\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3168297\n",
      "avg_cost 0.060602402253584424\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3157883\n",
      "avg_cost 0.06270766431635076\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071206\n",
      "avg_cost 0.06480504664507779\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304152\n",
      "avg_cost 0.06689973029223355\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023703\n",
      "avg_cost 0.06899279421026056\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3241115\n",
      "avg_cost 0.07110562281175092\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3271017\n",
      "avg_cost 0.07322116981853137\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089356\n",
      "avg_cost 0.07532020222056994\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037508\n",
      "avg_cost 0.07741452108729968\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938697\n",
      "avg_cost 0.07949985720894552\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887895\n",
      "avg_cost 0.08158057494597\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067472\n",
      "avg_cost 0.08367761785333805\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.329212\n",
      "avg_cost 0.08579508326270362\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2986064\n",
      "avg_cost 0.0878847254406322\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047118\n",
      "avg_cost 0.08997991800308226\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012908\n",
      "avg_cost 0.09207200050354003\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975016\n",
      "avg_cost 0.0941606382890181\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30132\n",
      "avg_cost 0.0962527474490079\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052397\n",
      "avg_cost 0.09834841988303444\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3146179\n",
      "avg_cost 0.1004526179487055\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979896\n",
      "avg_cost 0.10254169940948486\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973325\n",
      "avg_cost 0.10463018352335149\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292938\n",
      "avg_cost 0.10671467260880903\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051527\n",
      "avg_cost 0.10881026593121615\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3104823\n",
      "avg_cost 0.11091070435263893\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992606\n",
      "avg_cost 0.11300094127655029\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294494\n",
      "avg_cost 0.11508684483441439\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3147128\n",
      "avg_cost 0.11719112916426225\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047655\n",
      "avg_cost 0.11928637049414895\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2882998\n",
      "avg_cost 0.121366643038663\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034031\n",
      "avg_cost 0.12346064589240334\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001902\n",
      "avg_cost 0.12555172790180552\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299897\n",
      "avg_cost 0.12764254331588745\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298787\n",
      "avg_cost 0.12973234978589146\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126068\n",
      "avg_cost 0.13183471961454912\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950222\n",
      "avg_cost 0.13392110347747804\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974465\n",
      "avg_cost 0.13600969119505452\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313161\n",
      "avg_cost 0.1381125647371466\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031588\n",
      "avg_cost 0.14020634542812005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.28577\n",
      "avg_cost 0.1422843181003224\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043566\n",
      "avg_cost 0.14437918771396988\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295056\n",
      "avg_cost 0.1464656023545699\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067374\n",
      "avg_cost 0.14856263637542727\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3146677\n",
      "avg_cost 0.15066687974062834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050208\n",
      "avg_cost 0.15276235320351342\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2930355\n",
      "avg_cost 0.15484693093733357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301553\n",
      "avg_cost 0.15693925185637042\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29803\n",
      "avg_cost 0.1590283699469133\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932596\n",
      "avg_cost 0.1611131514202465\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305716\n",
      "avg_cost 0.16320925690911034\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052046\n",
      "avg_cost 0.16530489748174496\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093133\n",
      "avg_cost 0.16740427320653745\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3096375\n",
      "avg_cost 0.16950394370339136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3201969\n",
      "avg_cost 0.1716132135824724\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118427\n",
      "avg_cost 0.17371488874608823\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991827\n",
      "avg_cost 0.17580505479465836\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2891953\n",
      "avg_cost 0.17788614143024795\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2953935\n",
      "avg_cost 0.17997286276383836\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2888985\n",
      "avg_cost 0.18205367955294524\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975776\n",
      "avg_cost 0.18414238647981127\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051636\n",
      "avg_cost 0.18623798977244987\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2901173\n",
      "avg_cost 0.1883199145577171\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294858\n",
      "avg_cost 0.19040614908391784\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308329\n",
      "avg_cost 0.1925046300888062\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3205645\n",
      "avg_cost 0.1946142341873863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314139\n",
      "avg_cost 0.19671799681403423\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3134065\n",
      "avg_cost 0.198821093602614\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036034\n",
      "avg_cost 0.2009152785214511\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031359\n",
      "avg_cost 0.20300903840498494\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999504\n",
      "avg_cost 0.20509990236975933\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2933095\n",
      "avg_cost 0.20718472914262254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006687\n",
      "avg_cost 0.2092762461575595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3146822\n",
      "avg_cost 0.2113805027441545\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026693\n",
      "avg_cost 0.2134738384593617\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971368\n",
      "avg_cost 0.21556214462627066\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3160293\n",
      "avg_cost 0.2176676258173856\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076894\n",
      "avg_cost 0.21976552529768512\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3178072\n",
      "avg_cost 0.2218726227500222\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050263\n",
      "avg_cost 0.22396810119802302\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920196\n",
      "avg_cost 0.22605175538496539\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3168914\n",
      "avg_cost 0.22815802032297308\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292272\n",
      "avg_cost 0.23024190404198386\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2871966\n",
      "avg_cost 0.23232117371125655\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304255\n",
      "avg_cost 0.23441595099189064\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996361\n",
      "avg_cost 0.23650652928785842\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2912323\n",
      "avg_cost 0.23858946778557516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052616\n",
      "avg_cost 0.2406851601600647\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305553\n",
      "avg_cost 0.24278111739592118\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298003\n",
      "avg_cost 0.24487021099437364\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311483\n",
      "avg_cost 0.2469715590910478\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3182983\n",
      "avg_cost 0.2490791030363603\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2969394\n",
      "avg_cost 0.25116722973910244\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037214\n",
      "avg_cost 0.25326152194630014\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035586\n",
      "avg_cost 0.25535566611723465\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126266\n",
      "avg_cost 0.25745805393565785\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951782\n",
      "avg_cost 0.25954457954926924\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943728\n",
      "avg_cost 0.26163037300109865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010495\n",
      "avg_cost 0.2637222361564636\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032117\n",
      "avg_cost 0.2658160649646412\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2802198\n",
      "avg_cost 0.2678889920494773\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2786438\n",
      "avg_cost 0.26996048645539716\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073313\n",
      "avg_cost 0.27205806038596414\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309058\n",
      "avg_cost 0.2741572039777582\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003042\n",
      "avg_cost 0.2762483895908702\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3119307\n",
      "avg_cost 0.2783501447330821\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297472\n",
      "avg_cost 0.280438755642284\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999465\n",
      "avg_cost 0.2825296161391518\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887986\n",
      "avg_cost 0.28461034211245445\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051934\n",
      "avg_cost 0.2867059724981134\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305578\n",
      "avg_cost 0.288801952492107\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052208\n",
      "avg_cost 0.29089760780334467\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3096583\n",
      "avg_cost 0.29299729715694073\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2917254\n",
      "avg_cost 0.29508068388158615\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2899232\n",
      "avg_cost 0.2971624322371048\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3197908\n",
      "avg_cost 0.29927133300087655\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923882\n",
      "avg_cost 0.3013553222742946\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958956\n",
      "avg_cost 0.30344250007109197\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960558\n",
      "avg_cost 0.3055298235199667\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962077\n",
      "avg_cost 0.3076172850348732\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025331\n",
      "avg_cost 0.3097104969891634\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954056\n",
      "avg_cost 0.3117972293767061\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3061302\n",
      "avg_cost 0.3138937113501808\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060777\n",
      "avg_cost 0.31599014563993966\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984219\n",
      "avg_cost 0.3180796200578862\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972453\n",
      "avg_cost 0.32016802484338924\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294133\n",
      "avg_cost 0.32225360025059085\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2889817\n",
      "avg_cost 0.3243344926834106\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035262\n",
      "avg_cost 0.3264286073771389\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305796\n",
      "avg_cost 0.32852478547529734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090212\n",
      "avg_cost 0.3306238956884904\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952645\n",
      "avg_cost 0.3327104997634887\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3102732\n",
      "avg_cost 0.3348107481002807\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3028681\n",
      "avg_cost 0.3369042645801197\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976694\n",
      "avg_cost 0.33899305495348836\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2939734\n",
      "avg_cost 0.34107848535884505\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002357\n",
      "avg_cost 0.34316960876638236\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299204\n",
      "avg_cost 0.3452597943219271\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966704\n",
      "avg_cost 0.34734767653725357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291851\n",
      "avg_cost 0.34943117748607283\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3215013\n",
      "avg_cost 0.35154163317246867\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2653573\n",
      "avg_cost 0.3536010488596829\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299447\n",
      "avg_cost 0.3556914552775296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976203\n",
      "avg_cost 0.35778020100160074\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069346\n",
      "avg_cost 0.3598774142698808\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3164706\n",
      "avg_cost 0.3619832966544411\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.327949\n",
      "avg_cost 0.36409961396997625\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297095\n",
      "avg_cost 0.3661878822066567\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008504\n",
      "avg_cost 0.36827956438064574\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078432\n",
      "avg_cost 0.37037760366093025\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3109534\n",
      "avg_cost 0.37247847036881876\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30446\n",
      "avg_cost 0.3745734340494329\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303841\n",
      "avg_cost 0.3766678350622003\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285386\n",
      "avg_cost 0.37874545877630056\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299629\n",
      "avg_cost 0.3808360305699435\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2834556\n",
      "avg_cost 0.38291189930655734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2882507\n",
      "avg_cost 0.38499212720177384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3091645\n",
      "avg_cost 0.38709136767820873\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029344\n",
      "avg_cost 0.38918494441292495\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985797\n",
      "avg_cost 0.3912745623155073\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317357\n",
      "avg_cost 0.39338125055486495\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996414\n",
      "avg_cost 0.39547183361920435\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307047\n",
      "avg_cost 0.39756914897398504\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3231287\n",
      "avg_cost 0.39968108415603626\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290545\n",
      "avg_cost 0.4017633977803316\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3139198\n",
      "avg_cost 0.40386696121909393\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2913902\n",
      "avg_cost 0.40595004320144645\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2760627\n",
      "avg_cost 0.40801919113505963\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115716\n",
      "avg_cost 0.41012061986056236\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299899\n",
      "avg_cost 0.41221143722534176\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.332919\n",
      "avg_cost 0.41433227257295085\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2829561\n",
      "avg_cost 0.4164076872305436\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2870393\n",
      "avg_cost 0.4184868138486688\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289579\n",
      "avg_cost 0.42056824922561636\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2855124\n",
      "avg_cost 0.4226459878141229\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950802\n",
      "avg_cost 0.4247324243458834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972248\n",
      "avg_cost 0.4268208104913884\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3207722\n",
      "avg_cost 0.42893060337413436\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307007\n",
      "avg_cost 0.4310278825326399\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005478\n",
      "avg_cost 0.43311928965828633\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3269367\n",
      "avg_cost 0.4352346866781061\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312742\n",
      "avg_cost 0.4373371794007041\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310861\n",
      "avg_cost 0.4394379622286016\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058693\n",
      "avg_cost 0.4415342070839621\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2945163\n",
      "avg_cost 0.4436201310157775\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305926\n",
      "avg_cost 0.44571642745624884\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3242397\n",
      "avg_cost 0.4478293726660988\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2836516\n",
      "avg_cost 0.4499054195664145\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306749\n",
      "avg_cost 0.4520024642077359\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3018236\n",
      "avg_cost 0.45409503113139754\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064\n",
      "avg_cost 0.4561917584592645\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2939315\n",
      "avg_cost 0.4582771507176485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2889864\n",
      "avg_cost 0.4603580474853515\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299636\n",
      "avg_cost 0.4624486255645751\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985537\n",
      "avg_cost 0.4645382198420437\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3173628\n",
      "avg_cost 0.4666449132832613\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125198\n",
      "avg_cost 0.46874720400029957\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997556\n",
      "avg_cost 0.4708378908850929\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290871\n",
      "avg_cost 0.4729205007986588\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314814\n",
      "avg_cost 0.4750248772447759\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077548\n",
      "avg_cost 0.477122836112976\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003707\n",
      "avg_cost 0.47921408219770945\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3214996\n",
      "avg_cost 0.48132453636689615\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.319226\n",
      "avg_cost 0.4834329236637462\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299837\n",
      "avg_cost 0.48552368467504325\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018522\n",
      "avg_cost 0.48761627760800447\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3166094\n",
      "avg_cost 0.48972228613766755\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304918\n",
      "avg_cost 0.49181766618381845\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026438\n",
      "avg_cost 0.4939109787074002\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041391\n",
      "avg_cost 0.49600565065037117\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2937505\n",
      "avg_cost 0.4980908783999356\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954807\n",
      "avg_cost 0.5001776790618896\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291309\n",
      "avg_cost 0.5022606873512268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071523\n",
      "avg_cost 0.5043580985069275\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079407\n",
      "avg_cost 0.5064562264355746\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3113348\n",
      "avg_cost 0.5085574399341237\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307346\n",
      "avg_cost 0.5106550273028287\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990036\n",
      "avg_cost 0.5127450305765325\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975051\n",
      "avg_cost 0.5148336716131731\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302919\n",
      "avg_cost 0.5169272342595187\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902443\n",
      "avg_cost 0.5190092745694247\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307766\n",
      "avg_cost 0.5211072436246005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2901988\n",
      "avg_cost 0.5231892425363713\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291215\n",
      "avg_cost 0.5252721652117642\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992065\n",
      "avg_cost 0.5273623529347506\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291198\n",
      "avg_cost 0.5294452602213079\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054705\n",
      "avg_cost 0.531541142463684\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962344\n",
      "avg_cost 0.5336286282539366\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029935\n",
      "avg_cost 0.5357222587412053\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3177066\n",
      "avg_cost 0.5378292647275057\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046052\n",
      "avg_cost 0.539924360405315\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014038\n",
      "avg_cost 0.5420165456425059\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2935092\n",
      "avg_cost 0.5441015540469776\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964387\n",
      "avg_cost 0.5461892255869778\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303738\n",
      "avg_cost 0.5482835329662669\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038318\n",
      "avg_cost 0.550377925526012\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051364\n",
      "avg_cost 0.552473504109816\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.315728\n",
      "avg_cost 0.5545787113363091\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020546\n",
      "avg_cost 0.5566714882850645\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041897\n",
      "avg_cost 0.558766206177798\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032153\n",
      "avg_cost 0.560860038237138\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994926\n",
      "avg_cost 0.5629504860531198\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069832\n",
      "avg_cost 0.5650477435372091\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2734275\n",
      "avg_cost 0.5671144957975907\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2931006\n",
      "avg_cost 0.5691991327025673\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2892509\n",
      "avg_cost 0.5712802698395468\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3112555\n",
      "avg_cost 0.5733814111622897\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045743\n",
      "avg_cost 0.5754764786633578\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3202398\n",
      "avg_cost 0.5775857875563881\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3096159\n",
      "avg_cost 0.5796854383295232\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986536\n",
      "avg_cost 0.581775123422796\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2855253\n",
      "avg_cost 0.5838528737154873\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960112\n",
      "avg_cost 0.5859401566332036\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983437\n",
      "avg_cost 0.5880295599590648\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301637\n",
      "avg_cost 0.590121957172047\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088226\n",
      "avg_cost 0.5922208868373524\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29227\n",
      "avg_cost 0.5943047686056657\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3133724\n",
      "avg_cost 0.5964078343998303\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3190737\n",
      "avg_cost 0.5985160831971602\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294505\n",
      "avg_cost 0.6006019967252558\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062844\n",
      "avg_cost 0.6026986189322039\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305323\n",
      "avg_cost 0.6047943670099433\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963772\n",
      "avg_cost 0.6068819826299495\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055165\n",
      "avg_cost 0.6089779067039491\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973855\n",
      "avg_cost 0.6110664389350199\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030477\n",
      "avg_cost 0.6131601186232135\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2850437\n",
      "avg_cost 0.6152374310926959\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932425\n",
      "avg_cost 0.6173221969604493\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2882655\n",
      "avg_cost 0.6194024382938039\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294374\n",
      "avg_cost 0.6214882328293542\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072066\n",
      "avg_cost 0.623585693402724\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3161821\n",
      "avg_cost 0.6256913135268473\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31194\n",
      "avg_cost 0.6277930771220815\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29455\n",
      "avg_cost 0.629879031614824\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993531\n",
      "avg_cost 0.6319693526354705\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047957\n",
      "avg_cost 0.6340646214918658\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961032\n",
      "avg_cost 0.6361519880728289\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2947738\n",
      "avg_cost 0.6382381460883402\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2922158\n",
      "avg_cost 0.6403219786557285\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310391\n",
      "avg_cost 0.642422334064137\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3255048\n",
      "avg_cost 0.6445364293185148\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314154\n",
      "avg_cost 0.6466402056000451\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3168895\n",
      "avg_cost 0.6487464688040995\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300233\n",
      "avg_cost 0.6508375896107068\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009772\n",
      "avg_cost 0.6529293870925905\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041706\n",
      "avg_cost 0.655024087645791\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949002\n",
      "avg_cost 0.6571103605357086\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2865326\n",
      "avg_cost 0.6591890265724878\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024487\n",
      "avg_cost 0.6612821617993444\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299585\n",
      "avg_cost 0.6633726937120613\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297701\n",
      "avg_cost 0.6654615126956595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30963\n",
      "avg_cost 0.6675611762567002\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2916825\n",
      "avg_cost 0.6696445239673964\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032506\n",
      "avg_cost 0.6717383881048725\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306266\n",
      "avg_cost 0.67383499362252\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920244\n",
      "avg_cost 0.6759186521443455\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909658\n",
      "avg_cost 0.6780013483220882\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2944365\n",
      "avg_cost 0.6800871996446091\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2908955\n",
      "avg_cost 0.6821698318828238\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3114343\n",
      "avg_cost 0.6842711357636887\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2914028\n",
      "avg_cost 0.6863542292334819\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314984\n",
      "avg_cost 0.6884587602181871\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3059268\n",
      "avg_cost 0.6905550573088909\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2883663\n",
      "avg_cost 0.6926353903250264\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301096\n",
      "avg_cost 0.6947272957455032\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054285\n",
      "avg_cost 0.6968231398409067\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307094\n",
      "avg_cost 0.6989204981110316\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3267963\n",
      "avg_cost 0.7010357674685395\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949252\n",
      "avg_cost 0.7031220631165941\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970357\n",
      "avg_cost 0.705210277383978\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301939\n",
      "avg_cost 0.7073029492118145\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047056\n",
      "avg_cost 0.7093981361389163\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304312\n",
      "avg_cost 0.7114929652214054\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088503\n",
      "avg_cost 0.7135919200290337\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2892888\n",
      "avg_cost 0.7156730916283351\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.318086\n",
      "avg_cost 0.7177804424545986\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004718\n",
      "avg_cost 0.719871780438857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016894\n",
      "avg_cost 0.721964225335555\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961211\n",
      "avg_cost 0.7240516081723304\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30951\n",
      "avg_cost 0.7261511627110572\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2879229\n",
      "avg_cost 0.7282310925830499\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300701\n",
      "avg_cost 0.7303226388584488\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299939\n",
      "avg_cost 0.7324134924195034\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2895253\n",
      "avg_cost 0.7344948790290143\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3204284\n",
      "avg_cost 0.7366043593666775\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296042\n",
      "avg_cost 0.7386916702443908\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295995\n",
      "avg_cost 0.740778938423504\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982364\n",
      "avg_cost 0.7428682442144918\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968895\n",
      "avg_cost 0.744956325617704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982707\n",
      "avg_cost 0.7470456626198513\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29869\n",
      "avg_cost 0.749135380874981\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958755\n",
      "avg_cost 0.7512225404652686\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3196034\n",
      "avg_cost 0.7533312708681283\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2904212\n",
      "avg_cost 0.7554134720022032\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3057752\n",
      "avg_cost 0.7575096312436195\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960863\n",
      "avg_cost 0.759596982435747\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011215\n",
      "avg_cost 0.7616889110478492\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011475\n",
      "avg_cost 0.7637808632850651\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308031\n",
      "avg_cost 0.7658790733597499\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063014\n",
      "avg_cost 0.7679757109555335\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287099\n",
      "avg_cost 0.7700548917596994\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141234\n",
      "avg_cost 0.7721586402979768\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302121\n",
      "avg_cost 0.7742514775016095\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958326\n",
      "avg_cost 0.7763385980779478\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288024\n",
      "avg_cost 0.7784186198494655\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047895\n",
      "avg_cost 0.7805138830705126\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3148289\n",
      "avg_cost 0.7826182729547677\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089721\n",
      "avg_cost 0.7847173385186632\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040967\n",
      "avg_cost 0.7868119718811732\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082623\n",
      "avg_cost 0.788910392197696\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2843082\n",
      "avg_cost 0.7909870360114362\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079178\n",
      "avg_cost 0.7930851431326437\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069046\n",
      "avg_cost 0.7951823290911592\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920265\n",
      "avg_cost 0.7972659895636822\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043346\n",
      "avg_cost 0.7993608392368667\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964747\n",
      "avg_cost 0.8014485435052355\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3128748\n",
      "avg_cost 0.8035511569543322\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107407\n",
      "avg_cost 0.8056518303264275\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046854\n",
      "avg_cost 0.8077469988302755\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2879748\n",
      "avg_cost 0.8098269759524955\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932708\n",
      "avg_cost 0.8119117676128044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307567\n",
      "avg_cost 0.8140095556866042\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2965112\n",
      "avg_cost 0.8160972931168299\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3234773\n",
      "avg_cost 0.8182095451788471\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312955\n",
      "avg_cost 0.8203122314539826\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2899067\n",
      "avg_cost 0.8223939648541541\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304858\n",
      "avg_cost 0.8244892902807759\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014095\n",
      "avg_cost 0.8265814807198267\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084435\n",
      "avg_cost 0.8286800657619132\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2913604\n",
      "avg_cost 0.8307631206512454\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297101\n",
      "avg_cost 0.8328513943065299\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009634\n",
      "avg_cost 0.8349431792172521\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3181062\n",
      "avg_cost 0.8370505484667694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3101335\n",
      "avg_cost 0.839150669791482\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2937267\n",
      "avg_cost 0.8412358758666302\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298792\n",
      "avg_cost 0.8433256866715174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288237\n",
      "avg_cost 0.8454059022123166\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085344\n",
      "avg_cost 0.8475045698339292\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2854033\n",
      "avg_cost 0.8495822091536092\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3138921\n",
      "avg_cost 0.8516857474500485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046386\n",
      "avg_cost 0.8537808734720406\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2727041\n",
      "avg_cost 0.8558469681306321\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074102\n",
      "avg_cost 0.8579446138035169\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3191793\n",
      "avg_cost 0.860052958618511\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316104\n",
      "avg_cost 0.8621585076505489\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3185666\n",
      "avg_cost 0.8642662954330446\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3227372\n",
      "avg_cost 0.8663778747211804\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3194103\n",
      "avg_cost 0.8684864295612683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2978644\n",
      "avg_cost 0.8705753972313622\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013015\n",
      "avg_cost 0.8726674894853074\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956414\n",
      "avg_cost 0.8747544362328271\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3159482\n",
      "avg_cost 0.8768598437309267\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.279976\n",
      "avg_cost 0.8789325490864842\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038678\n",
      "avg_cost 0.8810269743745979\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2953742\n",
      "avg_cost 0.8831136781519111\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310304\n",
      "avg_cost 0.8852139544487001\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3174407\n",
      "avg_cost 0.887320718765259\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3184938\n",
      "avg_cost 0.889428440440785\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069212\n",
      "avg_cost 0.8915256415713919\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297107\n",
      "avg_cost 0.8936139206452806\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3183727\n",
      "avg_cost 0.8957215322147719\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087766\n",
      "avg_cost 0.8978204200484539\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312764\n",
      "avg_cost 0.8999229327115148\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2830572\n",
      "avg_cost 0.9019984392686327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961128\n",
      "avg_cost 0.9040858145193623\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2818635\n",
      "avg_cost 0.9061602358384568\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296442\n",
      "avg_cost 0.9082479104128753\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300987\n",
      "avg_cost 0.9103397167812697\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297308\n",
      "avg_cost 0.9124281785704875\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989962\n",
      "avg_cost 0.9145181751251223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2936156\n",
      "avg_cost 0.9166032801974906\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2889621\n",
