import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataINPUT_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    REGULARAZTION_RATE = 0.0001   TRAINING_STEPS = 5000        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)        # 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    regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)    regularaztion = regularizer(weights1) + regularizer(weights2)    loss = cross_entropy_mean + regularaztion        # 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    with tf.control_dependencies([train_step]):        train_op = tf.no_op(name='train')    # 计算正确率    correct_prediction = tf.equal(tf.argmax(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()