import os

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

INPUT_NODE = 784

OUTPUT_NODE = 10

LAYER1_NODE = 500

def get_weight_variable(shape, regularizer):

    weights = tf.get_variable("weights", shape, initializer=tf.truncated_normal_initializer(stddev=0.1))

    if(regularizer != None):

        tf.add_to_collection('losses', regularizer(weights))

    return weights

def inference(input_tensor, regularizer):

    with tf.variable_scope('layer1'):

        weights = get_weight_variable([INPUT_NODE, LAYER1_NODE], regularizer)

        biases = tf.get_variable("biases", [LAYER1_NODE], initializer=tf.constant_initializer(0.0))

        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights) + biases)

    with tf.variable_scope('layer2'):

        weights = get_weight_variable([LAYER1_NODE, OUTPUT_NODE], regularizer)

        biases = tf.get_variable("biases", [OUTPUT_NODE], initializer=tf.constant_initializer(0.0))

        layer2 = tf.matmul(layer1, weights) + biases

    return layer2

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

MODEL_SAVE_PATH = "F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data\\"

MODEL_NAME = "mnist_model"

def train(mnist):

    # 定义输入输出placeholder。

    x = tf.placeholder(tf.float32, [None, INPUT_NODE], name='x-input')

    y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name='y-input')

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

    y = inference(x, regularizer)

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

    # 定义损失函数、学习率、滑动平均操作以及训练过程。

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

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

    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)

    loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))

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

    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')

    # 初始化TensorFlow持久化类。

    saver = tf.train.Saver()

    with tf.Session() as sess:

        tf.global_variables_initializer().run()

        for i in range(TRAINING_STEPS):

            xs, ys = mnist.train.next_batch(BATCH_SIZE)

            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})

            if i % 1000 == 0:

                print("After %d training step(s), loss on training batch is %g." % (step, loss_value))

                saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)

def main(argv=None):

    mnist = input_data.read_data_sets("F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data\\", one_hot=True)

    train(mnist)

if __name__ == '__main__':

    main()