      "avg_cost 0.9186841548572889\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2814167\n",
      "avg_cost 0.9207581699978223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302285\n",
      "avg_cost 0.9228511563214391\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055513\n",
      "avg_cost 0.9249471120400865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301105\n",
      "avg_cost 0.9270390256968414\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314754\n",
      "avg_cost 0.9291433475234294\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29123\n",
      "avg_cost 0.9312262838537045\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997124\n",
      "avg_cost 0.9333169315078045\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3243792\n",
      "avg_cost 0.9354300035129897\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302394\n",
      "avg_cost 0.9375230888886887\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2866395\n",
      "avg_cost 0.9396018520268529\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2965603\n",
      "avg_cost 0.9416896341063762\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3207657\n",
      "avg_cost 0.9437994211370297\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020065\n",
      "avg_cost 0.9458921543034643\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.318779\n",
      "avg_cost 0.948000135205009\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3128722\n",
      "avg_cost 0.95010274626992\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021157\n",
      "avg_cost 0.9521955787051811\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305173\n",
      "avg_cost 0.9542911904508421\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2871828\n",
      "avg_cost 0.9563704475489533\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307212\n",
      "avg_cost 0.9584679131074388\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064036\n",
      "avg_cost 0.9605646436864683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3134713\n",
      "avg_cost 0.9626677994294603\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2925568\n",
      "avg_cost 0.9647519419410016\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317302\n",
      "avg_cost 0.9668585801124576\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989564\n",
      "avg_cost 0.9689485404708172\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303308\n",
      "avg_cost 0.9710424568436367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3169844\n",
      "avg_cost 0.9731488063118678\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037102\n",
      "avg_cost 0.97524308833209\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009176\n",
      "avg_cost 0.9773348316279329\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999148\n",
      "avg_cost 0.979425663297827\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004107\n",
      "avg_cost 0.9815169457955798\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3138366\n",
      "avg_cost 0.9836204335906293\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29935\n",
      "avg_cost 0.9857107517936018\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30664\n",
      "avg_cost 0.9878076971660966\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082876\n",
      "avg_cost 0.9899061404575007\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3205476\n",
      "avg_cost 0.9920157291672452\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107042\n",
      "avg_cost 0.9941163693774836\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3070457\n",
      "avg_cost 0.9962136836485435\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073235\n",
      "avg_cost 0.998311250426553\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2930431\n",
      "avg_cost 1.0003958350961863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292835\n",
      "avg_cost 1.0024802305481655\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038847\n",
      "avg_cost 1.0045746712251147\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3080049\n",
      "avg_cost 1.0066728574579416\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2883158\n",
      "avg_cost 1.0087531445243145\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3139322\n",
      "avg_cost 1.0108567192337734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3100774\n",
      "avg_cost 1.0129567896236078\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013716\n",
      "avg_cost 1.0150489456003366\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982996\n",
      "avg_cost 1.0171383088285277\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3068318\n",
      "avg_cost 1.0192354286800736\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299366\n",
      "avg_cost 1.021325761404905\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950718\n",
      "avg_cost 1.0234121903506197\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297012\n",
      "avg_cost 1.0255003831603315\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044946\n",
      "avg_cost 1.0275953782688492\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3048277\n",
      "avg_cost 1.0296906761689624\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2998292\n",
      "avg_cost 1.031781430027702\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986321\n",
      "avg_cost 1.033871095614\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029134\n",
      "avg_cost 1.03596465327523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296096\n",
      "avg_cost 1.0380520133538682\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090816\n",
      "avg_cost 1.0401511784033344\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982626\n",
      "avg_cost 1.04224050803618\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017612\n",
      "avg_cost 1.0443330181728712\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2942667\n",
      "avg_cost 1.046418715173548\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030295\n",
      "avg_cost 1.0485123783891852\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005767\n",
      "avg_cost 1.0506038117408754\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024433\n",
      "avg_cost 1.0526969419826162\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2959526\n",
      "avg_cost 1.0547841715812685\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981992\n",
      "avg_cost 1.056873443560167\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298983\n",
      "avg_cost 1.058963428193873\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3102393\n",
      "avg_cost 1.0610636457529938\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996929\n",
      "avg_cost 1.0631542756340724\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291186\n",
      "avg_cost 1.0652371720834215\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001304\n",
      "avg_cost 1.0673281996900388\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093944\n",
      "avg_cost 1.0694276491078467\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297341\n",
      "avg_cost 1.071516141024503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297926\n",
      "avg_cost 1.073605164614591\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3121471\n",
      "avg_cost 1.0757071165605028\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3165746\n",
      "avg_cost 1.0778130934455181\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026845\n",
      "avg_cost 1.0799064430323517\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314342\n",
      "avg_cost 1.0820103903250264\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117537\n",
      "avg_cost 1.0841119846430696\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2940383\n",
      "avg_cost 1.0861974740028386\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289642\n",
      "avg_cost 1.0882789668169894\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038473\n",
      "avg_cost 1.090373373465105\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014638\n",
      "avg_cost 1.0924656133218251\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308043\n",
      "avg_cost 1.094563834233718\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018813\n",
      "avg_cost 1.0966564536094672\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303628\n",
      "avg_cost 1.0987506608529531\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047674\n",
      "avg_cost 1.100845903916793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3159876\n",
      "avg_cost 1.1029513471776795\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298836\n",
      "avg_cost 1.1050411980802368\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297152\n",
      "avg_cost 1.1071295181187721\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3033369\n",
      "avg_cost 1.1092234607176352\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2866476\n",
      "avg_cost 1.1113022312251009\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038175\n",
      "avg_cost 1.1133966107801962\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2953353\n",
      "avg_cost 1.115483279228211\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008294\n",
      "avg_cost 1.1175749423287138\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2937536\n",
      "avg_cost 1.1196601728959523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3111959\n",
      "avg_cost 1.1217612600326545\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107772\n",
      "avg_cost 1.1238619665666068\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036833\n",
      "avg_cost 1.1259562240947385\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316108\n",
      "avg_cost 1.1280617768114272\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29988\n",
      "avg_cost 1.1301525768366736\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309287\n",
      "avg_cost 1.1322519287196082\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004954\n",
      "avg_cost 1.1343432881615387\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304595\n",
      "avg_cost 1.136438374519349\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3160658\n",
      "avg_cost 1.1385438888723207\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098419\n",
      "avg_cost 1.1406437451189222\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2953913\n",
      "avg_cost 1.1427304645018153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123093\n",
      "avg_cost 1.1448325638337578\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2930818\n",
      "avg_cost 1.1469171836159455\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299907\n",
      "avg_cost 1.1490080081332823\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014727\n",
      "avg_cost 1.1511002560095362\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013177\n",
      "avg_cost 1.1531923630020844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093557\n",
      "avg_cost 1.155291777307338\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3068385\n",
      "avg_cost 1.1573889032277203\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029242\n",
      "avg_cost 1.1594824706424376\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902987\n",
      "avg_cost 1.1615645603700127\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934089\n",
      "avg_cost 1.1636494775251918\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3097544\n",
      "avg_cost 1.1657492542266854\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943387\n",
      "avg_cost 1.1678350166840996\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934122\n",
      "avg_cost 1.169919936873697\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300715\n",
      "avg_cost 1.1720114959370016\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088584\n",
      "avg_cost 1.1741104581139314\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2953274\n",
      "avg_cost 1.1761971194093888\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3169205\n",
      "avg_cost 1.1783034107901844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026953\n",
      "avg_cost 1.1803967701305054\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980971\n",
      "avg_cost 1.182485949342902\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3147306\n",
      "avg_cost 1.184590249928562\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092651\n",
      "avg_cost 1.1866895818710337\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3211672\n",
      "avg_cost 1.1887997338988574\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2928362\n",
      "avg_cost 1.1908841304345574\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303972\n",
      "avg_cost 1.1929786504398703\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999132\n",
      "avg_cost 1.1950694805925552\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29612\n",
      "avg_cost 1.1971568623456097\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316758\n",
      "avg_cost 1.1992630059068863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103695\n",
      "avg_cost 1.20136334180832\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304982\n",
      "avg_cost 1.2034587799419065\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305197\n",
      "avg_cost 1.205554413578728\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014076\n",
      "avg_cost 1.2076466022838255\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052785\n",
      "avg_cost 1.2097423100471505\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049235\n",
      "avg_cost 1.211837695078417\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995372\n",
      "avg_cost 1.2139281834255573\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964296\n",
      "avg_cost 1.2160158467292794\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002708\n",
      "avg_cost 1.2181070019982085\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981324\n",
      "avg_cost 1.2201962132887414\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298058\n",
      "avg_cost 1.2222853569550955\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995195\n",
      "avg_cost 1.2243758292631677\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074176\n",
      "avg_cost 1.2264734816551215\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961936\n",
      "avg_cost 1.2285609303821223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970886\n",
      "avg_cost 1.2306491927667105\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301714\n",
      "avg_cost 1.2327416599880572\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996125\n",
      "avg_cost 1.2348322168263528\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098452\n",
      "avg_cost 1.2369320761073725\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2898993\n",
      "avg_cost 1.239013802788475\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073537\n",
      "avg_cost 1.2411113970929932\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093717\n",
      "avg_cost 1.2432108259201056\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968557\n",
      "avg_cost 1.2452988765456465\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.282755\n",
      "avg_cost 1.2473741082711658\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073385\n",
      "avg_cost 1.2494716887040576\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060343\n",
      "avg_cost 1.2515680835463787\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993004\n",
      "avg_cost 1.2536583566665653\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2940936\n",
      "avg_cost 1.2557438963109802\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2900684\n",
      "avg_cost 1.257825776663694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117008\n",
      "avg_cost 1.259927322864533\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126614\n",
      "avg_cost 1.262029742327604\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006337\n",
      "avg_cost 1.2641212274811489\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289954\n",
      "avg_cost 1.2662030037966645\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019674\n",
      "avg_cost 1.2682957014170564\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2832444\n",
      "avg_cost 1.270371378118342\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299303\n",
      "avg_cost 1.2724616536227145\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300061\n",
      "avg_cost 1.2745526181567803\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090262\n",
      "avg_cost 1.2766517329216007\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004391\n",
      "avg_cost 1.278743041211909\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311996\n",
      "avg_cost 1.2808448557420216\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990496\n",
      "avg_cost 1.2829349008473487\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2895794\n",
      "avg_cost 1.2850163366577847\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289568\n",
      "avg_cost 1.2870977620645008\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043442\n",
      "avg_cost 1.2891926204074518\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3105223\n",
      "avg_cost 1.291293095241894\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2804139\n",
      "avg_cost 1.2933661987564786\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3196204\n",
      "avg_cost 1.295474944548174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311801\n",
      "avg_cost 1.2975765817815612\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011198\n",
      "avg_cost 1.2996685088764541\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29695\n",
      "avg_cost 1.3017566453326836\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999787\n",
      "avg_cost 1.3038475350900134\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135357\n",
      "avg_cost 1.3059507493539293\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3114097\n",
      "avg_cost 1.3080520309101455\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988741\n",
      "avg_cost 1.310141916491769\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083198\n",
      "avg_cost 1.312240389043635\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095148\n",
      "avg_cost 1.314339947917245\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3184311\n",
      "avg_cost 1.3164476125890563\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3096893\n",
      "avg_cost 1.3185473301193935\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2691875\n",
      "avg_cost 1.3206102278015834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3150969\n",
      "avg_cost 1.3227148613062776\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301925\n",
      "avg_cost 1.3248075203462084\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946086\n",
      "avg_cost 1.3268935281580148\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2916906\n",
      "avg_cost 1.3289768832380124\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313116\n",
      "avg_cost 1.3310797160322019\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304271\n",
      "avg_cost 1.3331745078346948\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3212857\n",
      "avg_cost 1.3352847675843675\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932718\n",
      "avg_cost 1.337369560111653\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304931\n",
      "avg_cost 1.3394649518619888\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039477\n",
      "avg_cost 1.341559449759397\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2861793\n",
      "avg_cost 1.343637794581327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2726552\n",
      "avg_cost 1.345703844807365\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2870765\n",
      "avg_cost 1.3477830052375797\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3057315\n",
      "avg_cost 1.349879124814814\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077426\n",
      "avg_cost 1.3519770726290619\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956693\n",
      "avg_cost 1.3540640447356487\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2944481\n",
      "avg_cost 1.3561499066786336\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966743\n",
      "avg_cost 1.3582377923618667\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3176506\n",
      "avg_cost 1.3603447474132888\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987814\n",
      "avg_cost 1.3624345486814329\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974555\n",
      "avg_cost 1.3645231446352875\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301465\n",
      "avg_cost 1.3666153855757281\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001692\n",
      "avg_cost 1.368706448511644\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.302503\n",
      "avg_cost 1.3707996331561698\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2834127\n",
      "avg_cost 1.3728754628788344\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3114853\n",
      "avg_cost 1.3749768131429503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3181565\n",
      "avg_cost 1.3770842281254858\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044374\n",
      "avg_cost 1.3791791712154045\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2783828\n",
      "avg_cost 1.381250428286466\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2913451\n",
      "avg_cost 1.3833334693041717\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3080106\n",
      "avg_cost 1.3854316607388586\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095028\n",
      "avg_cost 1.3875312087752605\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050454\n",
      "avg_cost 1.3896267045627944\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003182\n",
      "avg_cost 1.391717902963812\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294309\n",
      "avg_cost 1.3938036383282058\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3212512\n",
      "avg_cost 1.3959138666499749\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3070354\n",
      "avg_cost 1.3980111716010357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923093\n",
      "avg_cost 1.400095089132136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3110085\n",
      "avg_cost 1.4021960059079261\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293367\n",
      "avg_cost 1.4042808849161326\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090858\n",
      "avg_cost 1.4063800538669937\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291543\n",
      "avg_cost 1.4084632747823547\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2822838\n",
      "avg_cost 1.4105380782214083\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3176725\n",
      "avg_cost 1.4126450532132935\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296943\n",
      "avg_cost 1.414733183167198\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2955124\n",
      "avg_cost 1.4168200126561257\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3181133\n",
      "avg_cost 1.4189273884079678\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306644\n",
      "avg_cost 1.4210243374651135\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125422\n",
      "avg_cost 1.423126648556103\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3177292\n",
      "avg_cost 1.4252336751330987\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3097045\n",
      "avg_cost 1.4273334065350625\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090856\n",
      "avg_cost 1.4294325752691794\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3097765\n",
      "avg_cost 1.4315323721278803\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009703\n",
      "avg_cost 1.4336241633241833\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158927\n",
      "avg_cost 1.4357295203208928\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312522\n",
      "avg_cost 1.4378318129886285\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289186\n",
      "avg_cost 1.4399128911711958\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3027723\n",
      "avg_cost 1.4420063205198814\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976253\n",
      "avg_cost 1.44409507079558\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086576\n",
      "avg_cost 1.4461938504739247\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2935352\n",
      "avg_cost 1.44827888250351\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307447\n",
      "avg_cost 1.450376561554996\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30622\n",
      "avg_cost 1.45247312524102\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993677\n",
      "avg_cost 1.4545634594830603\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887812\n",
      "avg_cost 1.456644169634039\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051333\n",
      "avg_cost 1.458739745400169\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3171225\n",
      "avg_cost 1.4608462203632704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063915\n",
      "avg_cost 1.4629429398883476\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050103\n",
      "avg_cost 1.4650384038144895\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31363\n",
      "avg_cost 1.467141703909094\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060195\n",
      "avg_cost 1.4692380853132772\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906134\n",
      "avg_cost 1.4713204611431472\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307403\n",
      "avg_cost 1.4734181003137072\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2978644\n",
      "avg_cost 1.4755070679838012\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964866\n",
      "avg_cost 1.477594783089378\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2901225\n",
      "avg_cost 1.4796767126430168\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011234\n",
      "avg_cost 1.4817686429890722\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058703\n",
      "avg_cost 1.4838648887114094\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092666\n",
      "avg_cost 1.485964221954346\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011553\n",
      "avg_cost 1.4880561813441193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295774\n",
      "avg_cost 1.4901432486013937\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299181\n",
      "avg_cost 1.4922334131327546\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2895982\n",
      "avg_cost 1.4943148660659793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3173847\n",
      "avg_cost 1.4964215794476599\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297871\n",
      "avg_cost 1.4985105531865903\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303139\n",
      "avg_cost 1.5006043158877982\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298106\n",
      "avg_cost 1.502693503119729\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2913756\n",
      "avg_cost 1.5047765718806876\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296815\n",
      "avg_cost 1.5068645854429767\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3200758\n",
      "avg_cost 1.508973745216023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954855\n",
      "avg_cost 1.5110605502128602\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092756\n",
      "avg_cost 1.513159891692075\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050566\n",
      "avg_cost 1.5152553976665846\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044453\n",
      "avg_cost 1.5173503479090606\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3110747\n",
      "avg_cost 1.5194513249397281\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043997\n",
      "avg_cost 1.521546233784069\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016698\n",
      "avg_cost 1.5236386609077457\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298618\n",
      "avg_cost 1.5257283137061382\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985625\n",
      "avg_cost 1.5278179160031409\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2922914\n",
      "avg_cost 1.5299018172784289\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2878845\n",
      "avg_cost 1.5319817122546113\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3111503\n",
      "avg_cost 1.5340827579931784\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2863264\n",
      "avg_cost 1.5361612365462567\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042512\n",
      "avg_cost 1.5382560103589842\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956293\n",
      "avg_cost 1.5403429460525517\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3057222\n",
      "avg_cost 1.5424390571767639\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090549\n",
      "avg_cost 1.5445381979508839\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060648\n",
      "avg_cost 1.5466346205364578\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103235\n",
      "avg_cost 1.548734914606268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307674\n",
      "avg_cost 1.550832799998197\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982733\n",
      "avg_cost 1.55292213938453\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2951417\n",
      "avg_cost 1.5550086318362846\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3175411\n",
      "avg_cost 1.557115487402136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060622\n",
      "avg_cost 1.5592119076035242\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298915\n",
      "avg_cost 1.5613018302483994\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3048759\n",
      "avg_cost 1.5633971719308335\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2790244\n",
      "avg_cost 1.565469012260437\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030179\n",
      "avg_cost 1.5675626648556102\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.320957\n",
      "avg_cost 1.5696726257150824\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089993\n",
      "avg_cost 1.5717717159878124\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3028731\n",
      "avg_cost 1.5738652370192787\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077924\n",
      "avg_cost 1.5759632301330566\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072424\n",
      "avg_cost 1.5780607232180508\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056014\n",
      "avg_cost 1.5801567244529724\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3193438\n",
      "avg_cost 1.5822652188214388\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3154023\n",
      "avg_cost 1.5843701299754056\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2783287\n",
      "avg_cost 1.5864413378455422\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009079\n",
      "avg_cost 1.5885330722548745\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016088\n",
      "avg_cost 1.5906254438920455\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2935512\n",
      "avg_cost 1.59271049044349\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29107\n",
      "avg_cost 1.5947932813384318\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3027298\n",
      "avg_cost 1.5968866721066564\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011773\n",
      "avg_cost 1.5989786514368927\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3144567\n",
      "avg_cost 1.6010827029835097\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2856529\n",
      "avg_cost 1.6031605692343283\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958915\n",
      "avg_cost 1.605247743346475\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2844207\n",
      "avg_cost 1.60732448946346\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3148158\n",
      "avg_cost 1.6094288674267863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005602\n",
      "avg_cost 1.6115202858231292\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066416\n",
      "avg_cost 1.613617232712833\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30363\n",
      "avg_cost 1.6157114419070164\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043778\n",
      "avg_cost 1.6178063308108943\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078742\n",
      "avg_cost 1.61990439826792\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058825\n",
      "avg_cost 1.6220006550442094\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2889035\n",
      "avg_cost 1.6240814763849438\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954693\n",
      "avg_cost 1.626168266643178\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076925\n",
      "avg_cost 1.6282661689411517\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3147874\n",
      "avg_cost 1.6303705211119226\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3155525\n",
      "avg_cost 1.632475568814712\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291056\n",
      "avg_cost 1.6345583469217482\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004835\n",
      "avg_cost 1.6366496955264707\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2890568\n",
      "avg_cost 1.6387306562337016\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986312\n",
      "avg_cost 1.6408203209530232\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990215\n",
      "avg_cost 1.6429103404825394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966354\n",
      "avg_cost 1.644998190836474\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3152342\n",
      "avg_cost 1.6471029491858058\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2892904\n",
      "avg_cost 1.6491841223023163\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2792726\n",
      "avg_cost 1.6512561882625936\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295176\n",
      "avg_cost 1.6533427119255075\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289004\n",
      "avg_cost 1.6554236247322784\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042817\n",
      "avg_cost 1.6575184262882587\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2926352\n",
      "avg_cost 1.6596026401086295\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3133798\n",
      "avg_cost 1.661705712621863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980378\n",
      "avg_cost 1.6637948378649634\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062518\n",
      "avg_cost 1.665891430377961\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983997\n",
      "avg_cost 1.6679808846387005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291855\n",
      "avg_cost 1.6700643892721705\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3112028\n",
      "avg_cost 1.6721654826944534\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3177927\n",
      "avg_cost 1.6742725669253966\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305011\n",
      "avg_cost 1.676368031501771\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3145847\n",
      "avg_cost 1.6784721994400034\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3242407\n",
      "avg_cost 1.68058514551683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992945\n",
      "avg_cost 1.6826754132184123\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3161242\n",
      "avg_cost 1.684780980673704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3163059\n",
      "avg_cost 1.6868867132880478\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971518\n",
      "avg_cost 1.688975033109839\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2814937\n",
      "avg_cost 1.691049118258737\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2904863\n",
      "avg_cost 1.6931313785639683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.275012\n",
      "avg_cost 1.695199571306056\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.282737\n",
      "avg_cost 1.6972747867757632\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3146584\n",
      "avg_cost 1.6993790216879419\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052478\n",
      "avg_cost 1.70147470149127\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298021\n",
      "avg_cost 1.7035638115622787\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3160338\n",
      "avg_cost 1.7056692968715328\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034818\n",
      "avg_cost 1.7077633712508469\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30199\n",
      "avg_cost 1.7098560894619341\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034708\n",
      "avg_cost 1.7119501538710167\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3137712\n",
      "avg_cost 1.7140535822781655\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.336008\n",
      "avg_cost 1.7161772259798922\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31616\n",
      "avg_cost 1.7182828259468084\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115726\n",
      "avg_cost 1.7203842555392876\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.318118\n",
      "avg_cost 1.7224916356260132\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058677\n",
      "avg_cost 1.7245878789641647\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2747853\n",
      "avg_cost 1.7266558655825535\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045936\n",
      "avg_cost 1.7287509506398988\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3153758\n",
      "avg_cost 1.7308558377352634\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056595\n",
      "avg_cost 1.7329518918557607\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296235\n",
      "avg_cost 1.7350393782962459\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973764\n",
      "avg_cost 1.7371279022910384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2988648\n",
      "avg_cost 1.7392177794196395\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3000507\n",
      "avg_cost 1.7413087346337064\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.318223\n",
      "avg_cost 1.7434162100878634\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298226\n",
      "avg_cost 1.7455055065588523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975059\n",
      "avg_cost 1.7475941482457253\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976305\n",
      "avg_cost 1.7496829032897956\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979345\n",
      "avg_cost 1.7517719346826734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311109\n",
      "avg_cost 1.7538729429245004\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310574\n",
      "avg_cost 1.7559734647924259\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993073\n",
      "avg_cost 1.7580637441981932\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076866\n",
      "avg_cost 1.7601616410775627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310685\n",
      "avg_cost 1.762262263731524\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974057\n",
      "avg_cost 1.7643508143858486\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3180327\n",
      "avg_cost 1.7664581168781637\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2967348\n",
      "avg_cost 1.768546057614414\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003023\n",
      "avg_cost 1.7706372414935727\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305211\n",
      "avg_cost 1.7727328879182997\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999048\n",
      "avg_cost 1.7748237104849391\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2937644\n",
      "avg_cost 1.776908950805665\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290471\n",
      "avg_cost 1.7789911972392698\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907286\n",
