import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

INPUT_NODE = 784     # 输入节点

OUTPUT_NODE = 10     # 输出节点

LAYER1_NODE = 500    # 隐藏层数       

BATCH_SIZE = 100     # 每次batch打包的样本个数        

# 模型相关的参数

LEARNING_RATE_BASE = 0.8

LEARNING_RATE_DECAY = 0.99    

TRAINING_STEPS = 5000

MOVING_AVERAGE_DECAY = 0.99  

def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):

    # 不使用滑动平均类

    if avg_class == None:

        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)

        return tf.matmul(layer1, weights2) + biases2

    else:

        # 使用滑动平均类

        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]))

    # 生成输出层的参数。

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

    biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))

    # 计算不含滑动平均类的前向传播结果

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

    # 定义训练轮数及相关的滑动平均类

    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)

    # 计算交叉熵及其平均值

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

    cross_entropy_mean = tf.reduce_mean(cross_entropy)

    # 损失函数的计算

    loss = cross_entropy_mean

    # 设置指数衰减的学习率。

    learning_rate = tf.train.exponential_decay(

        LEARNING_RATE_BASE,

        global_step,

        mnist.train.num_examples / BATCH_SIZE,

        LEARNING_RATE_DECAY,

        staircase=True)

    # 优化损失函数

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

    # 反向传播更新参数和更新每一个参数的滑动平均值

    with tf.control_dependencies([train_step, variables_averages_op]):

        train_op = tf.no_op(name='train')

    # 计算正确率

    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))

    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    # 初始化会话，并开始训练过程。

    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} 

        # 循环的训练神经网络。

        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("E:\\MNIST_data\\", one_hot=True)

    train(mnist)

if __name__=='__main__':

    main()