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

INPUT_NODE = 784
OUTPUT_NODE = 10

#配置参数
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

#辅助函数
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)



#训练过程

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


#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}

        for i in range(TRAINING_STEPS):
            if i % 1000 == 0:
                validate_acc = sess.run(accuracy, feed_dict=validate_feed)
                print('after %d training steps, 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,test accuracy using average''model is %g' % ( TRAINING_STEPS,test_acc))

def main(argv=None):
    mnist = input_data.read_data_sets('./tmp/data',one_hot=True)
    train(mnist)


if __name__ == '__main__':
    tf.app.run()