      "avg_cost 1.7810736777565706\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3149068\n",
      "avg_cost 1.783178138516167\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29866\n",
      "avg_cost 1.785267829461532\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039112\n",
      "avg_cost 1.7873622941970835\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297807\n",
      "avg_cost 1.789451209631834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932823\n",
      "avg_cost 1.7915360116958627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022056\n",
      "avg_cost 1.7936289258436733\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2877917\n",
      "avg_cost 1.7957087365063764\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958796\n",
      "avg_cost 1.7977958997813148\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022544\n",
      "avg_cost 1.7998888583616788\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043272\n",
      "avg_cost 1.8019837013157942\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055792\n",
      "avg_cost 1.8040796823935086\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3100655\n",
      "avg_cost 1.8061797419461347\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956016\n",
      "avg_cost 1.8082666524973794\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2904675\n",
      "avg_cost 1.8103488956798217\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303798\n",
      "avg_cost 1.8124432574618956\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2908943\n",
      "avg_cost 1.8145258886163895\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135002\n",
      "avg_cost 1.8166290705854253\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034446\n",
      "avg_cost 1.81872311115265\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294429\n",
      "avg_cost 1.8208089557561018\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.32065\n",
      "avg_cost 1.8229186376658364\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018844\n",
      "avg_cost 1.8250112598592596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3175194\n",
      "avg_cost 1.8271180957013922\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067212\n",
      "avg_cost 1.8292151149836464\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047667\n",
      "avg_cost 1.831310357397254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943387\n",
      "avg_cost 1.8333961198546682\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3155105\n",
      "avg_cost 1.8355011294104848\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3113482\n",
      "avg_cost 1.8376023550467069\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3232768\n",
      "avg_cost 1.8397144248268833\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062391\n",
      "avg_cost 1.8418110058524404\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2866044\n",
      "avg_cost 1.8438897371292127\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310634\n",
      "avg_cost 1.845990313399923\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3097506\n",
      "avg_cost 1.84809008663351\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045568\n",
      "avg_cost 1.850185138312254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093476\n",
      "avg_cost 1.852284545248206\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118777\n",
      "avg_cost 1.8543862522732137\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031983\n",
      "avg_cost 1.8564800689437182\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3101428\n",
      "avg_cost 1.8585801987214532\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044274\n",
      "avg_cost 1.860675132708117\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003106\n",
      "avg_cost 1.8627663241733214\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005772\n",
      "avg_cost 1.8648577579585\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293938\n",
      "avg_cost 1.8669431560689764\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009205\n",
      "avg_cost 1.8690349019657493\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950678\n",
      "avg_cost 1.8711213272268132\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006253\n",
      "avg_cost 1.8732128047943126\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2959054\n",
      "avg_cost 1.8752999914776207\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2895584\n",
      "avg_cost 1.8773814082145703\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031592\n",
      "avg_cost 1.879475189339032\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072388\n",
      "avg_cost 1.8815726791728637\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295512\n",
      "avg_cost 1.8836595082283032\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072746\n",
      "avg_cost 1.8857570305737594\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290409\n",
      "avg_cost 1.8878392206538819\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018007\n",
      "avg_cost 1.8899317667701039\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3128355\n",
      "avg_cost 1.8920343444564136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047695\n",
      "avg_cost 1.8941295894709511\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316317\n",
      "avg_cost 1.8962353322722705\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013124\n",
      "avg_cost 1.8983274344964471\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2841606\n",
      "avg_cost 1.9004039441455505\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991672\n",
      "avg_cost 1.90249409610575\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029826\n",
      "avg_cost 1.9045877166227871\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304716\n",
      "avg_cost 1.906682913086632\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2925587\n",
      "avg_cost 1.9087670573321265\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304452\n",
      "avg_cost 1.910862013643439\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064778\n",
      "avg_cost 1.9129588116299032\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032508\n",
      "avg_cost 1.9150526759841235\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297319\n",
      "avg_cost 1.917141147743573\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29457\n",
      "avg_cost 1.919227120442825\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994046\n",
      "avg_cost 1.9213174882802107\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.301757\n",
      "avg_cost 1.923409994732251\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021367\n",
      "avg_cost 1.9255028462409984\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310169\n",
      "avg_cost 1.9276029998605912\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952547\n",
      "avg_cost 1.9296895950490789\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3161833\n",
      "avg_cost 1.931795216256923\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063977\n",
      "avg_cost 1.9338919414173483\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021004\n",
      "avg_cost 1.935984759980983\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962792\n",
      "avg_cost 1.9380722865191382\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302034\n",
      "avg_cost 1.9401650446111516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017068\n",
      "avg_cost 1.9422575053301734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308067\n",
      "avg_cost 1.9443557481332268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063984\n",
      "avg_cost 1.9464524739438847\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3143253\n",
      "avg_cost 1.948556406064468\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2947342\n",
      "avg_cost 1.9506425281004482\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064044\n",
      "avg_cost 1.9527392593297102\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002746\n",
      "avg_cost 1.954830418066546\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29264\n",
      "avg_cost 1.9569146362218\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2947805\n",
      "avg_cost 1.9590008003061479\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992523\n",
      "avg_cost 1.9610910296440136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3091602\n",
      "avg_cost 1.9631902662190535\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3091497\n",
      "avg_cost 1.9652894932573501\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2822864\n",
      "avg_cost 1.9673642990805895\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303898\n",
      "avg_cost 1.9694587518952118\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3252969\n",
      "avg_cost 1.9715726581486797\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038476\n",
      "avg_cost 1.9736670650135395\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949193\n",
      "avg_cost 1.97575335524299\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3149335\n",
      "avg_cost 1.9778578402779325\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086724\n",
      "avg_cost 1.9799566333944154\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038726\n",
      "avg_cost 1.9820510630174124\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289203\n",
      "avg_cost 1.9841321565888153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067646\n",
      "avg_cost 1.9862292153185073\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29264\n",
      "avg_cost 1.9883134334737613\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3198662\n",
      "avg_cost 1.9904224027286885\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288968\n",
      "avg_cost 1.992503282807091\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2765615\n",
      "avg_cost 1.9945728841694927\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2916658\n",
      "avg_cost 1.9966562167080975\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289563\n",
      "avg_cost 1.9987376375631862\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307475\n",
      "avg_cost 2.0008353421904834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3191102\n",
      "avg_cost 2.0029436241496703\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29211\n",
      "avg_cost 2.0050273604826505\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3162072\n",
      "avg_cost 2.007133003364911\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013842\n",
      "avg_cost 2.0092251708290805\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29961\n",
      "avg_cost 2.0113157252831906\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312917\n",
      "avg_cost 2.013418377096004\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036606\n",
      "avg_cost 2.0155126140334403\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311707\n",
      "avg_cost 2.0176141658696274\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.327016\n",
      "avg_cost 2.0197296350652536\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887928\n",
      "avg_cost 2.0218103558366964\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963455\n",
      "avg_cost 2.023897942629729\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011765\n",
      "avg_cost 2.025989921309733\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3133738\n",
      "avg_cost 2.0280929884043624\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3208911\n",
      "avg_cost 2.0302028894424455\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014715\n",
      "avg_cost 2.032295136234979\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976263\n",
      "avg_cost 2.034383887377654\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015728\n",
      "avg_cost 2.0364762262864566\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2786586\n",
      "avg_cost 2.0385477341305145\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2918975\n",
      "avg_cost 2.0406312773444455\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040411\n",
      "avg_cost 2.0427258602055653\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308499\n",
      "avg_cost 2.044824495749042\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2890353\n",
      "avg_cost 2.0469054369492983\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3114104\n",
      "avg_cost 2.049006719155747\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989876\n",
      "avg_cost 2.051096707907592\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30502\n",
      "avg_cost 2.0531921807202447\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996655\n",
      "avg_cost 2.0552827856757445\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294049\n",
      "avg_cost 2.0573682847890007\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304408\n",
      "avg_cost 2.0594632012193874\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999635\n",
      "avg_cost 2.061554077105091\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304181\n",
      "avg_cost 2.0636487871950346\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994056\n",
      "avg_cost 2.065739155899397\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989738\n",
      "avg_cost 2.067829132080081\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004067\n",
      "avg_cost 2.069920410893183\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907946\n",
      "avg_cost 2.0720029514486167\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3027709\n",
      "avg_cost 2.0740963794968375\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314012\n",
      "avg_cost 2.0762000268155907\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3142092\n",
      "avg_cost 2.0783038533817666\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2947242\n",
      "avg_cost 2.080389966314492\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.325956\n",
      "avg_cost 2.08250447186557\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107297\n",
      "avg_cost 2.0846051352674335\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906125\n",
      "avg_cost 2.086687510230327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3180108\n",
      "avg_cost 2.088794792782179\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3033142\n",
      "avg_cost 2.0908887147903465\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062327\n",
      "avg_cost 2.0929852899638113\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103168\n",
      "avg_cost 2.095085577964785\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2854786\n",
      "avg_cost 2.0971632857756206\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031874\n",
      "avg_cost 2.0992570924758938\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971928\n",
      "avg_cost 2.101345449577681\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039315\n",
      "avg_cost 2.103439932736486\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301008\n",
      "avg_cost 2.105531758178367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3036447\n",
      "avg_cost 2.107625980593944\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299091\n",
      "avg_cost 2.1097160634127556\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999542\n",
      "avg_cost 2.111806930845437\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30312\n",
      "avg_cost 2.1139006762071118\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054018\n",
      "avg_cost 2.115996496027169\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063767\n",
      "avg_cost 2.118093202114108\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049865\n",
      "avg_cost 2.1201886443658338\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3144855\n",
      "avg_cost 2.1222927221384946\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297181\n",
      "avg_cost 2.1243810684030735\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3070679\n",
      "avg_cost 2.1264784028313404\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3128104\n",
      "avg_cost 2.128580957759513\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013752\n",
      "avg_cost 2.1306731169874045\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2866793\n",
      "avg_cost 2.132751916321844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296932\n",
      "avg_cost 2.1348400363055173\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308168\n",
      "avg_cost 2.1369383707913516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2917418\n",
      "avg_cost 2.1390217724713443\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141813\n",
      "avg_cost 2.1411255736784534\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058138\n",
      "avg_cost 2.143221768032424\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2864962\n",
      "avg_cost 2.145300400907346\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961316\n",
      "avg_cost 2.1473877932808647\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158164\n",
      "avg_cost 2.1494930809194424\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2876482\n",
      "avg_cost 2.1515727611021593\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063304\n",
      "avg_cost 2.153669425140731\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069887\n",
      "avg_cost 2.155766687609936\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3165758\n",
      "avg_cost 2.157872665578672\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306295\n",
      "avg_cost 2.159969297322363\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30357\n",
      "avg_cost 2.1620634518970174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035097\n",
      "avg_cost 2.1641575516353986\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963264\n",
      "avg_cost 2.166245121088898\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037858\n",
      "avg_cost 2.1683394718170197\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081887\n",
      "avg_cost 2.170437825159596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304795\n",
      "avg_cost 2.1725330933657587\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988782\n",
      "avg_cost 2.174622982632033\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3094206\n",
      "avg_cost 2.1767224558916984\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024733\n",
      "avg_cost 2.178815613443204\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2922695\n",
      "avg_cost 2.180899494778029\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981124\n",
      "avg_cost 2.182988687862052\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092883\n",
      "avg_cost 2.1850880408287074\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3000898\n",
      "avg_cost 2.187179031588817\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3112884\n",
      "avg_cost 2.189280202822254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026283\n",
      "avg_cost 2.1913735012574653\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117988\n",
      "avg_cost 2.193475136540155\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309617\n",
      "avg_cost 2.195574788397011\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299637\n",
      "avg_cost 2.1976653675599556\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003054\n",
      "avg_cost 2.1997565542567883\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023705\n",
      "avg_cost 2.2018496183915595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029494\n",
      "avg_cost 2.203943208781158\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041236\n",
      "avg_cost 2.206037866635758\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3140616\n",
      "avg_cost 2.2081415590372973\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3101645\n",
      "avg_cost 2.210241708538751\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053963\n",
      "avg_cost 2.2123375233736926\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296061\n",
      "avg_cost 2.214424851590939\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301625\n",
      "avg_cost 2.216517237966713\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2911203\n",
      "avg_cost 2.2186000745946735\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.284201\n",
      "avg_cost 2.2206766208735402\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067107\n",
      "avg_cost 2.2227736306190513\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3145177\n",
      "avg_cost 2.224877737652174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077962\n",
      "avg_cost 2.226975734233858\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052382\n",
      "avg_cost 2.2290714053674194\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.282148\n",
      "avg_cost 2.2311460852623\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303165\n",
      "avg_cost 2.233239871588622\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954297\n",
      "avg_cost 2.2353266258673252\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005314\n",
      "avg_cost 2.237418018037624\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2842376\n",
      "avg_cost 2.239494597695092\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2905478\n",
      "avg_cost 2.2415769139203174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017557\n",
      "avg_cost 2.243669419071893\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2913842\n",
      "avg_cost 2.2457524956356414\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943048\n",
      "avg_cost 2.2478382273153845\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298411\n",
      "avg_cost 2.2499276917630997\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306323\n",
      "avg_cost 2.252024349082602\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2921038\n",
      "avg_cost 2.254108079780234\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3206599\n",
      "avg_cost 2.256217770576479\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317815\n",
      "avg_cost 2.2583248751813736\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313528\n",
      "avg_cost 2.2604280825094762\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2894592\n",
      "avg_cost 2.262509409080854\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2918065\n",
      "avg_cost 2.264592869498515\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2922451\n",
      "avg_cost 2.2666767287254355\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987516\n",
      "avg_cost 2.2687665029005593\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993696\n",
      "avg_cost 2.270856838876553\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071516\n",
      "avg_cost 2.272954249382021\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3110244\n",
      "avg_cost 2.2750551806796704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307799\n",
      "avg_cost 2.277153179862285\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906733\n",
      "avg_cost 2.27923561009494\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3127728\n",
      "avg_cost 2.281338130777535\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289414\n",
      "avg_cost 2.2834194161675216\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980535\n",
      "avg_cost 2.2855085557157366\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3188195\n",
      "avg_cost 2.287616573463789\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3112283\n",
      "avg_cost 2.2897176900776977\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923043\n",
      "avg_cost 2.2918016030571704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932794\n",
      "avg_cost 2.293886402520269\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074832\n",
      "avg_cost 2.295984114516868\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009262\n",
      "avg_cost 2.2980758656155005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3027506\n",
      "avg_cost 2.300169275240467\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3132327\n",
      "avg_cost 2.3022722140225524\n",
      "---------------------\n",
      "Epoch: 3 cost = 2.302 test accuracy: 0.113\n",
      "====================================\n",
      "epoch 3\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994158\n",
      "avg_cost 0.00209037802436135\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020484\n",
      "avg_cost 0.004183149337768555\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970479\n",
      "avg_cost 0.006271374659104781\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309914\n",
      "avg_cost 0.008371296579187566\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066149\n",
      "avg_cost 0.010468219193545254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958562\n",
      "avg_cost 0.012555361227555708\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032284\n",
      "avg_cost 0.014649205207824707\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2935317\n",
      "avg_cost 0.016734233986247668\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2822013\n",
      "avg_cost 0.018808962431820956\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2788363\n",
      "avg_cost 0.020880631750280206\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301988\n",
      "avg_cost 0.022973348010670053\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986891\n",
      "avg_cost 0.02506306539882313\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3124757\n",
      "avg_cost 0.02716531601819125\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089774\n",
      "avg_cost 0.02926438635045832\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021293\n",
      "avg_cost 0.03135723114013672\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290962\n",
      "avg_cost 0.03343992384997281\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078218\n",
      "avg_cost 0.03553794362328269\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2734122\n",
      "avg_cost 0.037604682012037795\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3114572\n",
      "avg_cost 0.03970600670034235\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309932\n",
      "avg_cost 0.041805944876237344\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081686\n",
      "avg_cost 0.04390428001230413\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986066\n",
      "avg_cost 0.04599392240697687\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314922\n",
      "avg_cost 0.04809839703819968\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3249807\n",
      "avg_cost 0.05021201588890769\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292022\n",
      "avg_cost 0.05229567224329168\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108442\n",
      "avg_cost 0.05439643968235362\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954047\n",
      "avg_cost 0.056483171202919694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304041\n",
      "avg_cost 0.058577753847295584\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095748\n",
      "avg_cost 0.060677367340434676\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051522\n",
      "avg_cost 0.06277296022935347\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980752\n",
      "avg_cost 0.06486211950128729\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958117\n",
      "avg_cost 0.06694922100413929\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026571\n",
      "avg_cost 0.06904254566539417\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035007\n",
      "avg_cost 0.0711366371674971\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3176708\n",
      "avg_cost 0.07324361064217307\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950442\n",
      "avg_cost 0.07533001444556496\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3207884\n",
      "avg_cost 0.07743982206691395\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046174\n",
      "avg_cost 0.07953492879867553\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2836628\n",
      "avg_cost 0.08161098588596691\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012366\n",
      "avg_cost 0.08370301918549972\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3277826\n",
      "avg_cost 0.08581918521360918\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317206\n",
      "avg_cost 0.08792573603716763\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938159\n",
      "avg_cost 0.09001102317463268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3057337\n",
      "avg_cost 0.09210714470256459\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285872\n",
      "avg_cost 0.09418521014126865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079505\n",
      "avg_cost 0.09628334695642646\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956982\n",
      "avg_cost 0.09837034528905696\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298417\n",
      "avg_cost 0.10045981537212026\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141482\n",
      "avg_cost 0.10256358645179056\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2927194\n",
      "avg_cost 0.1046478767828508\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293128\n",
      "avg_cost 0.1067325386134061\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024912\n",
      "avg_cost 0.10882571242072367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021357\n",
      "avg_cost 0.11091856306249447\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3148594\n",
      "avg_cost 0.11302298069000245\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972727\n",
      "avg_cost 0.11511141040108422\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043334\n",
      "avg_cost 0.11720625899054789\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022904\n",
      "avg_cost 0.11929925029928036\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302083\n",
      "avg_cost 0.12139205304059117\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2953267\n",
      "avg_cost 0.123478713685816\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932081\n",
      "avg_cost 0.12556344834241\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301931\n",
      "avg_cost 0.12765611280094494\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985687\n",
      "avg_cost 0.1297457207332958\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3148582\n",
      "avg_cost 0.13185013727708297\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976558\n",
      "avg_cost 0.13393891529603438\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300552\n",
      "avg_cost 0.13603032610633156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032196\n",
      "avg_cost 0.13812416206706654\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036005\n",
      "avg_cost 0.14021834438497371\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303544\n",
      "avg_cost 0.1423124753345143\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014572\n",
      "avg_cost 0.14440470912239772\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021808\n",
      "avg_cost 0.1464976007288153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.277987\n",
      "avg_cost 0.1485684980045666\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2929454\n",
      "avg_cost 0.15065299380909317\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043363\n",
      "avg_cost 0.1527478449994868\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934077\n",
      "avg_cost 0.1548327610709451\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108492\n",
      "avg_cost 0.15693353306163446\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993896\n",
      "avg_cost 0.1590238872441379\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035116\n",
      "avg_cost 0.16111798871647234\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3072877\n",
      "avg_cost 0.16321552298285752\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083565\n",
      "avg_cost 0.1653140289133246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3119457\n",
      "avg_cost 0.16741579771041878\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300261\n",
      "avg_cost 0.16950694409283734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972915\n",
      "avg_cost 0.17159539092670797\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117244\n",
      "avg_cost 0.17369695858521905\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2967675\n",
      "avg_cost 0.1757849290154198\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084507\n",
      "avg_cost 0.1778835205598312\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289056\n",
      "avg_cost 0.1799644806168297\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.280459\n",
      "avg_cost 0.1820376250960611\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001661\n",
      "avg_cost 0.18412868521430287\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302441\n",
      "avg_cost 0.18622181328860207\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301835\n",
      "avg_cost 0.18831439061598354\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3149722\n",
      "avg_cost 0.19041891076348055\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025095\n",
      "avg_cost 0.19251210126009866\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301827\n",
      "avg_cost 0.1946046712181786\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044555\n",
      "avg_cost 0.1966996307806536\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017724\n",
      "avg_cost 0.19879215110432027\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951467\n",
      "avg_cost 0.20087864810770217\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313588\n",
      "avg_cost 0.20298190983858985\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022418\n",
      "avg_cost 0.20507485693151312\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2928095\n",
      "avg_cost 0.2071592291918669\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972307\n",
      "avg_cost 0.20924762075597603\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3219855\n",
      "avg_cost 0.21135851664976654\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294059\n",
      "avg_cost 0.21344402486627767\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066149\n",
      "avg_cost 0.21554094748063535\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3182147\n",
      "avg_cost 0.2176484153487467\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3114295\n",
      "avg_cost 0.2197497148947284\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051095\n",
      "avg_cost 0.22184526898644202\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2870073\n",
      "avg_cost 0.22392436656084944\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049116\n",
      "avg_cost 0.22601974075490794\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051825\n",
      "avg_cost 0.2281153611703354\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043833\n",
      "avg_cost 0.2302102550593291\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025122\n",
      "avg_cost 0.232303447940133\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990105\n",
      "avg_cost 0.23439345749941756\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968562\n",
      "avg_cost 0.23648150855844688\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3059406\n",
      "avg_cost 0.23857781822031213\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2955942\n",
      "avg_cost 0.24066472205248765\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288114\n",
      "avg_cost 0.24274482575329887\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976108\n",
      "avg_cost 0.24483356280760352\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052895\n",
      "avg_cost 0.2469292805411601\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293094\n",
      "avg_cost 0.2490139113773001\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076446\n",
      "avg_cost 0.25111177010969704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141356\n",
      "avg_cost 0.25321552970192673\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3111587\n",
      "avg_cost 0.2553165830265394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035293\n",
      "avg_cost 0.2574107005379419\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29435\n",
      "avg_cost 0.25949647318233166\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2840831\n",
      "avg_cost 0.2615729123895821\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305916\n",
      "avg_cost 0.26366919972679853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303842\n",
      "avg_cost 0.2657636016065426\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2859433\n",
      "avg_cost 0.26784173185175136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3027086\n",
      "avg_cost 0.2699351033297454\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2877395\n",
      "avg_cost 0.27201486652547685\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923467\n",
      "avg_cost 0.2740988180854106\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288853\n",
      "avg_cost 0.2761795934763824\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047338\n",
      "avg_cost 0.27827480597929544\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092852\n",
      "avg_cost 0.2803741561282767\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2868354\n",
      "avg_cost 0.28245309743014274\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31034\n",
      "avg_cost 0.2845534064553003\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035345\n",
      "avg_cost 0.2866475287350744\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042395\n",
      "avg_cost 0.2887422919273379\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3033233\n",
      "avg_cost 0.29083622217178373\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006165\n",
      "avg_cost 0.29292769171974903\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083358\n",
      "avg_cost 0.2950261787934739\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3149626\n",
      "avg_cost 0.2971306902712044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3181977\n",
      "avg_cost 0.2992381427504802\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3100924\n",
      "avg_cost 0.3013382267951968\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3110213\n",
      "avg_cost 0.3034391552751717\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2884097\n",
      "avg_cost 0.3055195277387448\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2859004\n",
      "avg_cost 0.30759761897000426\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292327\n",
      "avg_cost 0.3096815525401725\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050928\n",
      "avg_cost 0.31177709145979476\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013902\n",
      "avg_cost 0.3138692643425684\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078737\n",
      "avg_cost 0.3159673313661058\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085513\n",
      "avg_cost 0.3180660143765539\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305063\n",
      "avg_cost 0.3201615262031558\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2965815\n",
      "avg_cost 0.3222493275729095\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3122072\n",
      "avg_cost 0.3243513341383503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294227\n",
      "avg_cost 0.3264369949427521\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2889085\n",
      "avg_cost 0.3285178208351138\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2846396\n",
      "avg_cost 0.3305947659232403\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985504\n",
      "avg_cost 0.3326843571662906\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3113446\n",
      "avg_cost 0.3347855795513503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076727\n",
