#MNIST训练程序
import os
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_inference

#配置神经网络参数
BATCH_SIZE=100
LEARNING_RATE_BASE=0.8
LEARNING_RATE_DECAY=0.99
REGULARAZTION_RATE=0.0001
TRAINING_STEPS=30000
MOVING_AVERAGE_DECAY=0.99
#模型保存路径和文件名
MODEL_SAVE_PATH="/tmp/model/"
MODEL_NAME="model.ckpt"

def train(mnist):
    x=tf.placeholder(tf.float32,[None,mnist_inference.INPUT_NODE],name='x-input')
    y_=tf.placeholder(tf.float32,[None,mnist_inference.OUTPUT_NODE],name='y-input')
    regularizer=tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)
    #调用前向传播过程
    y=mnist_inference.inference(x,regularizer)
    global_step=tf.Variable(0,trainable=False)
    #定义损失函数，学习率，滑动平均操作和训练过程
    #滑动平均
    variable_averages=tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)
    variable_averages_op=variable_averages.apply(tf.trainable_variables())
    #损失函数
    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'))
    #指数衰减学习率
    learning_rate=tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,mnist.train.num_examples/BATCH_SIZE,LEARNING_RATE_DECAY)
    #优化器
    train_step=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)
    #开始训练
    with tf.control_dependencies([train_step,variable_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()
        #训练过程不验证，放在测试程序中
        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})
            #每1000轮保存一遍模型
            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("/tmp/datasets/MNIST_data",one_hot=True)
    train(mnist)

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



    