Created mnist classifier using TensorFlow tutorial

classifier.py

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+# From the tutorial on 
+# 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)