      "avg_cost 0.3368834638595584\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2916493\n",
      "avg_cost 0.33896678144281595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990043\n",
      "avg_cost 0.34105678536675227\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292597\n",
      "avg_cost 0.343140964508057\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295032\n",
      "avg_cost 0.34522735725749654\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.29935\n",
      "avg_cost 0.34731767546046893\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.319409\n",
      "avg_cost 0.34942622900009185\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302847\n",
      "avg_cost 0.35151972618970034\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016279\n",
      "avg_cost 0.35361211516640434\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301835\n",
      "avg_cost 0.35570469249378583\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309413\n",
      "avg_cost 0.3578041588176384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2808497\n",
      "avg_cost 0.35987765854055265\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3196173\n",
      "avg_cost 0.36198640151457384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902308\n",
      "avg_cost 0.36406842947006257\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130097\n",
      "avg_cost 0.36617116559635543\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3096828\n",
      "avg_cost 0.3682708772746003\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2878356\n",
      "avg_cost 0.3703507278182293\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992818\n",
      "avg_cost 0.37244098403237114\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117008\n",
      "avg_cost 0.3745425302332101\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3080425\n",
      "avg_cost 0.37664075071161474\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2859817\n",
      "avg_cost 0.3787189158526337\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074985\n",
      "avg_cost 0.3808166417208588\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295849\n",
      "avg_cost 0.3829037772525443\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2914402\n",
      "avg_cost 0.38498690475117103\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964234\n",
      "avg_cost 0.38707456241954485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3157957\n",
      "avg_cost 0.3891798312013802\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2853842\n",
      "avg_cost 0.39125745318152716\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2998204\n",
      "avg_cost 0.3933481990207328\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005908\n",
      "avg_cost 0.39543964516032853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285488\n",
      "avg_cost 0.3975173614241863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991624\n",
      "avg_cost 0.3996075090495026\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2743118\n",
      "avg_cost 0.401675065213984\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3165176\n",
      "avg_cost 0.40378099029714437\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992373\n",
      "avg_cost 0.40587120598012777\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.283686\n",
      "avg_cost 0.4079472840916029\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2873878\n",
      "avg_cost 0.41002672758969416\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029435\n",
      "avg_cost 0.4121203125606886\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305265\n",
      "avg_cost 0.4142160079695964\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957249\n",
      "avg_cost 0.4163030305775731\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297204\n",
      "avg_cost 0.418391397866336\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977216\n",
      "avg_cost 0.42048023570667636\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946749\n",
      "avg_cost 0.42256630377336\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314431\n",
      "avg_cost 0.4246703319116074\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071868\n",
      "avg_cost 0.4267677744952117\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310188\n",
      "avg_cost 0.42886794545433754\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093026\n",
      "avg_cost 0.4309673114256427\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309818\n",
      "avg_cost 0.43306714599782786\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2935395\n",
      "avg_cost 0.4351521819288082\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2899141\n",
      "avg_cost 0.43723392204804873\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3259857\n",
      "avg_cost 0.439348454475403\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309615\n",
      "avg_cost 0.44144810438156146\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3116596\n",
      "avg_cost 0.44354961308566027\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019724\n",
      "avg_cost 0.44564231525767956\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3186991\n",
      "avg_cost 0.44775022354992977\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2914171\n",
      "avg_cost 0.4498333300243727\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3136525\n",
      "avg_cost 0.4519366504929285\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2803168\n",
      "avg_cost 0.4540096657926389\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023596\n",
      "avg_cost 0.4561027199571786\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2921484\n",
      "avg_cost 0.4581864911859689\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042927\n",
      "avg_cost 0.4602813027121807\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141952\n",
      "avg_cost 0.4623851164904511\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2852135\n",
      "avg_cost 0.4644625832817775\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3102305\n",
      "avg_cost 0.4665627928213643\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985575\n",
      "avg_cost 0.4686523905667395\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909098\n",
      "avg_cost 0.47073503580960396\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902288\n",
      "avg_cost 0.4728170620311394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301636\n",
      "avg_cost 0.47490945837714493\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991974\n",
      "avg_cost 0.47699963786385313\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078687\n",
      "avg_cost 0.4790977003357631\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3244665\n",
      "avg_cost 0.4812108516693119\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077111\n",
      "avg_cost 0.4833087708733302\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005648\n",
      "avg_cost 0.48540019338781215\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996907\n",
      "avg_cost 0.4874908213181932\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2778423\n",
      "avg_cost 0.48956158703023767\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3189223\n",
      "avg_cost 0.49166969819502426\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2767706\n",
      "avg_cost 0.49373948964205683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2933912\n",
      "avg_cost 0.49582439075816787\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3138375\n",
      "avg_cost 0.497927879420194\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993934\n",
      "avg_cost 0.500018237070604\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3139691\n",
      "avg_cost 0.502121845375408\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056638\n",
      "avg_cost 0.5042179033973002\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3070464\n",
      "avg_cost 0.5063152183185926\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045068\n",
      "avg_cost 0.5084102244810627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304089\n",
      "avg_cost 0.5105048509077594\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.28728\n",
      "avg_cost 0.5125841964374891\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291294\n",
      "avg_cost 0.514667191071944\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2967384\n",
      "avg_cost 0.5167551350593569\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2929177\n",
      "avg_cost 0.5188396057215606\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.327281\n",
      "avg_cost 0.5209553157199516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307482\n",
      "avg_cost 0.5230530266328295\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295022\n",
      "avg_cost 0.5251394102790142\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.282899\n",
      "avg_cost 0.5272147729180079\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30888\n",
      "avg_cost 0.5293137548186565\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.304184\n",
      "avg_cost 0.5314084675095301\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085382\n",
      "avg_cost 0.5335071385990492\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314267\n",
      "avg_cost 0.5356110176173126\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3147542\n",
      "avg_cost 0.5377153396606448\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010647\n",
      "avg_cost 0.5398072166876362\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296034\n",
      "avg_cost 0.5418945204127921\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2835429\n",
      "avg_cost 0.5439704684777695\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084447\n",
      "avg_cost 0.5460690546035769\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052597\n",
      "avg_cost 0.5481647452441132\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3061934\n",
      "avg_cost 0.550261284654791\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075821\n",
      "avg_cost 0.5523590866002173\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044295\n",
      "avg_cost 0.5544540225375786\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2847173\n",
      "avg_cost 0.5565310382843021\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2918944\n",
      "avg_cost 0.558614578680559\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3213623\n",
      "avg_cost 0.5607249080051079\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3121498\n",
      "avg_cost 0.5628268623352054\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085883\n",
      "avg_cost 0.5649255789409987\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3194683\n",
      "avg_cost 0.5670341864499182\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066013\n",
      "avg_cost 0.5691310967098586\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294551\n",
      "avg_cost 0.5712170520695776\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3167818\n",
      "avg_cost 0.5733232173052705\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010402\n",
      "avg_cost 0.5754150720076131\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086925\n",
      "avg_cost 0.5775138833306056\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3061645\n",
      "avg_cost 0.5796103965152397\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2945557\n",
      "avg_cost 0.581696356209842\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983701\n",
      "avg_cost 0.5837857835943052\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103983\n",
      "avg_cost 0.5858861457217827\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064024\n",
      "avg_cost 0.5879828752170912\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296337\n",
      "avg_cost 0.590070454207334\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047678\n",
      "avg_cost 0.5921656977046623\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305522\n",
      "avg_cost 0.5942616267637777\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037436\n",
      "avg_cost 0.5963559391281825\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306845\n",
      "avg_cost 0.5984530709006574\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126202\n",
      "avg_cost 0.6005554528669882\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985222\n",
      "avg_cost 0.6026450185342273\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046417\n",
      "avg_cost 0.6047401473738935\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990723\n",
      "avg_cost 0.6068302130699162\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983332\n",
      "avg_cost 0.6089196068590342\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299289\n",
      "avg_cost 0.611009869575501\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312048\n",
      "avg_cost 0.613111731355841\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314327\n",
      "avg_cost 0.6152156649936333\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3059194\n",
      "avg_cost 0.6173119553652681\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001916\n",
      "avg_cost 0.6194030386751352\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996874\n",
      "avg_cost 0.621493663571098\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3035147\n",
      "avg_cost 0.6235877678611066\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008974\n",
      "avg_cost 0.6256794927336957\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301713\n",
      "avg_cost 0.6277719590880658\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950323\n",
      "avg_cost 0.6298583520542496\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3113112\n",
      "avg_cost 0.6319595440951264\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2889535\n",
      "avg_cost 0.6340404109521349\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038752\n",
      "avg_cost 0.6361348429593177\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30257\n",
      "avg_cost 0.6382280885089531\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020277\n",
      "avg_cost 0.6403208409656181\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043635\n",
      "avg_cost 0.6424157168648463\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007846\n",
      "avg_cost 0.6445073392174464\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126\n",
      "avg_cost 0.6466097027605233\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2915282\n",
      "avg_cost 0.6486929102377461\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3170586\n",
      "avg_cost 0.6507993271134119\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082\n",
      "avg_cost 0.652897690642964\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3124344\n",
      "avg_cost 0.6549999037655919\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3233368\n",
      "avg_cost 0.6571120281652972\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986033\n",
      "avg_cost 0.6592016675255516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013482\n",
      "avg_cost 0.6612938022613527\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887795\n",
      "avg_cost 0.6633745108951223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988265\n",
      "avg_cost 0.6654643531279132\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015597\n",
      "avg_cost 0.6675566801157866\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066897\n",
      "avg_cost 0.6696536707878115\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2955346\n",
      "avg_cost 0.6717405204339463\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024042\n",
      "avg_cost 0.6738336151296445\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923577\n",
      "avg_cost 0.6759175766598097\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3112142\n",
      "avg_cost 0.6780186804858123\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056147\n",
      "avg_cost 0.680114693858407\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077643\n",
      "avg_cost 0.6822126613963736\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036902\n",
      "avg_cost 0.684306925210086\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301138\n",
      "avg_cost 0.6863988687775353\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923448\n",
      "avg_cost 0.6884828186035158\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2862334\n",
      "avg_cost 0.6905612126263707\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2802322\n",
      "avg_cost 0.6926341509819033\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304417\n",
      "avg_cost 0.6947290754318239\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030326\n",
      "avg_cost 0.6968227414651352\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067923\n",
      "avg_cost 0.69891982533715\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3048646\n",
      "avg_cost 0.7010151568326084\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310025\n",
      "avg_cost 0.7031151795387269\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938275\n",
      "avg_cost 0.7052004772966559\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2917507\n",
      "avg_cost 0.7072838869961826\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3059459\n",
      "avg_cost 0.7093802014264194\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987838\n",
      "avg_cost 0.7114700048620052\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.315443\n",
      "avg_cost 0.7135749530792238\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3104885\n",
      "avg_cost 0.7156753971359948\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3033109\n",
      "avg_cost 0.7177693161097441\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951953\n",
      "avg_cost 0.7198558573289353\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045003\n",
      "avg_cost 0.721950857639313\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313107\n",
      "avg_cost 0.7240536821972242\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032115\n",
      "avg_cost 0.7261475107886577\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887115\n",
      "avg_cost 0.7282281576503409\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055184\n",
      "avg_cost 0.7303240834582938\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30318\n",
      "avg_cost 0.7324178834394978\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906594\n",
      "avg_cost 0.7345003011009913\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2927065\n",
      "avg_cost 0.7365845797278667\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289263\n",
      "avg_cost 0.7386657279187986\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300678\n",
      "avg_cost 0.7407572533867579\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997448\n",
      "avg_cost 0.742847930518064\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118362\n",
      "avg_cost 0.7449495998295874\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305678\n",
      "avg_cost 0.7470456706393852\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2895927\n",
      "avg_cost 0.7491271185874943\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3180878\n",
      "avg_cost 0.751234471147711\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2930326\n",
      "avg_cost 0.7533190462806012\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3139586\n",
      "avg_cost 0.7554226450486621\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047657\n",
      "avg_cost 0.757517886595293\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293372\n",
      "avg_cost 0.7596027701551268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932155\n",
      "avg_cost 0.7616875115307898\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118093\n",
      "avg_cost 0.7637891563502229\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295796\n",
      "avg_cost 0.7658762435479602\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3198671\n",
      "avg_cost 0.767985213669864\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019822\n",
      "avg_cost 0.770077924728394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2947738\n",
      "avg_cost 0.7721640827439052\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060083\n",
      "avg_cost 0.7742604539611128\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3298051\n",
      "avg_cost 0.7763784586299555\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950034\n",
      "avg_cost 0.7784648253700955\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295136\n",
      "avg_cost 0.7805513126199901\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973232\n",
      "avg_cost 0.7826397882808344\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949705\n",
      "avg_cost 0.78472612511028\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982528\n",
      "avg_cost 0.7868154458566151\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2894905\n",
      "avg_cost 0.7888968008214783\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3120677\n",
      "avg_cost 0.7909986805915838\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971363\n",
      "avg_cost 0.7930869863250045\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2835402\n",
      "avg_cost 0.7951629320057962\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079734\n",
      "avg_cost 0.7972610896283937\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2901614\n",
      "avg_cost 0.799343054511331\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2838302\n",
      "avg_cost 0.8014192637530247\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3185089\n",
      "avg_cost 0.8035269990834331\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923286\n",
      "avg_cost 0.8056109341708104\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3183935\n",
      "avg_cost 0.8077185645970439\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990756\n",
      "avg_cost 0.809808633327485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951899\n",
      "avg_cost 0.8118951695615604\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3218398\n",
      "avg_cost 0.8140059330246674\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3211424\n",
      "avg_cost 0.8161160625110981\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293567\n",
      "avg_cost 0.8182011233676572\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983816\n",
      "avg_cost 0.8202905611558402\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085003\n",
      "avg_cost 0.8223891977830374\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297999\n",
      "avg_cost 0.8244782876968392\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989857\n",
      "avg_cost 0.8265682747147308\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298017\n",
      "avg_cost 0.8286573811010889\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293775\n",
      "avg_cost 0.8307426311753021\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2820518\n",
      "avg_cost 0.8328172237222853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306425\n",
      "avg_cost 0.8349139738082894\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2882378\n",
      "avg_cost 0.8369941899993211\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020928\n",
      "avg_cost 0.8390870016271426\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3033235\n",
      "avg_cost 0.8411809320883326\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2896595\n",
      "avg_cost 0.8432624407248072\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004715\n",
      "avg_cost 0.8453537784923215\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3210835\n",
      "avg_cost 0.847463854442944\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2928884\n",
      "avg_cost 0.8495482984456157\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3162603\n",
      "avg_cost 0.8516539896618245\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088143\n",
      "avg_cost 0.8537529117410841\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2926743\n",
      "avg_cost 0.8558371611074976\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949924\n",
      "avg_cost 0.8579235178774062\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023415\n",
      "avg_cost 0.8600165555693895\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3113806\n",
      "avg_cost 0.8621178106828178\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014526\n",
      "avg_cost 0.8642100403525621\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297461\n",
      "avg_cost 0.8662986412915324\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3168843\n",
      "avg_cost 0.8684048997272152\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082175\n",
      "avg_cost 0.8705032792958354\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037539\n",
      "avg_cost 0.8725976009802392\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117447\n",
      "avg_cost 0.8746991870620041\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3140628\n",
      "avg_cost 0.8768028805472641\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.286367\n",
      "avg_cost 0.8788813959468501\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3165133\n",
      "avg_cost 0.8809873171286156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2904546\n",
      "avg_cost 0.8830695486068731\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302333\n",
      "avg_cost 0.8851625787128108\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314582\n",
      "avg_cost 0.8872667442668574\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986665\n",
      "avg_cost 0.889356441064315\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2889519\n",
      "avg_cost 0.8914373064041143\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031433\n",
      "avg_cost 0.8935310730067172\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2869875\n",
      "avg_cost 0.8956101525913591\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2871275\n",
      "avg_cost 0.8976893594048246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011427\n",
      "avg_cost 0.8997813073071573\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2884512\n",
      "avg_cost 0.9018617174842147\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298744\n",
      "avg_cost 0.9039514847235253\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3091483\n",
      "avg_cost 0.9060507104613571\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305703\n",
      "avg_cost 0.908146804029292\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008363\n",
      "avg_cost 0.9102384734153754\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076763\n",
      "avg_cost 0.912336360974746\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962692\n",
      "avg_cost 0.9144238784096465\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2866952\n",
      "avg_cost 0.9165026922659447\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2860117\n",
      "avg_cost 0.9185808847167282\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007908\n",
      "avg_cost 0.9206725127046765\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949915\n",
      "avg_cost 0.9227588686076085\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302334\n",
      "avg_cost 0.9248518995805227\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3157098\n",
      "avg_cost 0.9269570903344595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3281112\n",
      "avg_cost 0.9290735550360253\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.279523\n",
      "avg_cost 0.9311458485776735\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3101113\n",
      "avg_cost 0.9332459497451789\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980013\n",
      "avg_cost 0.9353350418264222\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958956\n",
      "avg_cost 0.9374222196232196\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3145468\n",
      "avg_cost 0.9395263530991301\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022592\n",
      "avg_cost 0.9416193160143773\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024445\n",
      "avg_cost 0.943712447339839\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3116632\n",
      "avg_cost 0.9458139592951001\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961302\n",
      "avg_cost 0.9479013503681537\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2821815\n",
      "avg_cost 0.9499760608239615\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981248\n",
      "avg_cost 0.9520652651786812\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29826\n",
      "avg_cost 0.9541545924273411\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2783537\n",
      "avg_cost 0.9562258230556149\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085217\n",
      "avg_cost 0.9583244791897868\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041773\n",
      "avg_cost 0.9604191858118238\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303513\n",
      "avg_cost 0.9625132885846233\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023891\n",
      "avg_cost 0.9646063696254391\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3120217\n",
      "avg_cost 0.9667082075639212\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3186145\n",
      "avg_cost 0.9688160389119935\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300257\n",
      "avg_cost 0.9709071816097613\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288626\n",
      "avg_cost 0.9729877506602901\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062615\n",
      "avg_cost 0.9750843520597985\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2901163\n",
      "avg_cost 0.977166275978089\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308204\n",
      "avg_cost 0.9792646431922919\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3264914\n",
      "avg_cost 0.9813796353340155\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988424\n",
      "avg_cost 0.9834694920886653\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029165\n",
      "avg_cost 0.9855630525675694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126671\n",
      "avg_cost 0.9876654772325002\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317478\n",
      "avg_cost 0.9897722753611484\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943301\n",
      "avg_cost 0.9918580300157727\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968295\n",
      "avg_cost 0.9939460567994558\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024158\n",
      "avg_cost 0.9960391621156179\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3096752\n",
      "avg_cost 0.9981388668580495\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3136666\n",
      "avg_cost 1.000242200114511\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984955\n",
      "avg_cost 1.002331741506404\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074338\n",
      "avg_cost 1.004429408636961\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3143716\n",
      "avg_cost 1.0065333828059118\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003473\n",
      "avg_cost 1.0086246076497172\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30514\n",
      "avg_cost 1.0107201894846836\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3205044\n",
      "avg_cost 1.0128297389637346\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314365\n",
      "avg_cost 1.0149337070638489\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3146715\n",
      "avg_cost 1.0170379538969565\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288761\n",
      "avg_cost 1.0191186456246815\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3145525\n",
      "avg_cost 1.021222784302452\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963703\n",
      "avg_cost 1.0233103936368775\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985847\n",
      "avg_cost 1.0254000160910872\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3028855\n",
      "avg_cost 1.0274935483932501\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3070784\n",
      "avg_cost 1.0295908923582604\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026977\n",
      "avg_cost 1.031684253866023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075345\n",
      "avg_cost 1.0337820124626167\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313696\n",
      "avg_cost 1.0358853723786101\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966788\n",
      "avg_cost 1.0379732621799822\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025467\n",
      "avg_cost 1.0400664864886897\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977462\n",
      "avg_cost 1.0421553466536788\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304007\n",
      "avg_cost 1.0442498985203836\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292131\n",
      "avg_cost 1.04633365392685\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004973\n",
      "avg_cost 1.048425015102734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038087\n",
      "avg_cost 1.0505193866382951\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993565\n",
      "avg_cost 1.0526097106933598\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292873\n",
      "avg_cost 1.0546941406076609\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30454\n",
      "avg_cost 1.0567891768975695\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938507\n",
      "avg_cost 1.0588744956796823\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958517\n",
      "avg_cost 1.0609616335955536\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991168\n",
      "avg_cost 1.0630517398227348\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2947013\n",
      "avg_cost 1.0651378319480205\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963247\n",
      "avg_cost 1.0672253998843109\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071148\n",
      "avg_cost 1.069322777011178\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010535\n",
      "avg_cost 1.0714146438511938\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039007\n",
      "avg_cost 1.073509099050002\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085918\n",
      "avg_cost 1.0756078189069578\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025799\n",
      "avg_cost 1.0777010733431038\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.319886\n",
      "avg_cost 1.0798100605877965\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2871048\n",
      "avg_cost 1.0818892468105665\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051097\n",
      "avg_cost 1.0839848011190243\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126287\n",
      "avg_cost 1.0860871908881449\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3045316\n",
      "avg_cost 1.088182219592008\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3101847\n",
      "avg_cost 1.0902823875167156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032448\n",
      "avg_cost 1.092376246452332\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311349\n",
      "avg_cost 1.0944774727387865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989185\n",
      "avg_cost 1.096567398634824\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993472\n",
      "avg_cost 1.0986577142368665\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2874136\n",
      "avg_cost 1.1007371811433273\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29749\n",
      "avg_cost 1.1028258083083413\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289041\n",
      "avg_cost 1.1049067547104576\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977915\n",
      "avg_cost 1.1069956560568377\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2904449\n",
      "avg_cost 1.1090778786485846\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3147707\n",
      "avg_cost 1.1111822156472642\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042116\n",
      "avg_cost 1.1132769534804605\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2875721\n",
      "avg_cost 1.1153565645217896\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2945604\n",
      "avg_cost 1.117442528551275\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030427\n",
      "avg_cost 1.1195362036878411\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3174207\n",
      "avg_cost 1.1216429497978904\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2959404\n",
      "avg_cost 1.1237301683425902\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029563\n",
      "avg_cost 1.1258237650177694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2911615\n",
      "avg_cost 1.1279066391424697\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307692\n",
      "avg_cost 1.130004541006955\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3144054\n",
      "avg_cost 1.132108545953577\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308462\n",
      "avg_cost 1.134207147684964\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098\n",
      "avg_cost 1.1363069657845928\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039248\n",
      "avg_cost 1.1384014428745615\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996483\n",
      "avg_cost 1.1404920322244816\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980568\n",
      "avg_cost 1.1425811748071149\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078337\n",
      "avg_cost 1.144679205417633\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292478\n",
      "avg_cost 1.1467632764036004\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006005\n",
      "avg_cost 1.1488547314297068\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086596\n",
      "avg_cost 1.150953512842005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051302\n",
      "avg_cost 1.1530490857904607\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040056\n",
      "avg_cost 1.1551436363567005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297255\n",
      "avg_cost 1.157232050028714\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3218188\n",
      "avg_cost 1.1593427944183348\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019888\n",
      "avg_cost 1.1614355115457013\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040226\n",
      "avg_cost 1.1635300775007766\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2880087\n",
      "avg_cost 1.165610085400668\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.318012\n",
      "avg_cost 1.1677173690362408\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973998\n",
      "avg_cost 1.1698059142719612\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297062\n",
      "avg_cost 1.171894152381203\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962198\n",
      "avg_cost 1.1739816249500616\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303119\n",
      "avg_cost 1.1760753694447599\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002517\n",
      "avg_cost 1.178166507374156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3146827\n",
      "avg_cost 1.1802707643942394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300361\n",
      "avg_cost 1.182362001592462\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3213363\n",
      "avg_cost 1.1844723072918972\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296557\n",
      "avg_cost 1.1865600863370023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301546\n",
      "avg_cost 1.1886524009704584\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3210506\n",
      "avg_cost 1.1907624470103866\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2872589\n",
      "avg_cost 1.1928417732498857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298472\n",
      "avg_cost 1.1949312931841065\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2884324\n",
      "avg_cost 1.197011686238375\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995455\n",
