Refactor ConvNet for TF1.0

Signed-off-by: Norman Heckscher <norman.heckscher@gmail.com>

Author: normanheckscher

examples/3_NeuralNetworks/convolutional_network.py

@@ -96,15 +96,15 @@ def conv_net(x, weights, biases, dropout):
 pred = conv_net(x, weights, biases, keep_prob)
 
 # Define loss and optimizer
-cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
 optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
 
 # Evaluate model
 correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
 accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
 
 # Initializing the variables
-init = tf.initialize_all_variables()
+init = tf.global_variables_initializer()
 
 # Launch the graph
 with tf.Session() as sess:

notebooks/3_NeuralNetworks/convolutional_network.ipynb

@@ -20,28 +20,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
-      "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
-      "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
-      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import tensorflow as tf\n",
     "\n",
     "# Import MNIST data\n",
     "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)"
+    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
    ]
   },
   {
@@ -150,189 +139,24 @@
     "pred = conv_net(x, weights, biases, keep_prob)\n",
     "\n",
     "# Define loss and optimizer\n",
-    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n",
+    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n",
     "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
     "\n",
     "# Evaluate model\n",
     "correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
     "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
     "\n",
     "# Initializing the variables\n",
-    "init = tf.initialize_all_variables()"
+    "init = tf.global_variables_initializer()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iter 1280, Minibatch Loss= 17231.589844, Training Accuracy= 0.25000\n",
-      "Iter 2560, Minibatch Loss= 10580.260742, Training Accuracy= 0.54688\n",
-      "Iter 3840, Minibatch Loss= 7395.362793, Training Accuracy= 0.64062\n",
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-      "Iter 6400, Minibatch Loss= 3830.062012, Training Accuracy= 0.80469\n",
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-      "Iter 186880, Minibatch Loss= 51.866646, Training Accuracy= 0.98438\n",
-      "Iter 188160, Minibatch Loss= 166.911987, Training Accuracy= 0.96875\n",
-      "Iter 189440, Minibatch Loss= 32.308147, Training Accuracy= 0.98438\n",
-      "Iter 190720, Minibatch Loss= 514.898071, Training Accuracy= 0.92188\n",
-      "Iter 192000, Minibatch Loss= 146.610031, Training Accuracy= 0.98438\n",
-      "Iter 193280, Minibatch Loss= 23.939758, Training Accuracy= 0.99219\n",
-      "Iter 194560, Minibatch Loss= 224.806641, Training Accuracy= 0.97656\n",
-      "Iter 195840, Minibatch Loss= 71.935089, Training Accuracy= 0.98438\n",
-      "Iter 197120, Minibatch Loss= 182.021210, Training Accuracy= 0.96875\n",
-      "Iter 198400, Minibatch Loss= 125.573784, Training Accuracy= 0.96875\n",
-      "Iter 199680, Minibatch Loss= 122.506104, Training Accuracy= 0.96875\n",
-      "Optimization Finished!\n",
-      "Testing Accuracy: 0.972656\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Launch the graph\n",
     "with tf.Session() as sess:\n",
@@ -361,6 +185,15 @@
     "                                      y: mnist.test.labels[:256],\n",
     "                                      keep_prob: 1.})"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -372,14 +205,14 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2.0
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,