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()