      "avg_cost 1.199102182171561\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2875757\n",
      "avg_cost 1.2011817964640525\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026104\n",
      "avg_cost 1.2032750786434516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302843\n",
      "avg_cost 1.2053685723651535\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943397\n",
      "avg_cost 1.2074543356895442\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992518\n",
      "avg_cost 1.2095445645939216\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007686\n",
      "avg_cost 1.2116361724246627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3230743\n",
      "avg_cost 1.2137480581890447\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022306\n",
      "avg_cost 1.2158409950949924\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29641\n",
      "avg_cost 1.217928640625693\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980812\n",
      "avg_cost 1.220017805316231\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983215\n",
      "avg_cost 1.2221071884848849\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3159742\n",
      "avg_cost 1.2242126196080982\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309443\n",
      "avg_cost 1.2263121132417152\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308425\n",
      "avg_cost 1.228410681377757\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297402\n",
      "avg_cost 1.2304992285641752\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934632\n",
      "avg_cost 1.2325841951370233\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087282\n",
      "avg_cost 1.2346830389716403\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3145328\n",
      "avg_cost 1.2367871596596454\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3215868\n",
      "avg_cost 1.2388976931571956\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3186953\n",
      "avg_cost 1.241005597981539\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291127\n",
      "avg_cost 1.2430884406783358\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012364\n",
      "avg_cost 1.2451804737611245\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982962\n",
      "avg_cost 1.2472698339548973\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021145\n",
      "avg_cost 1.2493626653064376\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029475\n",
      "avg_cost 1.2514562539620828\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296634\n",
      "avg_cost 1.2535441030155523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2908235\n",
      "avg_cost 1.2556266697970297\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3145266\n",
      "avg_cost 1.2577307848496864\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025675\n",
      "avg_cost 1.259824028015136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985702\n",
      "avg_cost 1.2619136372479518\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2880404\n",
      "avg_cost 1.2639936739748168\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2891688\n",
      "avg_cost 1.2660747365518044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3068213\n",
      "avg_cost 1.268171846866607\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993162\n",
      "avg_cost 1.2702621342919083\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107083\n",
      "avg_cost 1.2723627781867974\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991836\n",
      "avg_cost 1.2744529451023443\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054318\n",
      "avg_cost 1.276548792232166\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3167944\n",
      "avg_cost 1.2786549689552995\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3156157\n",
      "avg_cost 1.280760074095292\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309108\n",
      "avg_cost 1.2828592632033604\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017287\n",
      "avg_cost 1.2849517438628453\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964704\n",
      "avg_cost 1.2870394442298192\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3129778\n",
      "avg_cost 1.2891421513123942\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310798\n",
      "avg_cost 1.2912428767030886\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2889307\n",
      "avg_cost 1.2933237227526575\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016784\n",
      "avg_cost 1.295416157679124\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125439\n",
      "avg_cost 1.2975184702873228\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037758\n",
      "avg_cost 1.2996128119121897\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2929974\n",
      "avg_cost 1.3016973549669437\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063276\n",
      "avg_cost 1.3037940164045851\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984467\n",
      "avg_cost 1.3058835133639246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3160074\n",
      "avg_cost 1.3079889746145765\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055196\n",
      "avg_cost 1.3100849015062501\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2948892\n",
      "avg_cost 1.3121711644259362\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987015\n",
      "avg_cost 1.3142608930847857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085015\n",
      "avg_cost 1.3163595307957037\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3136935\n",
      "avg_cost 1.3184628885442555\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302839\n",
      "avg_cost 1.3205563785813066\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020039\n",
      "avg_cost 1.3226491093635553\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306094\n",
      "avg_cost 1.3247455583919172\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300265\n",
      "avg_cost 1.3268367084589865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300631\n",
      "avg_cost 1.3289281912283457\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3191187\n",
      "avg_cost 1.3310364809903226\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3057375\n",
      "avg_cost 1.333132605986161\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314489\n",
      "avg_cost 1.33523668679324\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985427\n",
      "avg_cost 1.337326271100477\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007388\n",
      "avg_cost 1.3394178518381978\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083813\n",
      "avg_cost 1.3415163803100578\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304162\n",
      "avg_cost 1.3436110730604685\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021734\n",
      "avg_cost 1.345703957947817\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983038\n",
      "avg_cost 1.3477933250774028\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906837\n",
      "avg_cost 1.349875764846801\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987037\n",
      "avg_cost 1.351965495456348\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291269\n",
      "avg_cost 1.3540484673326658\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062763\n",
      "avg_cost 1.3561450821703123\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2926993\n",
      "avg_cost 1.358229354294863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938802\n",
      "avg_cost 1.3603146999532518\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960243\n",
      "avg_cost 1.362401994791897\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071074\n",
      "avg_cost 1.364499365199695\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999213\n",
      "avg_cost 1.3665902027216816\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309443\n",
      "avg_cost 1.3686896963552986\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2928612\n",
      "avg_cost 1.3707741156491358\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040197\n",
      "avg_cost 1.3728686790032811\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098013\n",
      "avg_cost 1.374968498403375\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979505\n",
      "avg_cost 1.3770575443181117\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019667\n",
      "avg_cost 1.379150241288271\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296239\n",
      "avg_cost 1.3812377311966628\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010502\n",
      "avg_cost 1.3833295950022604\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2918298\n",
      "avg_cost 1.3854130766608492\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993627\n",
      "avg_cost 1.3875034063512623\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308159\n",
      "avg_cost 1.3896017328175627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022525\n",
      "avg_cost 1.3916946896639733\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949553\n",
      "avg_cost 1.3937810126217924\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077476\n",
      "avg_cost 1.3958789649876677\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2939308\n",
      "avg_cost 1.3979643565958193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071082\n",
      "avg_cost 1.4000617276538498\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034678\n",
      "avg_cost 1.4021557892452583\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041847\n",
      "avg_cost 1.4042505025863643\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076785\n",
      "avg_cost 1.4063483920964324\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293076\n",
      "avg_cost 1.40843300667676\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296954\n",
      "avg_cost 1.4105211466008962\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2865689\n",
      "avg_cost 1.4125998455827882\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296145\n",
      "avg_cost 1.4146872500939798\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3222997\n",
      "avg_cost 1.416798431656577\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287843\n",
      "avg_cost 1.418878288919275\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064506\n",
      "avg_cost 1.4209750621969046\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021848\n",
      "avg_cost 1.423067957487973\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073661\n",
      "avg_cost 1.4251655630631876\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974644\n",
      "avg_cost 1.4272541670365764\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076546\n",
      "avg_cost 1.4293520348722282\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294205\n",
      "avg_cost 1.4314376757361669\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296759\n",
      "avg_cost 1.4335256383635777\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302118\n",
      "avg_cost 1.4356184729662804\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2853098\n",
      "avg_cost 1.4376960273222485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3101232\n",
      "avg_cost 1.439796139326962\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.305691\n",
      "avg_cost 1.4418922220576889\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963836\n",
      "avg_cost 1.4439798435297875\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295576\n",
      "avg_cost 1.4460667308894066\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.318113\n",
      "avg_cost 1.4481741064245046\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008459\n",
      "avg_cost 1.4502657844803546\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2915297\n",
      "avg_cost 1.4523489932580425\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2941346\n",
      "avg_cost 1.4544345701824533\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047109\n",
      "avg_cost 1.4565297618779267\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3213534\n",
      "avg_cost 1.4586400831829416\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039691\n",
      "avg_cost 1.4607346005873245\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3219743\n",
      "avg_cost 1.4628454862941394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301788\n",
      "avg_cost 1.4649380209229208\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961893\n",
      "avg_cost 1.4670254657485267\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956703\n",
      "avg_cost 1.4691124387220902\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2856505\n",
      "avg_cost 1.471190302805467\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981708\n",
      "avg_cost 1.47327954899181\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975209\n",
      "avg_cost 1.4753682043335652\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974496\n",
      "avg_cost 1.4774567948688158\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951374\n",
      "avg_cost 1.4795432834191753\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024185\n",
      "avg_cost 1.4816363911195232\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021882\n",
      "avg_cost 1.4837292894450098\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2849631\n",
      "avg_cost 1.485806528654965\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.331849\n",
      "avg_cost 1.4879263914715155\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.279264\n",
      "avg_cost 1.4899984496290029\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023984\n",
      "avg_cost 1.492091539122841\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2905445\n",
      "avg_cost 1.494173852313648\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2899299\n",
      "avg_cost 1.4962556067380033\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3161042\n",
      "avg_cost 1.4983611559867853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950573\n",
      "avg_cost 1.500447571711106\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023973\n",
      "avg_cost 1.5025406601212234\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312183\n",
      "avg_cost 1.5046426445787597\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920885\n",
      "avg_cost 1.5067263614047652\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3159273\n",
      "avg_cost 1.5088317498293784\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006172\n",
      "avg_cost 1.5109232200275762\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2730575\n",
      "avg_cost 1.512989635901017\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103793\n",
      "avg_cost 1.5150899806889615\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2930565\n",
      "avg_cost 1.517174577496268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3235683\n",
      "avg_cost 1.5192869123545554\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043602\n",
      "avg_cost 1.5213817852193652\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3239655\n",
      "avg_cost 1.523494481173428\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125\n",
      "avg_cost 1.5255967539007007\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3269699\n",
      "avg_cost 1.527712181047959\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.323627\n",
      "avg_cost 1.5298245692253105\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3154454\n",
      "avg_cost 1.5319295196099707\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907658\n",
      "avg_cost 1.5340120339393608\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107073\n",
      "avg_cost 1.5361126769672733\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077755\n",
      "avg_cost 1.5382106546922156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3122637\n",
      "avg_cost 1.540312712626023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.28373\n",
      "avg_cost 1.5423888308351683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3151217\n",
      "avg_cost 1.5444934868812554\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074207\n",
      "avg_cost 1.5465911420908836\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031502\n",
      "avg_cost 1.5486849149790671\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306859\n",
      "avg_cost 1.5507820595394475\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973812\n",
      "avg_cost 1.5528705878691234\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001413\n",
      "avg_cost 1.5549616254459722\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3200104\n",
      "avg_cost 1.5570707258311178\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29654\n",
      "avg_cost 1.5591584894873873\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31345\n",
      "avg_cost 1.5612616259401488\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992344\n",
      "avg_cost 1.5633518390222023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291669\n",
      "avg_cost 1.5654351743784811\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073235\n",
      "avg_cost 1.5675327411564908\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988834\n",
      "avg_cost 1.5696226351911367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958975\n",
      "avg_cost 1.5717098147218873\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305267\n",
      "avg_cost 1.5738055120814927\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022397\n",
      "avg_cost 1.5758984572237185\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089314\n",
      "avg_cost 1.5779974857243622\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2855778\n",
      "avg_cost 1.5800752837007692\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2910376\n",
      "avg_cost 1.5821580451185049\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964942\n",
      "avg_cost 1.584245767159895\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30633\n",
      "avg_cost 1.5863424307649783\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019886\n",
      "avg_cost 1.5884351476756007\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012192\n",
      "avg_cost 1.5905271651528097\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3120506\n",
      "avg_cost 1.5926290293173355\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031507\n",
      "avg_cost 1.5947228026390075\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306598\n",
      "avg_cost 1.5968197098645296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296913\n",
      "avg_cost 1.5989078125086698\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307799\n",
      "avg_cost 1.6010058116912842\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071532\n",
      "avg_cost 1.6031032237139615\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305964\n",
      "avg_cost 1.6051995546167548\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302535\n",
      "avg_cost 1.6072927683049982\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2901835\n",
      "avg_cost 1.6093747533451428\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3033984\n",
      "avg_cost 1.6114687518639998\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981658\n",
      "avg_cost 1.6135579934987154\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019128\n",
      "avg_cost 1.615650641484694\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069215\n",
      "avg_cost 1.617747842832045\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2878206\n",
      "avg_cost 1.6198276797207918\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2874687\n",
      "avg_cost 1.6219071966951542\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3065667\n",
      "avg_cost 1.624004075527191\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2982063\n",
      "avg_cost 1.6260933540084144\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2925327\n",
      "avg_cost 1.6281774746287951\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095508\n",
      "avg_cost 1.6302770662307737\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306688\n",
      "avg_cost 1.6323740553855894\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316927\n",
      "avg_cost 1.6344803526184775\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987595\n",
      "avg_cost 1.6365701339461585\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287126\n",
      "avg_cost 1.638649339459159\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.281901\n",
      "avg_cost 1.6407237948070872\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305597\n",
      "avg_cost 1.6428197921406138\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078437\n",
      "avg_cost 1.6449178318543867\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3150249\n",
      "avg_cost 1.6470223999023437\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289155\n",
      "avg_cost 1.6491034499081698\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141928\n",
      "avg_cost 1.6512072615189985\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304799\n",
      "avg_cost 1.6533025334098121\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951877\n",
      "avg_cost 1.6553890676931902\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.286549\n",
      "avg_cost 1.6574677486853167\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2925346\n",
      "avg_cost 1.6595518710396506\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004012\n",
      "avg_cost 1.661643144867637\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3028588\n",
      "avg_cost 1.6637366528944537\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004072\n",
      "avg_cost 1.665827932141044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3104541\n",
      "avg_cost 1.6679283449866555\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2908306\n",
      "avg_cost 1.670010918270458\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3192496\n",
      "avg_cost 1.6721193270249801\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2944536\n",
      "avg_cost 1.6742051939530807\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296309\n",
      "avg_cost 1.6762927475842564\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3149567\n",
      "avg_cost 1.6783972536433829\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2835004\n",
      "avg_cost 1.6804731631278993\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.282737\n",
      "avg_cost 1.6825483785976065\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3242242\n",
      "avg_cost 1.684661309719086\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052912\n",
      "avg_cost 1.6867570289698517\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303931\n",
      "avg_cost 1.6888515116951686\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2918806\n",
      "avg_cost 1.690935039520264\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2823882\n",
      "avg_cost 1.693009937893261\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3151002\n",
      "avg_cost 1.6951145744323735\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3182638\n",
      "avg_cost 1.6972220869497823\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3091629\n",
      "avg_cost 1.699321325909008\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.343716\n",
      "avg_cost 1.7014519767327747\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087215\n",
      "avg_cost 1.7035508144985552\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041158\n",
      "avg_cost 1.7056454652005981\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980533\n",
      "avg_cost 1.707734604532069\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093913\n",
      "avg_cost 1.7098340511322025\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290501\n",
      "avg_cost 1.711916324875572\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2755983\n",
      "avg_cost 1.713985050591556\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010955\n",
      "avg_cost 1.7160769555785444\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301642\n",
      "avg_cost 1.718169357343154\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072631\n",
      "avg_cost 1.7202668692848904\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30431\n",
      "avg_cost 1.722361696633426\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902615\n",
      "avg_cost 1.7244437525489118\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052692\n",
      "avg_cost 1.7265394518592145\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092916\n",
      "avg_cost 1.7286388078602881\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038669\n",
      "avg_cost 1.730733232281425\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3173058\n",
      "avg_cost 1.7328398739207878\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3248348\n",
      "avg_cost 1.734953360124068\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909873\n",
      "avg_cost 1.7370360758087855\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310022\n",
      "avg_cost 1.7391360959139741\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305962\n",
      "avg_cost 1.741232425082814\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067303\n",
      "avg_cost 1.7433294526013465\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306917\n",
      "avg_cost 1.7454266498305586\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932997\n",
      "avg_cost 1.7475114677169112\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3245647\n",
      "avg_cost 1.749624708349055\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2965653\n",
      "avg_cost 1.7517124949802056\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3168366\n",
      "avg_cost 1.753818710067056\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030987\n",
      "avg_cost 1.7559124361385003\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298866\n",
      "avg_cost 1.7580023143508223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2978308\n",
      "avg_cost 1.7600912514599893\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984126\n",
      "avg_cost 1.7621807174249136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306959\n",
      "avg_cost 1.7642779528010981\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2940617\n",
      "avg_cost 1.766363463401795\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3110325\n",
      "avg_cost 1.7684644020687457\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960818\n",
      "avg_cost 1.770551749142734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3171983\n",
      "avg_cost 1.7726582930304793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009675\n",
      "avg_cost 1.7747500816258523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3096397\n",
      "avg_cost 1.7768497540734036\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072615\n",
      "avg_cost 1.778947264497931\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954261\n",
      "avg_cost 1.7810340155254716\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990258\n",
      "avg_cost 1.7831240389563825\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103669\n",
      "avg_cost 1.7852243724736305\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312621\n",
      "avg_cost 1.787326755306938\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025477\n",
      "avg_cost 1.7894199804826223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304478\n",
      "avg_cost 1.7915149604190486\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297303\n",
      "avg_cost 1.793603417656639\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963479\n",
      "avg_cost 1.7956910066171132\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3120198\n",
      "avg_cost 1.797792842821642\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296835\n",
      "avg_cost 1.7998808745904409\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302294\n",
      "avg_cost 1.8019738691503357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984498\n",
      "avg_cost 1.8040633689273493\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287911\n",
      "avg_cost 1.8061432879621337\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2915902\n",
      "avg_cost 1.8082265517928389\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3096676\n",
      "avg_cost 1.8103262495994572\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309462\n",
      "avg_cost 1.8124257605726073\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951865\n",
      "avg_cost 1.8145122937722644\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079271\n",
      "avg_cost 1.8166104093464943\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297939\n",
      "avg_cost 1.8186994448575111\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083963\n",
      "avg_cost 1.8207979869842534\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2933912\n",
      "avg_cost 1.8228828881003645\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2910013\n",
      "avg_cost 1.8249656165729875\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3162727\n",
      "avg_cost 1.8270713190598926\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287017\n",
      "avg_cost 1.8291504255208106\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296682\n",
      "avg_cost 1.831238318139857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306089\n",
      "avg_cost 1.8333347626165915\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030512\n",
      "avg_cost 1.8354284455559475\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3099587\n",
      "avg_cost 1.8375284080071888\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2967455\n",
      "avg_cost 1.8396163584969265\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078668\n",
      "avg_cost 1.841714419234883\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3122122\n",
      "avg_cost 1.8438164303519513\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083057\n",
      "avg_cost 1.8459148901159117\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158522\n",
      "avg_cost 1.8480202102661136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993598\n",
      "avg_cost 1.8501105373555966\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902403\n",
      "avg_cost 1.8521925739808518\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2914155\n",
      "avg_cost 1.8542756789380856\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907581\n",
      "avg_cost 1.8563581863316625\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042736\n",
      "avg_cost 1.8584529805183414\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298994\n",
      "avg_cost 1.8605429751222786\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293483\n",
      "avg_cost 1.8626279596848925\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307879\n",
      "avg_cost 1.8647260314768015\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.315624\n",
      "avg_cost 1.8668311442028396\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086064\n",
      "avg_cost 1.8689298772811893\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030198\n",
      "avg_cost 1.8710235316103159\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015182\n",
      "avg_cost 1.873115820884705\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923343\n",
      "avg_cost 1.8751997611739422\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979643\n",
      "avg_cost 1.8772888196598403\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2860184\n",
      "avg_cost 1.8793670181794604\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.32013\n",
      "avg_cost 1.8814762273701757\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025956\n",
      "avg_cost 1.8835694961114366\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3180795\n",
      "avg_cost 1.8856768410856077\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304123\n",
      "avg_cost 1.8877714982899756\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014295\n",
      "avg_cost 1.8898637069355362\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313967\n",
      "avg_cost 1.8919673132896426\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046448\n",
      "avg_cost 1.894062444946983\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952445\n",
      "avg_cost 1.8961490308154716\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3175814\n",
      "avg_cost 1.8982559230110865\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2939558\n",
      "avg_cost 1.9003413373773752\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030314\n",
      "avg_cost 1.9024350023269656\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305729\n",
      "avg_cost 1.9045311195200143\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2919033\n",
      "avg_cost 1.9066146679358051\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990515\n",
      "avg_cost 1.9087047147750857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310933\n",
      "avg_cost 1.9108055630597205\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923863\n",
      "avg_cost 1.9128895505991852\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988443\n",
      "avg_cost 1.9149794090877883\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949066\n",
      "avg_cost 1.9170656878297982\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010006\n",
      "avg_cost 1.9191575065526099\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2851315\n",
      "avg_cost 1.9212348987839443\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3181312\n",
      "avg_cost 1.9233422907915987\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290558\n",
      "avg_cost 1.925424616336823\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3057263\n",
      "avg_cost 1.927520731145686\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3070235\n",
      "avg_cost 1.9296180252595387\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073995\n",
      "avg_cost 1.9317156611789363\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31148\n",
      "avg_cost 1.9338170066746805\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3065171\n",
      "avg_cost 1.9359138404239313\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3048246\n",
      "avg_cost 1.9380091355063704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30406\n",
      "avg_cost 1.9401037354902793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313409\n",
      "avg_cost 1.9422068346630448\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975798\n",
      "avg_cost 1.9442955435406082\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2788537\n",
      "avg_cost 1.9463672286813913\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988348\n",
      "avg_cost 1.948457078500228\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971146\n",
      "avg_cost 1.9505453645099298\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3277285\n",
      "avg_cost 1.9526614813371144\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979565\n",
      "avg_cost 1.954750532670455\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3133087\n",
      "avg_cost 1.956853540593928\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046856\n",
      "avg_cost 1.9589487093145201\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.329964\n",
      "avg_cost 1.9610668583349753\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2941983\n",
      "avg_cost 1.9631524931300774\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300222\n",
      "avg_cost 1.9652436039664534\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040938\n",
      "avg_cost 1.9673382347280335\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3225498\n",
      "avg_cost 1.9694496436552573\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2868981\n",
      "avg_cost 1.9715286419608382\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073409\n",
      "avg_cost 1.9736262245611718\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021033\n",
      "avg_cost 1.9757190457257363\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966423\n",
      "avg_cost 1.9778069023652516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960794\n",
      "avg_cost 1.9798942472717984\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29625\n",
      "avg_cost 1.981981747367166\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949483\n",
      "avg_cost 1.9840680640394044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303025\n",
      "avg_cost 1.9861617231369024\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299555\n",
      "avg_cost 1.9882522277398549\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015542\n",
      "avg_cost 1.9903445497426127\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.311476\n",
      "avg_cost 1.992445891553706\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312108\n",
      "avg_cost 1.994547807953575\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018932\n",
      "avg_cost 1.9966404381665321\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970529\n",
      "avg_cost 1.9987286680394958\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125916\n",
      "avg_cost 2.0008310239965272\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306971\n",
      "avg_cost 2.002928270426664\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906547\n",
      "avg_cost 2.0050106837532744\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130424\n",
      "avg_cost 2.0071134495735174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3170872\n",
      "avg_cost 2.009219892458483\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3148499\n",
      "avg_cost 2.0113243014162245\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984157\n",
      "avg_cost 2.013413770198823\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990208\n",
      "avg_cost 2.0155037890781062\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297953\n",
      "avg_cost 2.0175928371602847\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971456\n",
      "avg_cost 2.019681151346728\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3124723\n",
      "avg_cost 2.0217833989316776\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118103\n",
      "avg_cost 2.0238850446180874\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076043\n",
      "avg_cost 2.025982866720721\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3110206\n",
      "avg_cost 2.0280837945504633\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907631\n",
      "avg_cost 2.0301663064956674\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077095\n",
      "avg_cost 2.0322642241824767\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158538\n",
      "avg_cost 2.034369545849888\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997684\n",
      "avg_cost 2.036460244438866\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301825\n",
      "avg_cost 2.0385528126629926\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302092\n",
      "avg_cost 2.0406456236405814\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960236\n",
      "avg_cost 2.042732917828994\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008988\n",
      "avg_cost 2.0448246440020483\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075433\n",
      "avg_cost 2.0469224106181763\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956448\n",
      "avg_cost 2.0490093604001145\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031368\n",
      "avg_cost 2.051103121150625\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301048\n",
      "avg_cost 2.053194983005525\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2935998\n",
      "avg_cost 2.0552800737727783\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044217\n",
      "avg_cost 2.057375002557582\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2948034\n",
      "avg_cost 2.0594611874493696\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053527\n",
      "avg_cost 2.0615569626201293\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307552\n",
