Tensorflow 实战Google深度学习框架 第五章 5.2.1Minister数字识别 源代码

 import os
 import tab
 import tensorflow as tf

 print "tensorflow 5.2 "

 from tensorflow.examples.tutorials.mnist import input_data

 '''
 mnist = input_data.read_data_sets("/asky/tensorflow/mnist_data",one_hot=True)
 print "-------------------------------------"
 print "Training data size: ",mnist.train.num_examples
 print "-------------------------------------"
 print "Validating data size: ",mnist.validation.num_examples
 print "-------------------------------------"
 print "Testing data size: " ,mnist.test.num_examples
 print "-------------------------------------"
 print "Example training data: ",mnist.train.images[0]
 print "-------------------------------------"
 print "Example training data label: ",mnist.train.labels[0]

 batch_size = 100
 xs,ys=mnist.train.next_batch(batch_size)

 print "X shape:",xs.shape

 print "Y shape:",ys.shape

 print "Test Tezt"
 '''

 INPUT_NODE = 784
 OUTPUT_NODE = 10

 LAYER1_NODE = 500

 BATCH_SIZE = 100

 LEARNING_RATE_BASE = 0.8
 LEARNING_RATE_DECAY = 0.99

 REGULARIZATION_RATE = 0.0001
 TRAINING_STEPS = 30000
 MOVING_AVERAGE_DECAY = 0.99

 def inference(input_tensor,avg_class,weights1,biases1,weights2,biases2):
     if avg_class == None:
         layer1 = tf.nn.relu(tf.matmul(input_tensor,weights1)+biases1)
         return tf.matmul(layer1,weights2)+biases2
     else:
         layer1 = tf.nn.relu(
             tf.matmul(input_tensor,avg_class.average(weights1))+
             avg_class.average(biases1))
         return tf.matmul(layer1,avg_class.average(weights2))+avg_class.average(biases2)

 def train(mnist):
     x = tf.placeholder(tf.float32,[None,INPUT_NODE],name='x-input')
     y_ = tf.placeholder(tf.float32,[None,OUTPUT_NODE],name='y-input')
     weights1 = tf.Variable(
         tf.truncated_normal([INPUT_NODE,LAYER1_NODE],stddev=0.1))
     biases1 = tf.Variable( tf.constant(0.1,shape=[LAYER1_NODE]))

     weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE,OUTPUT_NODE],stddev=0.1))
     biases2 = tf.Variable(tf.constant(0.1,shape=[OUTPUT_NODE]))

     y = inference(x,None,weights1,biases1,weights2,biases2)

     global_step = tf.Variable(0,trainable=False)

     variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)

     variables_averages_op = variable_averages.apply(tf.trainable_variables())

     average_y = inference(x,variable_averages,weights1,biases1,weights2,biases2)

     #cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(y, tf.argmax(y_, 1 ))
     cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.argmax(y_, 1), logits=y)

     cross_entropy_mean = tf.reduce_mean(cross_entropy)

     regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)

     regularization = regularizer(weights1) + regularizer(weights2)

     loss = cross_entropy_mean + regularization

     learning_rate = tf.train.exponential_decay(
         LEARNING_RATE_BASE,
         global_step,
         mnist.train.num_examples/BATCH_SIZE,
         LEARNING_RATE_DECAY
     )

     train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)

     with tf.control_dependencies([train_step,variables_averages_op]):
         train_op = tf.no_op(name='train')

     correct_prediction = tf.equal(tf.argmax(average_y,1),tf.argmax(y_,1))
     accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

     with tf.Session() as sess:
         tf.global_variables_initializer().run()
         validate_feed = {x: mnist.validation.images,
                          y_: mnist.validation.labels}
         test_feed = {x: mnist.test.images, y_: mnist.test.labels }
         for i in range(TRAINING_STEPS):
             if i % 1000 ==0:
                 validate_acc = sess.run(accuracy,feed_dict=validate_feed)
                 print ("After %d training step(s),validation accuracy "
                         "using average model is %g " %(i,validate_acc) )
             xs, ys = mnist.train.next_batch(BATCH_SIZE)
             sess.run(train_op,feed_dict={x: xs , y_ : ys})

         test_acc  = sess.run(accuracy,feed_dict=test_feed)
         print ( "After %d training step(s),test accuracy using average "
                 "model is %g " % (TRAINING_STEPS , test_acc)  )

 def main(argv=None) :
     mnist = input_data.read_data_sets("/asky/tensorflow/mnist_data",one_hot=True)
     train(mnist)

 if __name__ == '__main__':
     tf.app.run()