<code class="language-python hljs has-numbering" style="display: block; padding: 0px; color: inherit; box-sizing: border-box; font-family: 'Source Code Pro', monospace;font-size:undefined; white-space: pre; border-radius: 0px; word-wrap: normal; background: transparent;"><span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Copyright 2015 The TensorFlow Authors. All Rights Reserved.</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;">#</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Licensed under the Apache License, Version 2.0 (the "License");</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># you may not use this file except in compliance with the License.</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># You may obtain a copy of the License at</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;">#</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;">#</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Unless required by applicable law or agreed to in writing, software</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># distributed under the License is distributed on an "AS IS" BASIS,</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># See the License for the specific language governing permissions and</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># limitations under the License.</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># ==============================================================================</span>
<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">"""Builds the MNIST network.
Implements the inference/loss/training pattern for model building.
1. inference() - Builds the model as far as is required for running the network
forward to make predictions.
2. loss() - Adds to the inference model the layers required to generate loss.
3. training() - Adds to the loss model the Ops required to generate and
apply gradients.
This file is used by the various "fully_connected_*.py" files and not meant to
be run.
"""</span>
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">from</span> __future__ <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> absolute_import
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">from</span> __future__ <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> division
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">from</span> __future__ <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> print_function
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> math
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> tensorflow <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">as</span> tf
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># The MNIST dataset has 10 classes, representing the digits 0 through 9.</span>
NUM_CLASSES = <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">10</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># The MNIST images are always 28x28 pixels.</span>
IMAGE_SIZE = <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">28</span>
IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE
<span class="hljs-function" style="box-sizing: border-box;"><span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">def</span> <span class="hljs-title" style="box-sizing: border-box;">inference</span><span class="hljs-params" style="color: rgb(102, 0, 102); box-sizing: border-box;">(images, hidden1_units, hidden2_units)</span>:</span>
<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">"""Build the MNIST model up to where it may be used for inference.
Args:
images: Images placeholder, from inputs().
hidden1_units: Size of the first hidden layer.
hidden2_units: Size of the second hidden layer.
Returns:
softmax_linear: Output tensor with the computed logits.
"""</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Hidden 1</span>
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">with</span> tf.name_scope(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'hidden1'</span>):
weights = tf.Variable(
tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
stddev=<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">1.0</span> / math.sqrt(float(IMAGE_PIXELS))),
name=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'weights'</span>)
biases = tf.Variable(tf.zeros([hidden1_units]),
name=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'biases'</span>)
hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Hidden 2</span>
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">with</span> tf.name_scope(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'hidden2'</span>):
weights = tf.Variable(
tf.truncated_normal([hidden1_units, hidden2_units],
stddev=<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">1.0</span> / math.sqrt(float(hidden1_units))),
name=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'weights'</span>)
biases = tf.Variable(tf.zeros([hidden2_units]),
name=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'biases'</span>)
hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Linear</span>
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">with</span> tf.name_scope(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'softmax_linear'</span>):
weights = tf.Variable(
tf.truncated_normal([hidden2_units, NUM_CLASSES],
stddev=<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">1.0</span> / math.sqrt(float(hidden2_units))),
name=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'weights'</span>)
biases = tf.Variable(tf.zeros([NUM_CLASSES]),
name=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'biases'</span>)
logits = tf.matmul(hidden2, weights) + biases
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">return</span> logits
<span class="hljs-function" style="box-sizing: border-box;"><span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">def</span> <span class="hljs-title" style="box-sizing: border-box;">loss</span><span class="hljs-params" style="color: rgb(102, 0, 102); box-sizing: border-box;">(logits, labels)</span>:</span>
<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">"""Calculates the loss from the logits and the labels.
Args:
logits: Logits tensor, float - [batch_size, NUM_CLASSES].
labels: Labels tensor, int32 - [batch_size].
Returns:
loss: Loss tensor of type float.
"""</span>
labels = tf.to_int64(labels)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits, labels, name=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'xentropy'</span>)
loss = tf.reduce_mean(cross_entropy, name=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'xentropy_mean'</span>)
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">return</span> loss
<span class="hljs-function" style="box-sizing: border-box;"><span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">def</span> <span class="hljs-title" style="box-sizing: border-box;">training</span><span class="hljs-params" style="color: rgb(102, 0, 102); box-sizing: border-box;">(loss, learning_rate)</span>:</span>
<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">"""Sets up the training Ops.
Creates a summarizer to track the loss over time in TensorBoard.
Creates an optimizer and applies the gradients to all trainable variables.
The Op returned by this function is what must be passed to the
`sess.run()` call to cause the model to train.
Args:
loss: Loss tensor, from loss().
learning_rate: The learning rate to use for gradient descent.
Returns:
train_op: The Op for training.
"""</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Add a scalar summary for the snapshot loss.</span>
tf.scalar_summary(loss.op.name, loss)
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Create the gradient descent optimizer with the given learning rate.</span>
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Create a variable to track the global step.</span>
global_step = tf.Variable(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0</span>, name=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'global_step'</span>, trainable=<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">False</span>)
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Use the optimizer to apply the gradients that minimize the loss</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># (and also increment the global step counter) as a single training step.</span>
train_op = optimizer.minimize(loss, global_step=global_step)
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">return</span> train_op
<span class="hljs-function" style="box-sizing: border-box;"><span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">def</span> <span class="hljs-title" style="box-sizing: border-box;">evaluation</span><span class="hljs-params" style="color: rgb(102, 0, 102); box-sizing: border-box;">(logits, labels)</span>:</span>
<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">"""Evaluate the quality of the logits at predicting the label.
Args:
logits: Logits tensor, float - [batch_size, NUM_CLASSES].
labels: Labels tensor, int32 - [batch_size], with values in the
range [0, NUM_CLASSES).
Returns:
A scalar int32 tensor with the number of examples (out of batch_size)
that were predicted correctly.
"""</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># For a classifier model, we can use the in_top_k Op.</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># It returns a bool tensor with shape [batch_size] that is true for</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># the examples where the label is in the top k (here k=1)</span>
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># of all logits for that example.</span>
correct = tf.nn.in_top_k(logits, labels, <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">1</span>)
<span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;"># Return the number of true entries.</span>
<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">return</span> tf.reduce_sum(tf.cast(correct, tf.int32))
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