Tensorflow训练神经网络

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#MNIST数据集相关的系数
INPUT_NODE = 784
OUTPUT_NODE = 10

#配置神经网络的参数
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')
    
    #生成隐藏层的参数
    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)
    
    variable_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(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, variable_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.initialize_all_variables().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 ))

#主程序入口
if __name__ == '__main__':      
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
    train(mnist)