'''
Created on 2017年4月22日

@author: weizhen
'''
import os
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data

# 加载mnist_inference.py中定义的常量和前向传播的函数
import LeNet5_infernece

# 配置神经网络的参数
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

# 模型保存的路径和文件名
MODEL_SAVE_PATH = "/path/to/model/"
MODEL_NAME = "model.ckpt"

def train(mnist):
    # 定义输入输出placeholder
    x = tf.placeholder(tf.float32, [BATCH_SIZE,
                                    LeNet5_infernece.IMAGE_SIZE,#第一维表示一个batch中样例的个数
                                    LeNet5_infernece.IMAGE_SIZE,#第二维和第三维表示图片的尺寸
                                    LeNet5_infernece.NUM_CHANNELS],#第四维表示图片的深度，对于RGB格式的图片，深度为5
                                    name='x-input')
    y_ = tf.placeholder(tf.float32, [None, LeNet5_infernece.OUTPUT_NODE], name='y-input')
    
    regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)
    # 直接使用mnist_inference.py中定义的前向传播过程
    y = LeNet5_infernece.inference(x,True,regularizer)
    global_step = tf.Variable(0, trainable=False)
    
    # 和5.2.1小节样例中类似地定义损失函数、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)
    variable_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)
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step);
    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.initialize_all_variables().run()
        
        # 在训练过程中不再测试模型在验证数据上的表现，验证和测试的过程将会有一个独立的程序来完成
        for i in range(TRAINING_STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            reshaped_xs=np.reshape(xs,(BATCH_SIZE,
                                       LeNet5_infernece.IMAGE_SIZE,
                                       LeNet5_infernece.IMAGE_SIZE,
                                       LeNet5_infernece.NUM_CHANNELS))
            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x:reshaped_xs, y_:ys})
            
            # 每1000轮保存一次模型
            if i % 1000 == 0:
                # 输出当前训练情况。这里只输出了模型在当前训练batch上的损失函数大小
                # 通过损失函数的大小可以大概了解训练的情况。在验证数据集上的正确率信息
                # 会有一个单独的程序来生成
                print("After %d training step(s),loss on training batch is %g" % (step, loss_value))
                
                # 保存当前的模型。注意这里给出了global_step参数，这样可以让每个被保存模型的文件末尾加上训练的轮数
                # 比如"model.ckpt-1000"表示训练1000轮之后得到的模型
                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/data", one_hot=True)
    train(mnist)

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

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