      "avg_cost 2.063654737255791\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301339\n",
      "avg_cost 2.06574686353857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2948158\n",
      "avg_cost 2.0678330597010537\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062189\n",
      "avg_cost 2.069929622303357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973092\n",
      "avg_cost 2.0720180851762957\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037484\n",
      "avg_cost 2.074112401875584\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957706\n",
      "avg_cost 2.0761994660984397\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298756\n",
      "avg_cost 2.0782892441749583\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003602\n",
      "avg_cost 2.0803804807229485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293851\n",
      "avg_cost 2.0824657997218052\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296433\n",
      "avg_cost 2.0845534660599454\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317437\n",
      "avg_cost 2.0866602269085974\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2899508\n",
      "avg_cost 2.088742000406439\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.281655\n",
      "avg_cost 2.0908162322911354\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123689\n",
      "avg_cost 2.0929183858091185\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009167\n",
      "avg_cost 2.095010128237985\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3162699\n",
      "avg_cost 2.09711582812396\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305295\n",
      "avg_cost 2.0992115508426323\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291318\n",
      "avg_cost 2.1012945671515033\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084643\n",
      "avg_cost 2.103393171050332\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3232818\n",
      "avg_cost 2.105505245382136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285935\n",
      "avg_cost 2.1075833680412988\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994518\n",
      "avg_cost 2.1096737787940287\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3097155\n",
      "avg_cost 2.1117735201662238\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305647\n",
      "avg_cost 2.1138695627992803\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3070822\n",
      "avg_cost 2.115966910232197\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305514\n",
      "avg_cost 2.118062832138755\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304461\n",
      "avg_cost 2.1201577966863456\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029943\n",
      "avg_cost 2.122251427823847\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135386\n",
      "avg_cost 2.124354644688693\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088236\n",
      "avg_cost 2.126453575220975\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316224\n",
      "avg_cost 2.128559233492071\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054183\n",
      "avg_cost 2.1306550682674756\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958422\n",
      "avg_cost 2.1327421975135805\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293823\n",
      "avg_cost 2.1348274911533704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3113043\n",
      "avg_cost 2.1369286769086666\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3180327\n",
      "avg_cost 2.139035979400982\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3106554\n",
      "avg_cost 2.141136575178667\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2967193\n",
      "avg_cost 2.1432245018265466\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3160245\n",
      "avg_cost 2.145329978682778\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3116112\n",
      "avg_cost 2.147431443387812\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013542\n",
      "avg_cost 2.149523583542217\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073025\n",
      "avg_cost 2.15162113124674\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023217\n",
      "avg_cost 2.153714150948958\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055584\n",
      "avg_cost 2.1558101131699305\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067424\n",
      "avg_cost 2.1579071517424153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297843\n",
      "avg_cost 2.1599960999055345\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311939\n",
      "avg_cost 2.162097862633792\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019629\n",
      "avg_cost 2.164190556136045\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966347\n",
      "avg_cost 2.166278405839747\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971573\n",
      "avg_cost 2.1683667306466536\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2931445\n",
      "avg_cost 2.170451407432556\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304541\n",
      "avg_cost 2.1725464448061858\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130412\n",
      "avg_cost 2.174649209542708\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3005617\n",
      "avg_cost 2.176740629239516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062997\n",
      "avg_cost 2.1788372653180907\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981205\n",
      "avg_cost 2.1809264657714156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034034\n",
      "avg_cost 2.1830204688419\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004196\n",
      "avg_cost 2.1851117593591867\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29551\n",
      "avg_cost 2.187198586680673\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962985\n",
      "avg_cost 2.1892861307751055\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970443\n",
      "avg_cost 2.1913743528452794\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298149\n",
      "avg_cost 2.193463579307904\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2928379\n",
      "avg_cost 2.195547977360813\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991767\n",
      "avg_cost 2.1976381379907792\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902932\n",
      "avg_cost 2.1997202227332386\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3187819\n",
      "avg_cost 2.2018282062357133\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309837\n",
      "avg_cost 2.2039280581474316\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29829\n",
      "avg_cost 2.2060174127058563\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986414\n",
      "avg_cost 2.208107086745177\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046966\n",
      "avg_cost 2.2102022654360005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003805\n",
      "avg_cost 2.2122935204072447\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071349\n",
      "avg_cost 2.2143909157406214\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3101661\n",
      "avg_cost 2.2164910667592843\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2966964\n",
      "avg_cost 2.2185789725997247\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2868307\n",
      "avg_cost 2.2206579095667074\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902715\n",
      "avg_cost 2.222739974585448\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29924\n",
      "avg_cost 2.2248301928693617\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063955\n",
      "avg_cost 2.2269269160790897\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313395\n",
      "avg_cost 2.2290300024639498\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025393\n",
      "avg_cost 2.2311232200535884\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043063\n",
      "avg_cost 2.2332180439342175\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3268979\n",
      "avg_cost 2.235333405624739\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3144832\n",
      "avg_cost 2.237437481229958\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3195977\n",
      "avg_cost 2.239546206430958\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997203\n",
      "avg_cost 2.241636861237615\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2955482\n",
      "avg_cost 2.2437237232381673\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2998283\n",
      "avg_cost 2.2458144762299304\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055668\n",
      "avg_cost 2.2479104460369483\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086512\n",
      "avg_cost 2.2500092198632005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3139906\n",
      "avg_cost 2.252112847674979\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135326\n",
      "avg_cost 2.254216059121221\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299287\n",
      "avg_cost 2.256306320103734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2915344\n",
      "avg_cost 2.258389533216305\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072364\n",
      "avg_cost 2.260487020882695\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971325\n",
      "avg_cost 2.262575323148209\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311316\n",
      "avg_cost 2.2646765195239693\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011808\n",
      "avg_cost 2.266768502105368\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2945814\n",
      "avg_cost 2.26885448520834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2998383\n",
      "avg_cost 2.2709452473033576\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305429\n",
      "avg_cost 2.2730410918322494\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051832\n",
      "avg_cost 2.2751367128979094\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2895744\n",
      "avg_cost 2.277218144156718\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994568\n",
      "avg_cost 2.2793085594610756\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047497\n",
      "avg_cost 2.2814037864858476\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999048\n",
      "avg_cost 2.283494609052487\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907612\n",
      "avg_cost 2.285577119263738\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016934\n",
      "avg_cost 2.2876695678450867\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062937\n",
      "avg_cost 2.289766198505057\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2910578\n",
      "avg_cost 2.2918489783460467\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979338\n",
      "avg_cost 2.293938009088692\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980955\n",
      "avg_cost 2.2960271867838795\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2843056\n",
      "avg_cost 2.298103828213434\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130882\n",
      "avg_cost 2.3002066356485567\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291293\n",
      "avg_cost 2.302289629199291\n",
      "---------------------\n",
      "Epoch: 4 cost = 2.302 test accuracy: 0.113\n",
      "====================================\n",
      "epoch 4\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994187\n",
      "avg_cost 0.002090380625291304\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135788\n",
      "avg_cost 0.004193634119900791\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991056\n",
      "avg_cost 0.006283730160106313\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3111439\n",
      "avg_cost 0.008384770046580923\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962792\n",
      "avg_cost 0.010472296584736219\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999306\n",
      "avg_cost 0.012563142559745097\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063068\n",
      "avg_cost 0.014659785140644421\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303962\n",
      "avg_cost 0.016754296042702416\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3229234\n",
      "avg_cost 0.018866044608029455\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29667\n",
      "avg_cost 0.02095392638986761\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310685\n",
      "avg_cost 0.023054549043828792\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943907\n",
      "avg_cost 0.025140358751470394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3155193\n",
      "avg_cost 0.02724537632682107\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2927232\n",
      "avg_cost 0.02932967012578791\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010948\n",
      "avg_cost 0.03142157446254384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039565\n",
      "avg_cost 0.033516080379486085\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960765\n",
      "avg_cost 0.035603422685102984\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974234\n",
      "avg_cost 0.037691989378495654\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085966\n",
      "avg_cost 0.0397907135703347\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970858\n",
      "avg_cost 0.041878973353992814\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2959368\n",
      "avg_cost 0.043966188647530303\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2989118\n",
      "avg_cost 0.046056108474731454\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038535\n",
      "avg_cost 0.04815052075819536\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2744493\n",
      "avg_cost 0.050218201984058734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024256\n",
      "avg_cost 0.052311316186731516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2984846\n",
      "avg_cost 0.054400847608392894\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2947078\n",
      "avg_cost 0.056486945585771046\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312878\n",
      "avg_cost 0.05858956185254184\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2854545\n",
      "avg_cost 0.0606672477722168\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975264\n",
      "avg_cost 0.0627559080990878\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298319\n",
      "avg_cost 0.06484528910030019\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297122\n",
      "avg_cost 0.06693358182907105\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.33094\n",
      "avg_cost 0.06905261820012874\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067524\n",
      "avg_cost 0.07114966587586838\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2781699\n",
      "avg_cost 0.07322072939439254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.320108\n",
      "avg_cost 0.07532991842790085\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309908\n",
      "avg_cost 0.0774298347126354\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055613\n",
      "avg_cost 0.07952579953453759\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3149443\n",
      "avg_cost 0.08163029432296755\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296591\n",
      "avg_cost 0.0837181043624878\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3206835\n",
      "avg_cost 0.08582781661640515\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053513\n",
      "avg_cost 0.0879235904866999\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3161101\n",
      "avg_cost 0.09002914515408604\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954054\n",
      "avg_cost 0.0921158773248846\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30681\n",
      "avg_cost 0.09421297723596748\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074498\n",
      "avg_cost 0.09631065888838336\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125405\n",
      "avg_cost 0.09841296846216377\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071365\n",
      "avg_cost 0.1005103653127497\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092508\n",
      "avg_cost 0.10260968425057153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3196678\n",
      "avg_cost 0.10471847317435526\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2959511\n",
      "avg_cost 0.10680570147254251\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984612\n",
      "avg_cost 0.10889521165327593\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313333\n",
      "avg_cost 0.11099824168465354\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994022\n",
      "avg_cost 0.11308860735459761\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2967975\n",
      "avg_cost 0.11517660509456287\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909908\n",
      "avg_cost 0.11725932403044266\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123455\n",
      "avg_cost 0.11936145630749788\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039951\n",
      "avg_cost 0.12145599733699451\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994742\n",
      "avg_cost 0.12354642846367575\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3101196\n",
      "avg_cost 0.12564653721722688\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115509\n",
      "avg_cost 0.1277479470859874\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024797\n",
      "avg_cost 0.12984111048958516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2914584\n",
      "avg_cost 0.13192425446076825\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2900152\n",
      "avg_cost 0.13400608647953377\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074675\n",
      "avg_cost 0.1361037841710177\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310651\n",
      "avg_cost 0.13820437604730776\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304411\n",
      "avg_cost 0.14029929507862435\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991018\n",
      "avg_cost 0.14238938765092327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303\n",
      "avg_cost 0.1444830239902843\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2940478\n",
      "avg_cost 0.14656852201981976\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2926905\n",
      "avg_cost 0.1486527861248363\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034027\n",
      "avg_cost 0.1507467885450883\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3361979\n",
      "avg_cost 0.15287060477516864\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044052\n",
      "avg_cost 0.15496551860462532\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093798\n",
      "avg_cost 0.1570649548010392\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3199842\n",
      "avg_cost 0.159174031344327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29057\n",
      "avg_cost 0.16125636772675941\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3202522\n",
      "avg_cost 0.16336568789048622\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305223\n",
      "avg_cost 0.16546134515242136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3199549\n",
      "avg_cost 0.16757039503617713\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991076\n",
      "avg_cost 0.16966049281033596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036225\n",
      "avg_cost 0.1717546950687061\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299578\n",
      "avg_cost 0.17384522047909817\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2913272\n",
      "avg_cost 0.1759282452409917\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29947\n",
      "avg_cost 0.17801867246627803\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299106\n",
      "avg_cost 0.18010876872322773\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29888\n",
      "avg_cost 0.18219865972345523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044627\n",
      "avg_cost 0.18429362578825514\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304087\n",
      "avg_cost 0.18638825026425446\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954264\n",
      "avg_cost 0.18847500150853935\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988272\n",
      "avg_cost 0.1905648443915627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006933\n",
      "avg_cost 0.19265638373114846\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093297\n",
      "avg_cost 0.19475577441128816\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2861423\n",
      "avg_cost 0.19683408563787286\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3110588\n",
      "avg_cost 0.19893504814668134\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3099422\n",
      "avg_cost 0.20103499564257535\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064027\n",
      "avg_cost 0.20313172535462812\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025901\n",
      "avg_cost 0.20522498911077325\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2791562\n",
      "avg_cost 0.207296949299899\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313667\n",
      "avg_cost 0.20940028298984872\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290577\n",
      "avg_cost 0.21148262565786186\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907736\n",
      "avg_cost 0.2135651471398093\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098497\n",
      "avg_cost 0.21566501053896814\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017807\n",
      "avg_cost 0.2177575384486805\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3102617\n",
      "avg_cost 0.2198577763817527\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994485\n",
      "avg_cost 0.22194818410006434\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2960045\n",
      "avg_cost 0.22403546094894405\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298806\n",
      "avg_cost 0.22612528454173692\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972286\n",
      "avg_cost 0.22821367415514857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3146887\n",
      "avg_cost 0.23031793659383598\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954197\n",
      "avg_cost 0.2324046817692843\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3140185\n",
      "avg_cost 0.23450833494013004\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952662\n",
      "avg_cost 0.2365949405323375\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311321\n",
      "avg_cost 0.2386961414597251\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074284\n",
      "avg_cost 0.24079380360516633\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2922976\n",
      "avg_cost 0.24287771051580254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.320503\n",
      "avg_cost 0.24498725869438862\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2900274\n",
      "avg_cost 0.24706910176710645\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296424\n",
      "avg_cost 0.24915675986896857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3240561\n",
      "avg_cost 0.25126953818581316\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088815\n",
      "avg_cost 0.2533685213869268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024747\n",
      "avg_cost 0.2554616802388971\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298957\n",
      "avg_cost 0.2575516412474892\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058517\n",
      "avg_cost 0.2596478700637817\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.28667\n",
      "avg_cost 0.2617266609451987\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951908\n",
      "avg_cost 0.2638131980462507\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3171346\n",
      "avg_cost 0.26591968406330446\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004823\n",
      "avg_cost 0.2680110315843061\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297096\n",
      "avg_cost 0.27009930068796323\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299344\n",
      "avg_cost 0.27218961347233156\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158278\n",
      "avg_cost 0.2742949115146289\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303683\n",
      "avg_cost 0.27638916882601644\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295148\n",
      "avg_cost 0.27847566691311915\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2959144\n",
      "avg_cost 0.28056286183270535\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026617\n",
      "avg_cost 0.28265619061209935\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3102307\n",
      "avg_cost 0.28475640036843036\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987444\n",
      "avg_cost 0.2868461680412292\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310405\n",
      "avg_cost 0.28894653623754324\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3184986\n",
      "avg_cost 0.2910542622479525\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949262\n",
      "avg_cost 0.2931405587629838\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3167336\n",
      "avg_cost 0.2952466802163557\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3062081\n",
      "avg_cost 0.29734323306517163\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3059924\n",
      "avg_cost 0.2994395897605202\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041978\n",
      "avg_cost 0.30153431502255523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958875\n",
      "avg_cost 0.303621485450051\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961488\n",
      "avg_cost 0.30570889342914925\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3027122\n",
      "avg_cost 0.30780226815830575\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034315\n",
      "avg_cost 0.30989629680460146\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308738\n",
      "avg_cost 0.31199514952572904\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067372\n",
      "avg_cost 0.31409218332984223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2969885\n",
      "avg_cost 0.31618035468188194\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293998\n",
      "avg_cost 0.31826580741188737\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.283176\n",
      "avg_cost 0.3203414219075983\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2928553\n",
      "avg_cost 0.32242583578283135\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025312\n",
      "avg_cost 0.3245190460031683\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019083\n",
      "avg_cost 0.32661168987100775\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2936525\n",
      "avg_cost 0.3286968285387213\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977946\n",
      "avg_cost 0.33078573270277545\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3195775\n",
      "avg_cost 0.3328944394805215\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986586\n",
      "avg_cost 0.33498412912542175\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092356\n",
      "avg_cost 0.3370834341916171\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3033547\n",
      "avg_cost 0.3391773930462924\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289715\n",
      "avg_cost 0.34125895218415697\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296935\n",
      "avg_cost 0.3433470749855042\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289992\n",
      "avg_cost 0.345428885980086\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3203204\n",
      "avg_cost 0.3475382681326433\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047767\n",
      "avg_cost 0.3496335196495057\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3134136\n",
      "avg_cost 0.3517366229404103\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307575\n",
      "avg_cost 0.35383441838351165\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007612\n",
      "avg_cost 0.35592601949518377\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990541\n",
      "avg_cost 0.3580160687186501\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29816\n",
      "avg_cost 0.3601053051515059\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296801\n",
      "avg_cost 0.3621933061426336\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987423\n",
      "avg_cost 0.364283071864735\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3183978\n",
      "avg_cost 0.3663907061923634\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310212\n",
      "avg_cost 0.3684908988259056\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.283037\n",
      "avg_cost 0.37056638695976957\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011441\n",
      "avg_cost 0.3726583361625672\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083503\n",
      "avg_cost 0.3747568364576861\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3027706\n",
      "avg_cost 0.37685026428916246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973852\n",
      "avg_cost 0.3789387963034891\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034801\n",
      "avg_cost 0.38103286916559403\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079014\n",
      "avg_cost 0.3831309613314543\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300526\n",
      "avg_cost 0.3852223485166377\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071234\n",
      "avg_cost 0.3873197334462947\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089905\n",
      "avg_cost 0.3894188156994908\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3197474\n",
      "avg_cost 0.3915276770158249\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018272\n",
      "avg_cost 0.39362024719064903\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996418\n",
      "avg_cost 0.39571083068847673\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017945\n",
      "avg_cost 0.39780337116935055\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3110511\n",
      "avg_cost 0.39990432674234583\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30399\n",
      "avg_cost 0.4019988630034709\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3211262\n",
      "avg_cost 0.40410897775129856\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004792\n",
      "avg_cost 0.40620032245462606\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041074\n",
      "avg_cost 0.4082949655706234\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006153\n",
      "avg_cost 0.4103864340348679\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2859325\n",
      "avg_cost 0.4124645545265893\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308284\n",
      "avg_cost 0.4145629945668309\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2819092\n",
      "avg_cost 0.4166374575008047\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3134482\n",
      "avg_cost 0.41874059221961296\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063316\n",
      "avg_cost 0.42083725734190525\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022254\n",
      "avg_cost 0.42293018947948124\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2936308\n",
      "avg_cost 0.42501530842347596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025858\n",
      "avg_cost 0.42710856827822613\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294956\n",
      "avg_cost 0.4291948918862778\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2910128\n",
      "avg_cost 0.43127763076262055\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297429\n",
      "avg_cost 0.4333662026578731\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036082\n",
      "avg_cost 0.43546039191159347\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973504\n",
      "avg_cost 0.4375488922812723\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3097782\n",
      "avg_cost 0.43964869065718226\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075852\n",
      "avg_cost 0.4417464954202826\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984862\n",
      "avg_cost 0.44383602835915315\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2810683\n",
      "avg_cost 0.44590972683646474\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069022\n",
      "avg_cost 0.4480069106275386\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3110743\n",
      "avg_cost 0.4501078872247177\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3000963\n",
      "avg_cost 0.4521988838369197\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311152\n",
      "avg_cost 0.45429993109269584\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976317\n",
      "avg_cost 0.4563886872204868\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2776954\n",
      "avg_cost 0.45845931941812695\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046136\n",
      "avg_cost 0.46055442268198193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312048\n",
      "avg_cost 0.46265628446232193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3109794\n",
      "avg_cost 0.4647571747953242\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994416\n",
      "avg_cost 0.4668475762280551\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2866254\n",
      "avg_cost 0.4689263265783137\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310611\n",
      "avg_cost 0.4710268820415844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117294\n",
      "avg_cost 0.4731284542517229\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3147376\n",
      "avg_cost 0.4752327611229637\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3162951\n",
      "avg_cost 0.47733848398382017\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047528\n",
      "avg_cost 0.47943371382626626\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3242097\n",
      "avg_cost 0.4815466317263517\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2911654\n",
      "avg_cost 0.4836295093189587\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2927892\n",
      "avg_cost 0.48571386315605863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072796\n",
      "avg_cost 0.4878113900531423\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047748\n",
      "avg_cost 0.4899066398360513\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3241923\n",
      "avg_cost 0.49201954191381286\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098075\n",
      "avg_cost 0.49411936694925485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2872121\n",
      "avg_cost 0.4961986507068981\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022878\n",
      "avg_cost 0.4982916396314448\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3070834\n",
      "avg_cost 0.5003889881480824\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3236465\n",
      "avg_cost 0.5025013940984553\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.319582\n",
      "avg_cost 0.5046101049943404\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054078\n",
      "avg_cost 0.5067059302330018\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946887\n",
      "avg_cost 0.5087920108708469\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294591\n",
      "avg_cost 0.5108780026435853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29185\n",
      "avg_cost 0.5129615027254278\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305278\n",
      "avg_cost 0.5150572100552646\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299727\n",
      "avg_cost 0.5171478709307584\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2863133\n",
      "avg_cost 0.5192263375629078\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308738\n",
      "avg_cost 0.5213251902840355\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300231\n",
      "avg_cost 0.5234163093566895\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071475\n",
      "avg_cost 0.5255137161775069\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2878566\n",
      "avg_cost 0.5275935857946222\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298152\n",
      "avg_cost 0.5296828148581765\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130026\n",
      "avg_cost 0.5317855444821444\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088205\n",
      "avg_cost 0.5338844721967524\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962983\n",
      "avg_cost 0.5359720160744407\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312436\n",
      "avg_cost 0.5380742307142777\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3100252\n",
      "avg_cost 0.5401742536371404\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010561\n",
      "avg_cost 0.542266122861342\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29702\n",
      "avg_cost 0.5443543228236112\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297458\n",
      "avg_cost 0.5464429209449074\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003025\n",
      "avg_cost 0.5485341050408103\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310297\n",
      "avg_cost 0.5506343750520186\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951534\n",
      "avg_cost 0.5527208781242371\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2928932\n",
      "avg_cost 0.554805326461792\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3188162\n",
      "avg_cost 0.5569133411754262\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2961733\n",
      "avg_cost 0.5590007714791733\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990842\n",
      "avg_cost 0.5610908480124042\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135571\n",
      "avg_cost 0.5631940817832949\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3171499\n",
      "avg_cost 0.565300581671975\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985923\n",
      "avg_cost 0.567390211061998\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2939594\n",
      "avg_cost 0.5694756286794491\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301031\n",
      "avg_cost 0.5715674751455135\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30391\n",
      "avg_cost 0.5736619387973441\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992947\n",
      "avg_cost 0.5757522067156707\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3215246\n",
      "avg_cost 0.5778626836429945\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002584\n",
      "avg_cost 0.5799538276412273\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.320027\n",
      "avg_cost 0.5820629431984644\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003852\n",
      "avg_cost 0.5841542025045918\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066916\n",
      "avg_cost 0.58625119491057\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086076\n",
      "avg_cost 0.5883499290726405\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303489\n",
      "avg_cost 0.5904440099542795\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108096\n",
      "avg_cost 0.5925447459654378\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2880633\n",
      "avg_cost 0.5946248034997423\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3065047\n",
      "avg_cost 0.5967216259782968\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311412\n",
      "avg_cost 0.5988229097019546\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012521\n",
      "avg_cost 0.600914957089858\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2979608\n",
      "avg_cost 0.6030040123245937\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920759\n",
      "avg_cost 0.6050877176631585\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303157\n",
      "avg_cost 0.6071814968369228\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305001\n",
      "avg_cost 0.6092769523100423\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3112624\n",
      "avg_cost 0.6113780999183659\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985764\n",
      "avg_cost 0.61346771478653\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2920012\n",
      "avg_cost 0.6155513522841718\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304821\n",
