Refactor ConvNet for TF1.0

Signed-off-by: Norman Heckscher <[email protected]>
aymericdamien · Jan 14, 2017 · 3133823 · 3133823
1 parent 53a7228
commit 3133823
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diff --git a/examples/3_NeuralNetworks/convolutional_network.py b/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:

diff --git a/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/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",
- "Iter 5120, Minibatch Loss= 4864.292480, Training Accuracy= 0.75781\n",
- "Iter 6400, Minibatch Loss= 3830.062012, Training Accuracy= 0.80469\n",
- "Iter 7680, Minibatch Loss= 6031.701172, Training Accuracy= 0.72656\n",
- "Iter 8960, Minibatch Loss= 2549.708740, Training Accuracy= 0.81250\n",
- "Iter 10240, Minibatch Loss= 2010.484985, Training Accuracy= 0.84375\n",
- "Iter 11520, Minibatch Loss= 1607.380981, Training Accuracy= 0.89062\n",
- "Iter 12800, Minibatch Loss= 1983.302856, Training Accuracy= 0.82812\n",
- "Iter 14080, Minibatch Loss= 401.215088, Training Accuracy= 0.94531\n",
- "Iter 15360, Minibatch Loss= 976.289307, Training Accuracy= 0.95312\n",
- "Iter 16640, Minibatch Loss= 1844.699951, Training Accuracy= 0.89844\n",
- "Iter 17920, Minibatch Loss= 1009.859863, Training Accuracy= 0.92969\n",
- "Iter 19200, Minibatch Loss= 1397.939453, Training Accuracy= 0.88281\n",
- "Iter 20480, Minibatch Loss= 540.369995, Training Accuracy= 0.96094\n",
- "Iter 21760, Minibatch Loss= 2589.246826, Training Accuracy= 0.87500\n",
- "Iter 23040, Minibatch Loss= 404.981293, Training Accuracy= 0.96094\n",
- "Iter 24320, Minibatch Loss= 742.155396, Training Accuracy= 0.93750\n",
- "Iter 25600, Minibatch Loss= 851.599731, Training Accuracy= 0.93750\n",
- "Iter 26880, Minibatch Loss= 1527.609619, Training Accuracy= 0.90625\n",
- "Iter 28160, Minibatch Loss= 1209.633301, Training Accuracy= 0.91406\n",
- "Iter 29440, Minibatch Loss= 1123.146851, Training Accuracy= 0.93750\n",
- "Iter 30720, Minibatch Loss= 950.860596, Training Accuracy= 0.92188\n",
- "Iter 32000, Minibatch Loss= 1217.373779, Training Accuracy= 0.92188\n",
- "Iter 33280, Minibatch Loss= 859.433105, Training Accuracy= 0.91406\n",
- "Iter 34560, Minibatch Loss= 487.426331, Training Accuracy= 0.95312\n",
- "Iter 35840, Minibatch Loss= 287.507721, Training Accuracy= 0.96875\n",
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- "Iter 38400, Minibatch Loss= 248.981216, Training Accuracy= 0.97656\n",
- "Iter 39680, Minibatch Loss= 147.081467, Training Accuracy= 0.97656\n",
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- "Iter 49920, Minibatch Loss= 459.845520, Training Accuracy= 0.94531\n",
- "Iter 51200, Minibatch Loss= 133.995087, Training Accuracy= 0.97656\n",
- "Iter 52480, Minibatch Loss= 378.886780, Training Accuracy= 0.96094\n",
- "Iter 53760, Minibatch Loss= 122.112671, Training Accuracy= 0.98438\n",
- "Iter 55040, Minibatch Loss= 357.410950, Training Accuracy= 0.96875\n",
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- "Iter 60160, Minibatch Loss= 289.819153, Training Accuracy= 0.94531\n",
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- "Iter 74240, Minibatch Loss= 124.091103, Training Accuracy= 0.97656\n",
- "Iter 75520, Minibatch Loss= 614.557251, Training Accuracy= 0.94531\n",
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- "Iter 81920, Minibatch Loss= 98.455017, Training Accuracy= 0.96875\n",
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- "Iter 85760, Minibatch Loss= 262.921265, Training Accuracy= 0.97656\n",
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- "Iter 96000, Minibatch Loss= 317.929260, Training Accuracy= 0.96875\n",
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- "Iter 180480, Minibatch Loss= 57.089508, Training Accuracy= 0.97656\n",
- "Iter 181760, Minibatch Loss= 601.268799, Training Accuracy= 0.93750\n",
- "Iter 183040, Minibatch Loss= 59.850044, Training Accuracy= 0.97656\n",
- "Iter 184320, Minibatch Loss= 145.267883, Training Accuracy= 0.96875\n",
- "Iter 185600, Minibatch Loss= 24.205322, Training Accuracy= 0.99219\n",
- "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,