1 # Copyright 2015 The TensorFlow Authors. All Rights Reserved. 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License"); 4 # you may not use this file except in compliance with the License. 5 # You may obtain a copy of the License at 6 # 7 # http://www.apache.org/licenses/LICENSE-2.0 8 # 9 # Unless required by applicable law or agreed to in writing, software 10 # distributed under the License is distributed on an "AS IS" BASIS, 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 # See the License for the specific language governing permissions and 13 # limitations under the License. 14 # ============================================================================== 15 16 """A deep MNIST classifier using convolutional layers. 17 18 See extensive documentation at 19 https://www.tensorflow.org/get_started/mnist/pros 20 """ 21 # Disable linter warnings to maintain consistency with tutorial. 22 # pylint: disable=invalid-name 23 # pylint: disable=g-bad-import-order 24 25 from __future__ import absolute_import 26 from __future__ import division 27 from __future__ import print_function 28 29 import argparse 30 import sys 31 import tempfile 32 33 from tensorflow.examples.tutorials.mnist import input_data 34 35 import tensorflow as tf 36 37 FLAGS = None 38 39 40 def deepnn(x): 41 """deepnn builds the graph for a deep net for classifying digits. 42 43 Args: 44 x: an input tensor with the dimensions (N_examples, 784), where 784 is the 45 number of pixels in a standard MNIST image. 46 47 Returns: 48 A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values 49 equal to the logits of classifying the digit into one of 10 classes (the 50 digits 0-9). keep_prob is a scalar placeholder for the probability of 51 dropout. 52 """ 53 # Reshape to use within a convolutional neural net. 54 # Last dimension is for "features" - there is only one here, since images are 55 # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. 56 with tf.name_scope('reshape'): 57 x_image = tf.reshape(x, [-1, 28, 28, 1]) 58 59 # First convolutional layer - maps one grayscale image to 32 feature maps. 60 with tf.name_scope('conv1'): 61 W_conv1 = weight_variable([5, 5, 1, 32]) 62 b_conv1 = bias_variable([32]) 63 h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 64 65 # Pooling layer - downsamples by 2X. 66 with tf.name_scope('pool1'): 67 h_pool1 = max_pool_2x2(h_conv1) 68 69 # Second convolutional layer -- maps 32 feature maps to 64. 70 with tf.name_scope('conv2'): 71 W_conv2 = weight_variable([5, 5, 32, 64]) 72 b_conv2 = bias_variable([64]) 73 h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 74 75 # Second pooling layer. 76 with tf.name_scope('pool2'): 77 h_pool2 = max_pool_2x2(h_conv2) 78 79 # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image 80 # is down to 7x7x64 feature maps -- maps this to 1024 features. 81 with tf.name_scope('fc1'): 82 W_fc1 = weight_variable([7 * 7 * 64, 1024]) 83 b_fc1 = bias_variable([1024]) 84 85 h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64]) 86 h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 87 88 # Dropout - controls the complexity of the model, prevents co-adaptation of 89 # features. 90 with tf.name_scope('dropout'): 91 keep_prob = tf.placeholder(tf.float32) 92 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 93 94 # Map the 1024 features to 10 classes, one for each digit 95 with tf.name_scope('fc2'): 96 W_fc2 = weight_variable([1024, 10]) 97 b_fc2 = bias_variable([10]) 98 99 y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 100 return y_conv, keep_prob 101 102 103 def conv2d(x, W): 104 """conv2d returns a 2d convolution layer with full stride.""" 105 return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 106 107 108 def max_pool_2x2(x): 109 """max_pool_2x2 downsamples a feature map by 2X.""" 110 return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], 111 strides=[1, 2, 2, 1], padding='SAME') 112 113 114 def weight_variable(shape): 115 """weight_variable generates a weight variable of a given shape.""" 116 initial = tf.truncated_normal(shape, stddev=0.1) 117 return tf.Variable(initial) 118 119 120 def bias_variable(shape): 121 """bias_variable generates a bias variable of a given shape.""" 122 initial = tf.constant(0.1, shape=shape) 123 return tf.Variable(initial) 124 125 126 def main(_): 127 # Import data 128 mnist = input_data.read_data_sets(FLAGS.data_dir) 129 130 # Create the model 131 x = tf.placeholder(tf.float32, [None, 784]) 132 133 # Define loss and optimizer 134 y_ = tf.placeholder(tf.int64, [None]) 135 136 # Build the graph for the deep net 137 y_conv, keep_prob = deepnn(x) 138 139 with tf.name_scope('loss'): 140 cross_entropy = tf.losses.sparse_softmax_cross_entropy( 141 labels=y_, logits=y_conv) 142 cross_entropy = tf.reduce_mean(cross_entropy) 143 144 with tf.name_scope('adam_optimizer'): 145 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 146 147 with tf.name_scope('accuracy'): 148 correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_) 149 correct_prediction = tf.cast(correct_prediction, tf.float32) 150 accuracy = tf.reduce_mean(correct_prediction) 151 152 graph_location = tempfile.mkdtemp() 153 print('Saving graph to: %s' % graph_location) 154 train_writer = tf.summary.FileWriter(graph_location) 155 train_writer.add_graph(tf.get_default_graph()) 156 157 with tf.Session() as sess: 158 sess.run(tf.global_variables_initializer()) 159 for i in range(20000): 160 batch = mnist.train.next_batch(50) 161 if i % 100 == 0: 162 train_accuracy = accuracy.eval(feed_dict={ 163 x: batch[0], y_: batch[1], keep_prob: 1.0}) 164 print('step %d, training accuracy %g' % (i, train_accuracy)) 165 train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) 166 167 print('test accuracy %g' % accuracy.eval(feed_dict={ 168 x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) 169 170 if __name__ == '__main__': 171 parser = argparse.ArgumentParser() 172 parser.add_argument('--data_dir', type=str, 173 default='/tmp/tensorflow/mnist/input_data', 174 help='Directory for storing input data') 175 FLAGS, unparsed = parser.parse_known_args() 176 tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) 177