      "avg_cost 0.6176466441154485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306974\n",
      "avg_cost 0.6197438931465153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082063\n",
      "avg_cost 0.6218422625281599\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295496\n",
      "avg_cost 0.6239290770617403\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073704\n",
      "avg_cost 0.6260266865383499\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3104947\n",
      "avg_cost 0.6281271362304691\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036444\n",
      "avg_cost 0.6302213584293023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304261\n",
      "avg_cost 0.6323161411285404\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3145087\n",
      "avg_cost 0.6344202399253849\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047156\n",
      "avg_cost 0.6365154359557416\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052883\n",
      "avg_cost 0.6386111526055773\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996137\n",
      "avg_cost 0.6407017105275938\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3137186\n",
      "avg_cost 0.6428050910342827\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951505\n",
      "avg_cost 0.6448915915055713\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025243\n",
      "avg_cost 0.6469847954403275\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993188\n",
      "avg_cost 0.6490750852498146\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302764\n",
      "avg_cost 0.6511685070124544\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064654\n",
      "avg_cost 0.6532652937282221\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985206\n",
      "avg_cost 0.655354857878252\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30401\n",
      "avg_cost 0.6574494123458867\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037994\n",
      "avg_cost 0.6595437754284257\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055322\n",
      "avg_cost 0.66163971380754\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072274\n",
      "avg_cost 0.663737193237652\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2879794\n",
      "avg_cost 0.6658171744780113\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303816\n",
      "avg_cost 0.6679115527326416\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064332\n",
      "avg_cost 0.6700083101879472\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051605\n",
      "avg_cost 0.6721039106629116\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2899346\n",
      "avg_cost 0.6741856694221502\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084042\n",
      "avg_cost 0.6762842187014498\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001347\n",
      "avg_cost 0.6783752502094621\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005435\n",
      "avg_cost 0.6804666534337136\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994986\n",
      "avg_cost 0.6825571066682995\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3137455\n",
      "avg_cost 0.6846605116670788\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040147\n",
      "avg_cost 0.6867550704695967\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950408\n",
      "avg_cost 0.6888414712385703\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292448\n",
      "avg_cost 0.6909255149147733\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090262\n",
      "avg_cost 0.6930246296795938\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304921\n",
      "avg_cost 0.6951200123266746\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2956667\n",
      "avg_cost 0.6972069820490756\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305338\n",
      "avg_cost 0.6993027437816972\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316168\n",
      "avg_cost 0.7014083511179149\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020203\n",
      "avg_cost 0.7035010968555109\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2958248\n",
      "avg_cost 0.7055882102792919\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2878425\n",
      "avg_cost 0.7076680671085016\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950287\n",
      "avg_cost 0.7097544568235229\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092883\n",
      "avg_cost 0.7118538097901783\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988396\n",
      "avg_cost 0.7139436639438981\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2915685\n",
      "avg_cost 0.7160269080508844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014555\n",
      "avg_cost 0.7181191403215587\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3121018\n",
      "avg_cost 0.7202210510860795\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975423\n",
      "avg_cost 0.7223097259348094\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313041\n",
      "avg_cost 0.7244124904545876\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026395\n",
      "avg_cost 0.7265057990767744\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302053\n",
      "avg_cost 0.7285985745083207\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306404\n",
      "avg_cost 0.7306953055208384\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072975\n",
      "avg_cost 0.7327928486737342\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037963\n",
      "avg_cost 0.734887208938599\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979867\n",
      "avg_cost 0.7369762877984485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293725\n",
      "avg_cost 0.7390614923563875\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970583\n",
      "avg_cost 0.7411497272144669\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298175\n",
      "avg_cost 0.743238977302205\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2883596\n",
      "avg_cost 0.7453193042495039\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2974396\n",
      "avg_cost 0.7474078856814996\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971826\n",
      "avg_cost 0.7494962334632879\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972236\n",
      "avg_cost 0.7515846185250721\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306648\n",
      "avg_cost 0.7536815712668684\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118865\n",
      "avg_cost 0.7557832863114102\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2941697\n",
      "avg_cost 0.7578688950972129\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952278\n",
      "avg_cost 0.7599554657936102\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957885\n",
      "avg_cost 0.7620425462722784\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999651\n",
      "avg_cost 0.7641334236751909\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3088417\n",
      "avg_cost 0.7662323706800293\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056927\n",
      "avg_cost 0.7683284549279652\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.315413\n",
      "avg_cost 0.7704333758354193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013666\n",
      "avg_cost 0.7725255272605208\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135955\n",
      "avg_cost 0.7746287959272217\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092446\n",
      "avg_cost 0.7767281092296953\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999697\n",
      "avg_cost 0.7788189907507469\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015532\n",
      "avg_cost 0.780911311886528\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3223224\n",
      "avg_cost 0.7830225140398205\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115256\n",
      "avg_cost 0.7851239009336998\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3139918\n",
      "avg_cost 0.7872275298291993\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039865\n",
      "avg_cost 0.789322063055906\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066528\n",
      "avg_cost 0.7914190201325856\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.322437\n",
      "avg_cost 0.7935303265398205\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987106\n",
      "avg_cost 0.7956200634349482\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946172\n",
      "avg_cost 0.7977060790495445\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306853\n",
      "avg_cost 0.7998032181913208\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087423\n",
      "avg_cost 0.8019020748138433\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308744\n",
      "avg_cost 0.804000932953575\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2924469\n",
      "avg_cost 0.8060849755460572\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086474\n",
      "avg_cost 0.8081837459044029\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2998416\n",
      "avg_cost 0.8102745110338391\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002954\n",
      "avg_cost 0.8123656886274171\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.317233\n",
      "avg_cost 0.8144722641598101\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022363\n",
      "avg_cost 0.8165652062676176\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3106322\n",
      "avg_cost 0.8186657810211189\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3135476\n",
      "avg_cost 0.820769006122243\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301023\n",
      "avg_cost 0.8228608452190059\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984824\n",
      "avg_cost 0.8249503746899698\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021865\n",
      "avg_cost 0.8270432714982473\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3201427\n",
      "avg_cost 0.8291524921764034\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3006573\n",
      "avg_cost 0.8312439987876206\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071067\n",
      "avg_cost 0.8333413685451861\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072748\n",
      "avg_cost 0.8354388911073866\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2992876\n",
      "avg_cost 0.8375291525233883\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995012\n",
      "avg_cost 0.83961960814216\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003566\n",
      "avg_cost 0.8417108414389878\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299117\n",
      "avg_cost 0.8438009478829132\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307703\n",
      "avg_cost 0.8458988597176299\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3025403\n",
      "avg_cost 0.847992078174245\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2758317\n",
      "avg_cost 0.8500610160827644\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943919\n",
      "avg_cost 0.8521468268741268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305559\n",
      "avg_cost 0.8542427895285873\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090427\n",
      "avg_cost 0.856341919248755\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.312966\n",
      "avg_cost 0.858444615710866\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2892644\n",
      "avg_cost 0.8605257652022629\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295697\n",
      "avg_cost 0.8626127624511726\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295069\n",
      "avg_cost 0.8646991887959574\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299187\n",
      "avg_cost 0.8667893587459224\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3046215\n",
      "avg_cost 0.8688844691623347\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015862\n",
      "avg_cost 0.8709768202088103\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2887385\n",
      "avg_cost 0.8730574915625839\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053272\n",
      "avg_cost 0.8751532435417182\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098516\n",
      "avg_cost 0.8772531086748303\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305944\n",
      "avg_cost 0.8793494213711138\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3119085\n",
      "avg_cost 0.8814511563561186\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090262\n",
      "avg_cost 0.8835502711209391\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3197675\n",
      "avg_cost 0.8856591506437829\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3244658\n",
      "avg_cost 0.8877723013270992\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3109045\n",
      "avg_cost 0.8898731236024343\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2912362\n",
      "avg_cost 0.8919560655680576\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002746\n",
      "avg_cost 0.8940472243048935\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305886\n",
      "avg_cost 0.8961434843323455\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3041677\n",
      "avg_cost 0.8982381822846159\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013117\n",
      "avg_cost 0.9003302838585601\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3142562\n",
      "avg_cost 0.9024341531233361\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298792\n",
      "avg_cost 0.9045239639282233\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973084\n",
      "avg_cost 0.9066124261509295\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016958\n",
      "avg_cost 0.9087048768997199\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3128512\n",
      "avg_cost 0.9108074688911445\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081675\n",
      "avg_cost 0.9129058029434904\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300717\n",
      "avg_cost 0.9149973639574924\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296549\n",
      "avg_cost 0.91708513585004\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064764\n",
      "avg_cost 0.9191819325360391\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295141\n",
      "avg_cost 0.9212684243375611\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3071873\n",
      "avg_cost 0.9233658673546536\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999427\n",
      "avg_cost 0.9254567243836148\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304689\n",
      "avg_cost 0.9275518961386252\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996209\n",
      "avg_cost 0.9296424605629666\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970414\n",
      "avg_cost 0.9317306800322105\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994046\n",
      "avg_cost 0.9338210478695962\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014317\n",
      "avg_cost 0.9359132584658542\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049047\n",
      "avg_cost 0.9380086263743319\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2889795\n",
      "avg_cost 0.9400895168564541\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976496\n",
      "avg_cost 0.9421782892400573\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949169\n",
      "avg_cost 0.9442645773020663\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118644\n",
      "avg_cost 0.946366272189401\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3117514\n",
      "avg_cost 0.9484678643400024\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024325\n",
      "avg_cost 0.9505609848282559\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302163\n",
      "avg_cost 0.9526538601788612\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2856379\n",
      "avg_cost 0.9547317127747974\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3148212\n",
      "avg_cost 0.9568360957232394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3033133\n",
      "avg_cost 0.9589300168644304\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018658\n",
      "avg_cost 0.9610226221518089\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085938\n",
      "avg_cost 0.963121343742718\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079073\n",
      "avg_cost 0.9652194413271823\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299856\n",
      "avg_cost 0.9673102194612682\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308763\n",
      "avg_cost 0.9694090949405328\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.293256\n",
      "avg_cost 0.9714938731627036\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3121722\n",
      "avg_cost 0.9735958478667525\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985384\n",
      "avg_cost 0.9756854282725946\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993615\n",
      "avg_cost 0.9777757568792869\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075075\n",
      "avg_cost 0.9798734909837902\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3104076\n",
      "avg_cost 0.98197386156429\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3097684\n",
      "avg_cost 0.9840736510536893\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3236198\n",
      "avg_cost 0.9861860327287159\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292201\n",
      "avg_cost 0.9882698518579661\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3119292\n",
      "avg_cost 0.990371605699713\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299581\n",
      "avg_cost 0.9924621339277793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118756\n",
      "avg_cost 0.9945638390020896\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296278\n",
      "avg_cost 0.996651364456524\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2914457\n",
      "avg_cost 0.9987344969402665\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013308\n",
      "avg_cost 1.0008266158537436\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2949767\n",
      "avg_cost 1.0029129583185374\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2926269\n",
      "avg_cost 1.0049971645528626\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987192\n",
      "avg_cost 1.0070869092507801\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043296\n",
      "avg_cost 1.0091817543723371\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982714\n",
      "avg_cost 1.011271092024717\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3155334\n",
      "avg_cost 1.0133761223879734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304998\n",
      "avg_cost 1.015471575043419\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107948\n",
      "avg_cost 1.0175722976164392\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305873\n",
      "avg_cost 1.0196685457229622\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2945275\n",
      "avg_cost 1.0217544798417533\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021955\n",
      "avg_cost 1.023847384886309\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049486\n",
      "avg_cost 1.0259427926757128\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299014\n",
      "avg_cost 1.0280328054861598\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976625\n",
      "avg_cost 1.0301215895739477\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285981\n",
      "avg_cost 1.0321997540647343\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295757\n",
      "avg_cost 1.0342868059331731\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126771\n",
      "avg_cost 1.0363892397013588\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2903712\n",
      "avg_cost 1.0384713953191593\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014762\n",
      "avg_cost 1.0405636464465757\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3205254\n",
      "avg_cost 1.0426732149991131\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3224347\n",
      "avg_cost 1.0447845192389065\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077064\n",
      "avg_cost 1.0468824341080416\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2842236\n",
      "avg_cost 1.048959000977604\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002584\n",
      "avg_cost 1.0510501449758367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3121371\n",
      "avg_cost 1.0531520878184937\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308668\n",
      "avg_cost 1.0552508768168374\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957745\n",
      "avg_cost 1.0573379445075999\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296593\n",
      "avg_cost 1.0594257562810734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3067198\n",
      "avg_cost 1.0615227742628628\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932885\n",
      "avg_cost 1.0636075819622397\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3267481\n",
      "avg_cost 1.0657228075374268\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3068833\n",
      "avg_cost 1.0678199742057117\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014734\n",
      "avg_cost 1.0699122227321982\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2917778\n",
      "avg_cost 1.0719956571405596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3091505\n",
      "avg_cost 1.0740948848290888\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3037822\n",
      "avg_cost 1.076189232306048\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985847\n",
      "avg_cost 1.0782788547602578\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3155484\n",
      "avg_cost 1.0803838987783962\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309777\n",
      "avg_cost 1.0824836960705855\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311574\n",
      "avg_cost 1.0845851269635298\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058832\n",
      "avg_cost 1.0866813843900518\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020437\n",
      "avg_cost 1.0887741513685758\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299024\n",
      "avg_cost 1.0908641732822777\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123085\n",
      "avg_cost 1.0929662719639877\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975\n",
      "avg_cost 1.0950549082322565\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036027\n",
      "avg_cost 1.0971490925008611\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3048162\n",
      "avg_cost 1.0992443799972544\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303522\n",
      "avg_cost 1.101338491006332\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304855\n",
      "avg_cost 1.1034338138320239\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288775\n",
      "avg_cost 1.1055145183476545\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053937\n",
      "avg_cost 1.1076103307984102\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305229\n",
      "avg_cost 1.1097059934789495\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316158\n",
      "avg_cost 1.1118115917119125\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3079264\n",
      "avg_cost 1.1139097066359098\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299489\n",
      "avg_cost 1.1160001512007292\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024\n",
      "avg_cost 1.1180932422117764\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954876\n",
      "avg_cost 1.1201800491593112\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2930079\n",
      "avg_cost 1.1222646017508084\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125234\n",
      "avg_cost 1.1243668957190092\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2973816\n",
      "avg_cost 1.1264554244821734\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299359\n",
      "avg_cost 1.128545750921424\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2965317\n",
      "avg_cost 1.1306335069916478\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3059132\n",
      "avg_cost 1.1327297917279342\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298872\n",
      "avg_cost 1.1348196753588602\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3097923\n",
      "avg_cost 1.1369194865226757\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066146\n",
      "avg_cost 1.1390164089202892\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2964427\n",
      "avg_cost 1.1411040841449402\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997525\n",
      "avg_cost 1.1431947682120596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3082397\n",
      "avg_cost 1.1452931679378868\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130877\n",
      "avg_cost 1.1473959749395208\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3042197\n",
      "avg_cost 1.1494907201420188\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304581\n",
      "avg_cost 1.1515857937119234\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2988493\n",
      "avg_cost 1.1536756567521538\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3106837\n",
      "avg_cost 1.1557762783223942\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3048792\n",
      "avg_cost 1.1578716230392465\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3143117\n",
      "avg_cost 1.1599755428054126\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073838\n",
      "avg_cost 1.1620731644196953\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906156\n",
      "avg_cost 1.1641555422002627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301653\n",
      "avg_cost 1.1662479539351038\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2884083\n",
      "avg_cost 1.168328325098212\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009536\n",
      "avg_cost 1.1704201011224236\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3051443\n",
      "avg_cost 1.172515686858785\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952597\n",
      "avg_cost 1.1746022865989\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298158\n",
      "avg_cost 1.1766915210810585\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991319\n",
      "avg_cost 1.178781640963122\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295166\n",
      "avg_cost 1.1808681555227811\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3136187\n",
      "avg_cost 1.1829714452136657\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3085067\n",
      "avg_cost 1.1850700876929554\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3145761\n",
      "avg_cost 1.187174247828398\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943754\n",
      "avg_cost 1.1892600436644132\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3131711\n",
      "avg_cost 1.1913629265265042\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049178\n",
      "avg_cost 1.193458306355911\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987728\n",
      "avg_cost 1.1955480998212653\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288484\n",
      "avg_cost 1.1976285399090172\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299438\n",
      "avg_cost 1.1997189380905857\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2870321\n",
      "avg_cost 1.201798058206386\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056083\n",
      "avg_cost 1.2038940657268884\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014228\n",
      "avg_cost 1.2059862683036124\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058715\n",
      "avg_cost 1.2080825151096704\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2934427\n",
      "avg_cost 1.2101674630425205\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3152206\n",
      "avg_cost 1.2122722090374352\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2894676\n",
      "avg_cost 1.2143535431948587\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952774\n",
      "avg_cost 1.216440158974042\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309266\n",
      "avg_cost 1.2185394917834902\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3039963\n",
      "avg_cost 1.2206340338967077\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2925086\n",
      "avg_cost 1.222718132625928\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3102758\n",
      "avg_cost 1.2248183833469057\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3150418\n",
      "avg_cost 1.2269229667836983\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2860963\n",
      "avg_cost 1.2290012361786595\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2903645\n",
      "avg_cost 1.2310833857276235\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984564\n",
      "avg_cost 1.2331728915734737\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997901\n",
      "avg_cost 1.2352636098861707\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938192\n",
      "avg_cost 1.237348900058054\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997227\n",
      "avg_cost 1.2394395570321528\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3052163\n",
      "avg_cost 1.2415352082252513\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951946\n",
      "avg_cost 1.24362174879421\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3241806\n",
      "avg_cost 1.2457346402515077\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285087\n",
      "avg_cost 1.2478119921684279\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307428\n",
      "avg_cost 1.2499096538803807\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2947598\n",
      "avg_cost 1.2519957991079864\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055615\n",
      "avg_cost 1.2540917641466327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291067\n",
      "avg_cost 1.2561745522239005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.279954\n",
      "avg_cost 1.258247237638995\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.326679\n",
      "avg_cost 1.2603624003583749\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2899733\n",
      "avg_cost 1.2624441942301678\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.329395\n",
      "avg_cost 1.2645618260990505\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2892225\n",
      "avg_cost 1.2666429374434747\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295162\n",
      "avg_cost 1.268729448318483\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2955308\n",
      "avg_cost 1.2708162944967112\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3091307\n",
      "avg_cost 1.272915504195475\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069942\n",
      "avg_cost 1.2750127716497957\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3007848\n",
      "avg_cost 1.27710439421914\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996194\n",
      "avg_cost 1.2791949573430164\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064222\n",
      "avg_cost 1.2812917048280905\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296578\n",
      "avg_cost 1.283379502946682\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.276159\n",
      "avg_cost 1.285448738444937\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2738826\n",
      "avg_cost 1.2875159044699251\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2846339\n",
      "avg_cost 1.2895928443561917\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987282\n",
      "avg_cost 1.2916825972903876\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2750552\n",
      "avg_cost 1.2937508292631688\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2858245\n",
      "avg_cost 1.2958288515697844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301142\n",
      "avg_cost 1.2979207988218846\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076446\n",
      "avg_cost 1.3000186575542816\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3108132\n",
      "avg_cost 1.3021193968166023\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2897835\n",
      "avg_cost 1.3042010181600416\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018157\n",
      "avg_cost 1.306293577931146\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038223\n",
      "avg_cost 1.3083879618211245\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2923832\n",
      "avg_cost 1.310471946542915\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075607\n",
      "avg_cost 1.3125697289813667\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295236\n",
      "avg_cost 1.3146563072638096\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29264\n",
      "avg_cost 1.3167405254190636\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2879045\n",
      "avg_cost 1.3188204386017557\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2788045\n",
      "avg_cost 1.3208920790932412\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999723\n",
      "avg_cost 1.3229829629984786\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058915\n",
      "avg_cost 1.3250792280110464\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3126075\n",
      "avg_cost 1.3271815984899364\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026965\n",
      "avg_cost 1.3292749589139783\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2951326\n",
      "avg_cost 1.3313614431294545\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.323367\n",
      "avg_cost 1.3334735950556684\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3313851\n",
      "avg_cost 1.3355930360880781\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2994514\n",
      "avg_cost 1.3376834464073197\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2923033\n",
      "avg_cost 1.3397673585198158\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289539\n",
      "avg_cost 1.341848757700488\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.31828\n",
      "avg_cost 1.3439562849565\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3069644\n",
      "avg_cost 1.3460535253178005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3277671\n",
      "avg_cost 1.3481696772575393\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060536\n",
      "avg_cost 1.3502660896561378\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103495\n",
      "avg_cost 1.352366407351062\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938874\n",
      "avg_cost 1.3544517595117758\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3176153\n",
      "avg_cost 1.3565586824850617\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304573\n",
      "avg_cost 1.358653748902409\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2919357\n",
      "avg_cost 1.3607373267954062\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979355\n",
      "avg_cost 1.3628263590552605\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3002584\n",
      "avg_cost 1.3649175030534932\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3203585\n",
      "avg_cost 1.3670269198851166\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300153\n",
      "avg_cost 1.3691179680824295\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2873\n",
      "avg_cost 1.3711973318186688\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064673\n",
      "avg_cost 1.3732941202683897\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005133\n",
      "avg_cost 1.3753854959661325\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305332\n",
      "avg_cost 1.37748125228015\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3213663\n",
      "avg_cost 1.3795915852893497\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311291\n",
      "avg_cost 1.3816927589069727\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.314496\n",
      "avg_cost 1.3837968462163766\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.319293\n",
      "avg_cost 1.3859052944183363\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285802\n",
      "avg_cost 1.3879832961342564\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086789\n",
      "avg_cost 1.3900820951028316\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3059933\n",
      "avg_cost 1.3921784526651568\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290974\n",
      "avg_cost 1.3942611562122011\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2975883\n",
      "avg_cost 1.3963498728925545\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054113\n",
      "avg_cost 1.3984457013823783\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2926638\n",
      "avg_cost 1.4005299412120487\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2948828\n",
      "avg_cost 1.4026161982796423\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3167357\n",
      "avg_cost 1.4047223216837117\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3172815\n",
      "avg_cost 1.4068289412151698\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2877948\n",
      "avg_cost 1.408908754695547\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957869\n",
      "avg_cost 1.410995833657006\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302211\n",
      "avg_cost 1.4130887527899323\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2995768\n",
      "avg_cost 1.4151792771166036\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3105917\n",
      "avg_cost 1.4172798150235972\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054767\n",
      "avg_cost 1.4193757029013216\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019485\n",
      "avg_cost 1.4214683833989246\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077705\n",
      "avg_cost 1.4235663565722394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3161576\n",
      "avg_cost 1.425671954371714\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300967\n",
      "avg_cost 1.4277637425335987\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3158584\n",
      "avg_cost 1.4298690683191488\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2943807\n",
      "avg_cost 1.4319548689235355\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2929602\n",
      "avg_cost 1.4340393781662002\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30772\n",
      "avg_cost 1.4361373053897526\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957563\n",
      "avg_cost 1.4382243566079589\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985168\n",
      "avg_cost 1.4403139172900823\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297934\n",
      "avg_cost 1.4424029482494718\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2861187\n",
      "avg_cost 1.4444812380183845\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103852\n",
      "avg_cost 1.4465815882249329\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2900825\n",
      "avg_cost 1.4486634813655523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2987068\n",
      "avg_cost 1.4507532147927735\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3214605\n",
      "avg_cost 1.4528636334159175\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044777\n",
      "avg_cost 1.4549586131355996\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2844157\n",
      "avg_cost 1.4570353547009571\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3125353\n",
      "avg_cost 1.4591376595063659\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946382\n",
      "avg_cost 1.4612236941944485\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3014464\n",
      "avg_cost 1.4633159182288447\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036385\n",
      "avg_cost 1.4654101350090738\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036134\n",
      "avg_cost 1.4675043290311658\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990127\n",
      "avg_cost 1.4695943405411478\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063312\n",
      "avg_cost 1.4716910052299517\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005276\n",
      "avg_cost 1.4737823939323442\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984006\n",
      "avg_cost 1.4758718490600602\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308169\n",
      "avg_cost 1.4779701844128712\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3130639\n",
