notebooks/mlp_mnist_simple.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MULTI-LAYER PERCEPTRON ON MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PACKAGES LOADED\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "%matplotlib inline  \n",
    "print (\"PACKAGES LOADED\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOAD MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/train-images-idx3-ubyte.gz\n",
      "Extracting data/train-labels-idx1-ubyte.gz\n",
      "Extracting data/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('data/', one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEFINE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NETWORK READY\n"
     ]
    }
   ],
   "source": [
    "# NETWORK TOPOLOGIES\n",
    "n_hidden_1 = 256 \n",
    "n_hidden_2 = 128 \n",
    "n_input    = 784 \n",
    "n_classes  = 10  \n",
    "\n",
    "# INPUTS AND OUTPUTS\n",
    "x = tf.placeholder(\"float\", [None, n_input])\n",
    "y = tf.placeholder(\"float\", [None, n_classes])\n",
    "    \n",
    "# NETWORK PARAMETERS\n",
    "stddev = 0.1\n",
    "weights = {\n",
    "    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=stddev)),\n",
    "    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=stddev)),\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], stddev=stddev))\n",
    "}\n",
    "biases = {\n",
    "    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "}\n",
    "print (\"NETWORK READY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLP AS A FUNCTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def multilayer_perceptron(_X, _weights, _biases):\n",
    "    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) \n",
    "    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2']))\n",
    "    return (tf.matmul(layer_2, _weights['out']) + _biases['out'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEFINE FUNCTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FUNCTIONS READY\n"
     ]
    }
   ],
   "source": [
    "# PREDICTION\n",
    "pred = multilayer_perceptron(x, weights, biases)\n",
    "\n",
    "# LOSS AND OPTIMIZER\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) \n",
    "# optm = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(cost) \n",
    "optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost) \n",
    "corr = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))    \n",
    "accr = tf.reduce_mean(tf.cast(corr, \"float\"))\n",
    "\n",
    "# INITIALIZER\n",
    "init = tf.initialize_all_variables()\n",
    "print (\"FUNCTIONS READY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 003/020 cost: 0.124787590\n",
      "TRAIN ACCURACY: 0.960\n",
      "TEST ACCURACY: 0.961\n",
      "Epoch: 007/020 cost: 0.050386614\n",
      "TRAIN ACCURACY: 0.970\n",
      "TEST ACCURACY: 0.975\n",
      "Epoch: 011/020 cost: 0.019682230\n",
      "TRAIN ACCURACY: 1.000\n",
      "TEST ACCURACY: 0.979\n",
      "Epoch: 015/020 cost: 0.007102341\n",
      "TRAIN ACCURACY: 1.000\n",
      "TEST ACCURACY: 0.979\n",
      "Epoch: 019/020 cost: 0.002848990\n",
      "TRAIN ACCURACY: 1.000\n",
      "TEST ACCURACY: 0.979\n",
      "OPTIMIZATION FINISHED\n"
     ]
    }
   ],
   "source": [
    "# PARAMETERS\n",
    "training_epochs = 20\n",
    "batch_size      = 100\n",
    "display_step    = 4\n",
    "# LAUNCH THE GRAPH\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "# OPTIMIZE\n",
    "for epoch in range(training_epochs):\n",
    "    avg_cost = 0.\n",
    "    total_batch = int(mnist.train.num_examples/batch_size)\n",
    "    # ITERATION\n",
    "    for i in range(total_batch):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "        feeds = {x: batch_xs, y: batch_ys}\n",
    "        sess.run(optm, feed_dict=feeds)\n",
    "        avg_cost += sess.run(cost, feed_dict=feeds)\n",
    "    avg_cost = avg_cost / total_batch\n",
    "    # DISPLAY\n",
    "    if (epoch+1) % display_step == 0:\n",
    "        print (\"Epoch: %03d/%03d cost: %.9f\" % (epoch, training_epochs, avg_cost))\n",
    "        feeds = {x: batch_xs, y: batch_ys}\n",
    "        train_acc = sess.run(accr, feed_dict=feeds)\n",
    "        print (\"TRAIN ACCURACY: %.3f\" % (train_acc))\n",
    "        feeds = {x: mnist.test.images, y: mnist.test.labels}\n",
    "        test_acc = sess.run(accr, feed_dict=feeds)\n",
    "        print (\"TEST ACCURACY: %.3f\" % (test_acc))\n",
    "print (\"OPTIMIZATION FINISHED\")"
   ]
  }
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