old_contents: # 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 very simple MNIST classifier.
  
  See extensive documentation at
  http://tensorflow.org/tutorials/mnist/beginners/index.md
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import sys
  
  # Import data
  from tensorflow.examples.tutorials.mnist import input_data
  
  import tensorflow as tf
  
  FLAGS = None
  
  
  def main(_):
    mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
  
    # Create the model
    x = tf.placeholder(tf.float32, [None, 784])
    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))
    y = tf.matmul(x, W) + b
  
    # Define loss and optimizer
    y_ = tf.placeholder(tf.float32, [None, 10])
  
    # The raw formulation of cross-entropy,
    #
    #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
    #                                 reduction_indices=[1]))
    #
    # can be numerically unstable.
    #
    # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
    # outputs of 'y', and then average across the batch.
    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  
    sess = tf.InteractiveSession()
    # Train
    tf.global_variables_initializer().run()
    for _ in range(1000):
      batch_xs, batch_ys = mnist.train.next_batch(100)
      sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
  
    # Test trained model
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print(sess.run(accuracy, feed_dict={x: mnist.test.images,
                                        y_: mnist.test.labels}))
  
  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)
  
new_contents: # 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 very simple MNIST classifier.
  
  See extensive documentation at
  http://tensorflow.org/tutorials/mnist/beginners/index.md
  """
  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 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])
    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))
    y = tf.matmul(x, W) + b
  
    # Define loss and optimizer
    y_ = tf.placeholder(tf.float32, [None, 10])
  
    # The raw formulation of cross-entropy,
    #
    #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
    #                                 reduction_indices=[1]))
    #
    # can be numerically unstable.
    #
    # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
    # outputs of 'y', and then average across the batch.
    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  
    sess = tf.InteractiveSession()
    tf.global_variables_initializer().run()
    # Train
    for _ in range(1000):
      batch_xs, batch_ys = mnist.train.next_batch(100)
      sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
  
    # Test trained model
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print(sess.run(accuracy, feed_dict={x: mnist.test.images,
                                        y_: mnist.test.labels}))
  
  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)
  
subject: Update mnist_softmax.py (#5714)