      "avg_cost 1.480072969740089\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2834764\n",
      "avg_cost 1.482148857333445\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996006\n",
      "avg_cost 1.4842394033345325\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083396\n",
      "avg_cost 1.486337893876164\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092961\n",
      "avg_cost 1.4884372539953767\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3208513\n",
      "avg_cost 1.4905471188371846\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310666\n",
      "avg_cost 1.492647724368357\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2979891\n",
      "avg_cost 1.494736805395648\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3129828\n",
      "avg_cost 1.4968395170298505\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3235586\n",
      "avg_cost 1.4989518430016273\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073695\n",
      "avg_cost 1.5010494516112602\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011956\n",
      "avg_cost 1.503141447630797\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2914813\n",
      "avg_cost 1.5052246124094197\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907002\n",
      "avg_cost 1.507307067134165\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3089213\n",
      "avg_cost 1.509406086531554\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.283247\n",
      "avg_cost 1.5114817656170254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010142\n",
      "avg_cost 1.5135735966942543\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073041\n",
      "avg_cost 1.5156711459159866\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064823\n",
      "avg_cost 1.5177679480205897\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2928367\n",
      "avg_cost 1.519852344989778\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2985766\n",
      "avg_cost 1.5219419600746862\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3140426\n",
      "avg_cost 1.5240456351366924\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3102238\n",
      "avg_cost 1.5261458386074427\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3092709\n",
      "avg_cost 1.528245175751774\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2767668\n",
      "avg_cost 1.5303149637309001\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2936378\n",
      "avg_cost 1.5324000889604756\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2978709\n",
      "avg_cost 1.534489062482662\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3229437\n",
      "avg_cost 1.5366008294712428\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017416\n",
      "avg_cost 1.5386933218349124\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2999663\n",
      "avg_cost 1.5407842003215457\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2845418\n",
      "avg_cost 1.5428610565445655\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963088\n",
      "avg_cost 1.544948609958997\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316664\n",
      "avg_cost 1.5470546681230735\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3111815\n",
      "avg_cost 1.5491557422551259\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3113625\n",
      "avg_cost 1.5512569808959977\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300646\n",
      "avg_cost 1.5533484773202393\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3104322\n",
      "avg_cost 1.555448870225388\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3004339\n",
      "avg_cost 1.5575401737473245\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2903986\n",
      "avg_cost 1.5596223542907037\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2847717\n",
      "avg_cost 1.5616994194550964\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123507\n",
      "avg_cost 1.5638015565005232\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2978582\n",
      "avg_cost 1.565890518535269\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024075\n",
      "avg_cost 1.5679836162653853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307121\n",
      "avg_cost 1.5700809990276006\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.291508\n",
      "avg_cost 1.5721641880815695\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294528\n",
      "avg_cost 1.574250122633849\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072464\n",
      "avg_cost 1.576347619403494\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3131058\n",
      "avg_cost 1.5784504428776844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303935\n",
      "avg_cost 1.5805449292876521\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2873135\n",
      "avg_cost 1.5826243051615645\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909849\n",
      "avg_cost 1.5847070186788401\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026073\n",
      "avg_cost 1.5868002980405649\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3074715\n",
      "avg_cost 1.5888979994166996\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056722\n",
      "avg_cost 1.5909940650246375\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3080094\n",
      "avg_cost 1.5930922553756035\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060186\n",
      "avg_cost 1.59518863591281\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305944\n",
      "avg_cost 1.5972849486090934\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288837\n",
      "avg_cost 1.5993657094782063\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005419\n",
      "avg_cost 1.6014571111852487\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2852714\n",
      "avg_cost 1.6035346306454066\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3078218\n",
      "avg_cost 1.6056326504187164\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2893174\n",
      "avg_cost 1.6077138480273174\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2795691\n",
      "avg_cost 1.6097861836173333\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3169417\n",
      "avg_cost 1.6118924942883592\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066146\n",
      "avg_cost 1.6139894166859727\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034403\n",
      "avg_cost 1.6160834533518025\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3020556\n",
      "avg_cost 1.6181762311675345\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3106961\n",
      "avg_cost 1.6202768640084713\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981834\n",
      "avg_cost 1.622366121682255\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909348\n",
      "avg_cost 1.6244487896832565\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990737\n",
      "avg_cost 1.626538856679744\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303405\n",
      "avg_cost 1.6286328612674377\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2873607\n",
      "avg_cost 1.6307122800566944\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087308\n",
      "avg_cost 1.6328111262754972\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304714\n",
      "avg_cost 1.6349063207886447\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023736\n",
      "avg_cost 1.63699938774109\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968612\n",
      "avg_cost 1.6390874433517468\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.283629\n",
      "avg_cost 1.641163469661367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296684\n",
      "avg_cost 1.6432513642311108\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2996488\n",
      "avg_cost 1.6453419540145193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3134885\n",
      "avg_cost 1.647445125363091\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306951\n",
      "avg_cost 1.6495423535867184\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976408\n",
      "avg_cost 1.6516311179507877\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031154\n",
      "avg_cost 1.6537248591943234\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2832232\n",
      "avg_cost 1.6558005166053786\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2932856\n",
      "avg_cost 1.6578853217038256\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2948532\n",
      "avg_cost 1.659971551895143\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030922\n",
      "avg_cost 1.662065272114495\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3036144\n",
      "avg_cost 1.6641594670035638\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308117\n",
      "avg_cost 1.6662577551061473\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993376\n",
      "avg_cost 1.6683480620384232\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2937784\n",
      "avg_cost 1.6704333151470547\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083563\n",
      "avg_cost 1.6725318208607776\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3112984\n",
      "avg_cost 1.6746330011974697\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306417\n",
      "avg_cost 1.6767297439141722\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2832088\n",
      "avg_cost 1.6788053883205776\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.288344\n",
      "avg_cost 1.6808857009627618\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2980802\n",
      "avg_cost 1.682974864786323\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2990777\n",
      "avg_cost 1.6850649354674614\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3030083\n",
      "avg_cost 1.687158579392868\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.285563\n",
      "avg_cost 1.6892363639311372\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3120952\n",
      "avg_cost 1.6913382686268215\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2893734\n",
      "avg_cost 1.6934195171703006\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309615\n",
      "avg_cost 1.695519167076459\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2970843\n",
      "avg_cost 1.6976074255596523\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2907672\n",
      "avg_cost 1.6996899411895074\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3151886\n",
      "avg_cost 1.7017946581407042\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298799\n",
      "avg_cost 1.7038844754479163\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2854867\n",
      "avg_cost 1.7059621906280533\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300474\n",
      "avg_cost 1.7080535305630093\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3058443\n",
      "avg_cost 1.7101497526602327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305716\n",
      "avg_cost 1.7122458581490967\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2921326\n",
      "avg_cost 1.7143296150727723\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3103738\n",
      "avg_cost 1.716429954875601\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2843838\n",
      "avg_cost 1.7185066673972407\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950354\n",
      "avg_cost 1.7205930631810986\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3072531\n",
      "avg_cost 1.7226905660195801\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311069\n",
      "avg_cost 1.7247915378483876\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304678\n",
      "avg_cost 1.7268866996331664\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2984354\n",
      "avg_cost 1.7289761864055302\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010957\n",
      "avg_cost 1.7310680916092627\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3205771\n",
      "avg_cost 1.7331777071952834\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3083467\n",
      "avg_cost 1.7352762042392398\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2911391\n",
      "avg_cost 1.737359057989989\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3076801\n",
      "avg_cost 1.739456949017266\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305249\n",
      "avg_cost 1.741552629904315\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2906613\n",
      "avg_cost 1.7436350492997617\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.323789\n",
      "avg_cost 1.7457475846463997\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968478\n",
      "avg_cost 1.7478356281193832\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3047695\n",
      "avg_cost 1.7499308731339207\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3028138\n",
      "avg_cost 1.7520243401960907\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2942452\n",
      "avg_cost 1.754110017689793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3012304\n",
      "avg_cost 1.7562020453539775\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909157\n",
      "avg_cost 1.758284696015446\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026235\n",
      "avg_cost 1.760377990115774\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3084724\n",
      "avg_cost 1.7624766013839042\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3009753\n",
      "avg_cost 1.7645683971318344\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2997723\n",
      "avg_cost 1.7666590991887192\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310841\n",
      "avg_cost 1.768759863810107\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3177962\n",
      "avg_cost 1.7708669512922126\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2883482\n",
      "avg_cost 1.7729472678357918\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3005648\n",
      "avg_cost 1.7750386903502737\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308269\n",
      "avg_cost 1.7771371167356331\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081722\n",
      "avg_cost 1.7792354551228624\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.293202\n",
      "avg_cost 1.7813201841441082\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296189\n",
      "avg_cost 1.7834076287529699\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30591\n",
      "avg_cost 1.7855039106715822\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29952\n",
      "avg_cost 1.7875943834131427\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3044744\n",
      "avg_cost 1.7896893600984065\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308097\n",
      "avg_cost 1.7917876299944804\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962499\n",
      "avg_cost 1.7938751298731037\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2805057\n",
      "avg_cost 1.795948316834191\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3136342\n",
      "avg_cost 1.7980516206134463\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300258\n",
      "avg_cost 1.8001427641781906\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3166625\n",
      "avg_cost 1.802248821041802\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016157\n",
      "avg_cost 1.8043411989645537\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3140695\n",
      "avg_cost 1.8064448985186503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3131256\n",
      "avg_cost 1.8085477399826062\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2947927\n",
      "avg_cost 1.8106339151209063\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3087654\n",
      "avg_cost 1.8127327927676127\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2965624\n",
      "avg_cost 1.8148205767978334\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3250685\n",
      "avg_cost 1.8169342754103932\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301143\n",
      "avg_cost 1.81902622352947\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3159118\n",
      "avg_cost 1.8211315978657128\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040752\n",
      "avg_cost 1.823226211721248\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29838\n",
      "avg_cost 1.825315647992222\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3023384\n",
      "avg_cost 1.827408682866531\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294896\n",
      "avg_cost 1.8294949518550536\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302689\n",
      "avg_cost 1.8315883055600264\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2927473\n",
      "avg_cost 1.8336726212501537\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3018568\n",
      "avg_cost 1.835765218301254\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954206\n",
      "avg_cost 1.837851964343679\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950346\n",
      "avg_cost 1.8399383594773044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093212\n",
      "avg_cost 1.8420377423546543\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.298354\n",
      "avg_cost 1.8441271550005145\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29848\n",
      "avg_cost 1.8462166823040367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3056283\n",
      "avg_cost 1.8483127080310486\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296649\n",
      "avg_cost 1.8504005707394005\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307794\n",
      "avg_cost 1.8524985653703874\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2998385\n",
      "avg_cost 1.8545893276821495\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302635\n",
      "avg_cost 1.8566826321861971\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2983553\n",
      "avg_cost 1.8587720461325223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304354\n",
      "avg_cost 1.860866913361984\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296493\n",
      "avg_cost 1.8629546343196532\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981412\n",
      "avg_cost 1.8650438536297202\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2976873\n",
      "avg_cost 1.8671326602589011\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3011713\n",
      "avg_cost 1.8692246341705334\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3019836\n",
      "avg_cost 1.8713173465295283\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053215\n",
      "avg_cost 1.8734130933068027\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297658\n",
      "avg_cost 1.8755018732764517\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077364\n",
      "avg_cost 1.8775998154553513\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107624\n",
      "avg_cost 1.8797005085511653\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305133\n",
      "avg_cost 1.881796084100551\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3129303\n",
      "avg_cost 1.8838987480510376\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2912865\n",
      "avg_cost 1.8859817357496793\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295186\n",
      "avg_cost 1.888068268515848\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.303582\n",
      "avg_cost 1.8901624339277106\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3216221\n",
      "avg_cost 1.892272999503397\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3174486\n",
      "avg_cost 1.8943797709725132\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2955883\n",
      "avg_cost 1.8964666693860845\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2982213\n",
      "avg_cost 1.89855596152219\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294944\n",
      "avg_cost 1.9006422742930336\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098502\n",
      "avg_cost 1.9027421381256806\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017685\n",
      "avg_cost 1.9048346549814406\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2912261\n",
      "avg_cost 1.9069175878438092\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086352\n",
      "avg_cost 1.9090163471482027\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971618\n",
      "avg_cost 1.9111046760732486\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3031888\n",
      "avg_cost 1.9131984840739866\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3022132\n",
      "avg_cost 1.9152914051576104\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29313\n",
      "avg_cost 1.917376068722119\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3132124\n",
      "avg_cost 1.9194789890809503\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302685\n",
      "avg_cost 1.9215723391012722\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909799\n",
      "avg_cost 1.9236550480669206\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3124216\n",
      "avg_cost 1.9257572494853636\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.304263\n",
      "avg_cost 1.9278520341352994\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3053467\n",
      "avg_cost 1.9299478038874551\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302713\n",
      "avg_cost 1.9320411792668442\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993326\n",
      "avg_cost 1.9341314816474926\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3093412\n",
      "avg_cost 1.9362308827313521\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2875023\n",
      "avg_cost 1.9383104302666416\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3073766\n",
      "avg_cost 1.9404080453785995\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3068871\n",
      "avg_cost 1.942505215514791\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2896998\n",
      "avg_cost 1.9445867607810292\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098333\n",
      "avg_cost 1.9466866092248407\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311463\n",
      "avg_cost 1.9487879393317493\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2874253\n",
      "avg_cost 1.9508674168586742\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3081744\n",
      "avg_cost 1.9529657571966008\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2968578\n",
      "avg_cost 1.9550538097728394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.287462\n",
      "avg_cost 1.9571333206783652\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3097374\n",
      "avg_cost 1.9592330819910233\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29678\n",
      "avg_cost 1.9613210639086647\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986887\n",
      "avg_cost 1.9634107808633294\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292943\n",
      "avg_cost 1.9654952745004144\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2928936\n",
      "avg_cost 1.9675797232714576\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3261738\n",
      "avg_cost 1.9696944267099563\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.307778\n",
      "avg_cost 1.9717924066023402\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.325399\n",
      "avg_cost 1.97390640562231\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957342\n",
      "avg_cost 1.975993436683309\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3173995\n",
      "avg_cost 1.9781001635031277\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2886393\n",
      "avg_cost 1.9801807446913295\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2952049\n",
      "avg_cost 1.9822672945802873\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3055139\n",
      "avg_cost 1.984363216270101\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141937\n",
      "avg_cost 1.9864670287479065\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3063989\n",
      "avg_cost 1.9885637549920527\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115332\n",
      "avg_cost 1.9906651488217453\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.309836\n",
      "avg_cost 1.9927649996497427\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3176305\n",
      "avg_cost 1.9948719364946552\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2944946\n",
      "avg_cost 1.9969578407027517\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2969217\n",
      "avg_cost 1.9990459513664258\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3119233\n",
      "avg_cost 2.0011476997895685\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3060968\n",
      "avg_cost 2.0032441514188606\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2972934\n",
      "avg_cost 2.0053325999866844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977805\n",
      "avg_cost 2.007421491362833\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3064985\n",
      "avg_cost 2.0095183082060393\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300731\n",
      "avg_cost 2.0116098817912027\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303726\n",
      "avg_cost 2.0137041781165395\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118773\n",
      "avg_cost 2.015805884708059\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297017\n",
      "avg_cost 2.0178940820693985\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3001575\n",
      "avg_cost 2.0199851343848505\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2981434\n",
      "avg_cost 2.0220743556456147\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3049657\n",
      "avg_cost 2.024169779040598\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3066678\n",
      "avg_cost 2.0262667497721596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.311154\n",
      "avg_cost 2.028367798761889\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3013086\n",
      "avg_cost 2.030459897518159\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2883916\n",
      "avg_cost 2.0325402535091754\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305584\n",
      "avg_cost 2.034636238921773\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2986498\n",
      "avg_cost 2.036725920547139\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.320207\n",
      "avg_cost 2.0388351997462193\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3107696\n",
      "avg_cost 2.0409358993443583\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3017173\n",
      "avg_cost 2.0430283696001235\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3040586\n",
      "avg_cost 2.045122968283567\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29274\n",
      "avg_cost 2.0472072774713697\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991822\n",
      "avg_cost 2.0492974430864512\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310249\n",
      "avg_cost 2.051397669532083\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3070312\n",
      "avg_cost 2.053494970581749\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303067\n",
      "avg_cost 2.05558866782622\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299903\n",
      "avg_cost 2.057679488658906\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.295023\n",
      "avg_cost 2.0597658731720676\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050852\n",
      "avg_cost 2.061861405155877\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954834\n",
      "avg_cost 2.0639482082020164\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3000898\n",
      "avg_cost 2.066039198962126\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.303938\n",
      "avg_cost 2.0681336879730234\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2909055\n",
      "avg_cost 2.070216329314493\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2892578\n",
      "avg_cost 2.072297472737053\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.3015833\n",
      "avg_cost 2.0743898211825984\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2936738\n",
      "avg_cost 2.0764749791405426\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2942677\n",
      "avg_cost 2.078560677008196\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305962\n",
      "avg_cost 2.080657006177036\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294889\n",
      "avg_cost 2.082743268879978\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.30245\n",
      "avg_cost 2.0848364051905555\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289384\n",
      "avg_cost 2.086917663270778\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963636\n",
      "avg_cost 2.089005266536367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3016944\n",
      "avg_cost 2.0910977159846924\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.296381\n",
      "avg_cost 2.0931853350726053\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3075998\n",
      "avg_cost 2.0952831530570997\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302431\n",
      "avg_cost 2.097376272244888\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310328\n",
      "avg_cost 2.0994765704328375\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.306848\n",
      "avg_cost 2.1015737050229863\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.313173\n",
      "avg_cost 2.1036765896190306\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302896\n",
      "avg_cost 2.1057701314579367\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2800663\n",
      "avg_cost 2.107842918959532\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2851043\n",
      "avg_cost 2.1099202864820317\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.301319\n",
      "avg_cost 2.1120123945583007\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289462\n",
      "avg_cost 2.1140937237306083\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294241\n",
      "avg_cost 2.1161793973229157\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141446\n",
      "avg_cost 2.1182831651514236\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3189275\n",
      "avg_cost 2.1203912810845815\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.302609\n",
      "avg_cost 2.1224845619635153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.292748\n",
      "avg_cost 2.124568878303875\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3095076\n",
      "avg_cost 2.12666843067516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3024268\n",
      "avg_cost 2.1287615459615536\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.305972\n",
      "avg_cost 2.1308578842336483\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.315055\n",
      "avg_cost 2.1329624795913698\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3086448\n",
      "avg_cost 2.1350612475655297\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2786164\n",
      "avg_cost 2.137132717045871\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3172696\n",
      "avg_cost 2.139239325740121\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3010075\n",
      "avg_cost 2.1413311507485133\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3003128\n",
      "avg_cost 2.1434223441644153\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2902458\n",
      "avg_cost 2.145504385774786\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2929788\n",
      "avg_cost 2.1475889119234957\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3026118\n",
      "avg_cost 2.1496821954033596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3118556\n",
      "avg_cost 2.15178388227116\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2857654\n",
      "avg_cost 2.153861850825223\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.289509\n",
      "avg_cost 2.155943222696131\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3090155\n",
      "avg_cost 2.1580423277074643\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3008866\n",
      "avg_cost 2.160134042826566\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2971854\n",
      "avg_cost 2.162222393209284\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.27387\n",
      "avg_cost 2.164289547746832\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2804682\n",
      "avg_cost 2.1663627006790858\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3133268\n",
      "avg_cost 2.1684657250751154\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.323215\n",
      "avg_cost 2.1705777387185536\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938683\n",
      "avg_cost 2.1726630735397343\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2878072\n",
      "avg_cost 2.174742898290808\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2993162\n",
      "avg_cost 2.1768331857161094\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.290416\n",
      "avg_cost 2.178915382081813\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3077192\n",
      "avg_cost 2.1810133086551327\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.318647\n",
      "avg_cost 2.1831211694804113\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2894773\n",
      "avg_cost 2.1852025125243455\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.297884\n",
      "avg_cost 2.1872914979674607\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2962186\n",
      "avg_cost 2.1893789694525987\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3032463\n",
      "avg_cost 2.19147282968868\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3248088\n",
      "avg_cost 2.1935862922668465\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2813363\n",
      "avg_cost 2.1956602343645972\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2936325\n",
      "avg_cost 2.197745354825801\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2977057\n",
      "avg_cost 2.1998341781442825\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2908113\n",
      "avg_cost 2.2019167338718075\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308407\n",
      "avg_cost 2.204015285752037\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3263795\n",
      "avg_cost 2.2061301762407486\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310087\n",
      "avg_cost 2.208230255300349\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2946742\n",
      "avg_cost 2.2103163227168006\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2894344\n",
      "avg_cost 2.2123976267467853\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3190827\n",
      "avg_cost 2.2145058837803937\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2965348\n",
      "avg_cost 2.2165936426682915\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3123088\n",
      "avg_cost 2.2186957415667456\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3038857\n",
      "avg_cost 2.2207901831106716\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3333492\n",
      "avg_cost 2.2229114096814944\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3098562\n",
      "avg_cost 2.2250112789327456\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2991676\n",
      "avg_cost 2.2271014313264335\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2950127\n",
      "avg_cost 2.229187806519596\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3171606\n",
      "avg_cost 2.2312943161617635\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3100572\n",
      "avg_cost 2.233394368128344\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3106196\n",
      "avg_cost 2.2354949313944044\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294494\n",
      "avg_cost 2.2375808349522686\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2864726\n",
      "avg_cost 2.2396594463695187\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2910573\n",
      "avg_cost 2.24174222577702\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2935839\n",
      "avg_cost 2.2438273020224146\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.316151\n",
      "avg_cost 2.2459328937530527\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3034105\n",
      "avg_cost 2.248026903325862\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957315\n",
      "avg_cost 2.2501139320026753\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2877288\n",
      "avg_cost 2.2521936854449196\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.294485\n",
      "avg_cost 2.25427958098325\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3181455\n",
      "avg_cost 2.256386985995554\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c 2.2754326\n",
      "avg_cost 2.258455561074345\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.279954\n",
      "avg_cost 2.2605282464894394\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2938404\n",
      "avg_cost 2.2626135559515532\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29554\n",
      "avg_cost 2.2647004105828037\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.299899\n",
      "avg_cost 2.266791227947583\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3029516\n",
      "avg_cost 2.268884820287879\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.29777\n",
      "avg_cost 2.2709737021272844\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115401\n",
      "avg_cost 2.2730751022425575\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3043091\n",
      "avg_cost 2.2751699287241167\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2762895\n",
      "avg_cost 2.2772392827814287\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2963724\n",
      "avg_cost 2.2793268940665516\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3115244\n",
      "avg_cost 2.2814282798767103\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3054461\n",
      "avg_cost 2.2835241400111816\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3050368\n",
      "avg_cost 2.2856196279959256\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.300286\n",
      "avg_cost 2.2877107971364814\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2957726\n",
      "avg_cost 2.2897978630932907\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3021278\n",
      "avg_cost 2.291890706582504\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.308753\n",
      "avg_cost 2.293989572958514\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3141727\n",
      "avg_cost 2.296093366362833\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.2954333\n",
      "avg_cost 2.2981801238926987\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.310347\n",
      "avg_cost 2.300280439420181\n",
      "---------------------\n",
      "batch_x (50, 784)\n",
      "batch_y (50, 10)\n",
      "c 2.3015254\n",
      "avg_cost 2.302372735196895\n",
      "---------------------\n",
      "Epoch: 5 cost = 2.302 test accuracy: 0.113\n",
      "====================================\n",
      "\n",
      "Training complete!\n",
      "0.1135\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    # initialise the variables\n",
    "    sess.run(init_op)\n",
    "    total_batch = int(len(mnist.train.labels) / batch_size)\n",
    "    print(\"total_batch\",total_batch)\n",
    "    for epoch in range(epochs):\n",
    "        print(\"epoch\",epoch)\n",
    "        avg_cost = 0\n",
    "        for i in range(total_batch):\n",
    "            batch_x, batch_y = mnist.train.next_batch(batch_size=batch_size)\n",
    "            print(\"batch_x\",batch_x.shape)\n",
    "            print(\"batch_y\", batch_y.shape)\n",
    "            _, c = sess.run([optimiser, cross_entropy], \n",
    "                            feed_dict={x: batch_x, y: batch_y})\n",
    "            \n",
    "            print(\"c\", c)\n",
    "            avg_cost += c / total_batch\n",
    "            print(\"avg_cost\", avg_cost)\n",
    "            print(\"---------------------\")\n",
    "        test_acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})\n",
    "        print(\"Epoch:\", (epoch + 1), \"cost =\", \"{:.3f}\".format(avg_cost), \"test accuracy: {:.3f}\".format(test_acc))\n",
    "        print(\"====================================\")\n",
    "\n",
    "    print(\"\\nTraining complete!\")\n",
    "    print(sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#http://adventuresinmachinelearning.com/convolutional-neural-networks-tutorial-tensorflow/"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}