TensorFlow－1: 如何识别数字

cross_entropy = tf.reduce_mean(
      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))

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})

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}))

"""A very simple MNIST classifier.
See extensive documentation at
https://www.tensorflow.org/get_started/mnist/beginners
"""
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])    
          # a 2-D tensor of floating-point numbers
          # None means that a dimension can be of any length
  W = tf.Variable(tf.zeros([784, 10]))
  b = tf.Variable(tf.zeros([10]))
  y = tf.matmul(x, W) + b
          # It only takes one line to define it

  # 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]))
                  # tf.reduce_sum adds the elements in the second dimension of y, 
                  # due to the reduction_indices=[1] parameter.
                  # tf.reduce_mean computes the mean over all the examples in the batch.
  #
  # 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(labels=y_, logits=y))
  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
          # apply your choice of optimization algorithm to modify the variables and reduce the loss.

  sess = tf.InteractiveSession()
          # launch the model in an InteractiveSession
  tf.global_variables_initializer().run()
          # create an operation to initialize the variables

  # Train～～stochastic training
  for _ in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
            # Each step of the loop, 
            # we get a "batch" of one hundred random data points from our training set.
    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))
              # use tf.equal to check if our prediction matches the truth
              # tf.argmax(y,1) is the label our model thinks is most likely for each input, 
              # while tf.argmax(y_,1) is the correct label.
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
            # [True, False, True, True] would become [1,0,1,1] which would become 0.75.
  print(sess.run(accuracy, feed_dict={x: mnist.test.images,
                                      y_: mnist.test.labels}))
            # ask for our accuracy on our test data，about 92%

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)