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author | 2017-03-24 11:43:13 -0800 | |
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committer | 2017-03-24 13:32:07 -0700 | |
commit | 3e1676e40aace360440886d823c53cc63a98ace1 (patch) | |
tree | 99f304f450e05004a10c083d9a0ee8c176a328e8 /tensorflow/examples/tutorials | |
parent | f2574c273778eeb05a8ef3ba40544ddee98a9e07 (diff) |
- Added accompanying .py file for deep MNIST tutorial
Change: 151158189
Diffstat (limited to 'tensorflow/examples/tutorials')
-rw-r--r-- | tensorflow/examples/tutorials/mnist/mnist_deep.py | 155 | ||||
-rw-r--r-- | tensorflow/examples/tutorials/mnist/mnist_softmax.py | 2 |
2 files changed, 156 insertions, 1 deletions
diff --git a/tensorflow/examples/tutorials/mnist/mnist_deep.py b/tensorflow/examples/tutorials/mnist/mnist_deep.py new file mode 100644 index 0000000000..2896eee77d --- /dev/null +++ b/tensorflow/examples/tutorials/mnist/mnist_deep.py @@ -0,0 +1,155 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""A deep MNIST classifier using convolutional layers. + +See extensive documentation at +https://www.tensorflow.org/get_started/mnist/pros +""" +# Disable linter warnings to maintain consistency with tutorial. +# pylint: disable=invalid-name +# pylint: disable=g-bad-import-order + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import sys + +from tensorflow.examples.tutorials.mnist import input_data + +import tensorflow as tf + +FLAGS = None + + +def deepnn(x): + """deepnn builds the graph for a deep net for classifying digits. + + Args: + x: an input tensor with the dimensions (N_examples, 784), where 784 is the + number of pixels in a standard MNIST image. + + Returns: + A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values + equal to the logits of classifying the digit into one of 10 classes (the + digits 0-9). keep_prob is a scalar placeholder for the probability of + dropout. + """ + # Reshape to use within a convolutional neural net. + # Last dimension is for "features" - there is only one here, since images are + # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. + x_image = tf.reshape(x, [-1, 28, 28, 1]) + + # First convolutional layer - maps one grayscale image to 32 feature maps. + W_conv1 = weight_variable([5, 5, 1, 32]) + b_conv1 = bias_variable([32]) + h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) + + # Pooling layer - downsamples by 2X. + h_pool1 = max_pool_2x2(h_conv1) + + # Second convolutional layer -- maps 32 feature maps to 64. + W_conv2 = weight_variable([5, 5, 32, 64]) + b_conv2 = bias_variable([64]) + h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) + + # Second pooling layer. + h_pool2 = max_pool_2x2(h_conv2) + + # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image + # is down to 7x7x64 feature maps -- maps this to 1024 features. + W_fc1 = weight_variable([7 * 7 * 64, 1024]) + b_fc1 = bias_variable([1024]) + + h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) + h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) + + # Dropout - controls the complexity of the model, prevents co-adaptation of + # features. + keep_prob = tf.placeholder(tf.float32) + h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) + + # Map the 1024 features to 10 classes, one for each digit + W_fc2 = weight_variable([1024, 10]) + b_fc2 = bias_variable([10]) + + y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 + return y_conv, keep_prob + + +def conv2d(x, W): + """conv2d returns a 2d convolution layer with full stride.""" + return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') + + +def max_pool_2x2(x): + """max_pool_2x2 downsamples a feature map by 2X.""" + return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], padding='SAME') + + +def weight_variable(shape): + """weight_variable generates a weight variable of a given shape.""" + initial = tf.truncated_normal(shape, stddev=0.1) + return tf.Variable(initial) + + +def bias_variable(shape): + """bias_variable generates a bias variable of a given shape.""" + initial = tf.constant(0.1, shape=shape) + return tf.Variable(initial) + + +def main(_): + # Import data + mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) + + # Create the model + x = tf.placeholder(tf.float32, [None, 784]) + + # Define loss and optimizer + y_ = tf.placeholder(tf.float32, [None, 10]) + + # Build the graph for the deep net + y_conv, keep_prob = deepnn(x) + + cross_entropy = tf.reduce_mean( + tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) + train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) + correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + for i in range(20000): + batch = mnist.train.next_batch(50) + if i % 100 == 0: + train_accuracy = accuracy.eval(feed_dict={ + x: batch[0], y_: batch[1], keep_prob: 1.0}) + print('step %d, training accuracy %g' % (i, train_accuracy)) + train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) + + print('test accuracy %g' % accuracy.eval(feed_dict={ + x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, + default='/tmp/tensorflow/mnist/input_data', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/tensorflow/examples/tutorials/mnist/mnist_softmax.py b/tensorflow/examples/tutorials/mnist/mnist_softmax.py index 4fa89ff246..addd2d3810 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_softmax.py +++ b/tensorflow/examples/tutorials/mnist/mnist_softmax.py @@ -16,7 +16,7 @@ """A very simple MNIST classifier. See extensive documentation at -http://tensorflow.org/tutorials/mnist/beginners/index.md +https://www.tensorflow.org/get_started/mnist/beginners """ from __future__ import absolute_import from __future__ import division |