# -*- coding: utf-8 -*-
"""
Created on Thu Sep  8 15:29:48 2016

@author: root
"""
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("../MNIST_data/", one_hot=True)     #下载并加载mnist数据
x = tf.placeholder(tf.float32, [None, 784])                        #输入的数据占位符
y_actual = tf.placeholder(tf.float32, shape=[None, 10])            #输入的标签占位符

#定义一个函数，用于初始化所有的权值 W
def weight_variable(shape, name=''):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial, name=name)

#定义一个函数，用于初始化所有的偏置项 b
def bias_variable(shape,name=''):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial, name=name)

#定义一个函数，用于构建卷积层
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

#定义一个函数，用于构建池化层
def max_pool(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

#构建网络
with tf.name_scope('conv_layer1'):
    x_image = tf.reshape(x, [-1,28,28,1])         #转换输入数据shape,以便于用于网络中
    W_conv1 = weight_variable([5, 5, 1, 32], name="W_conv1")
    b_conv1 = bias_variable([32], name="b_conv1")
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)     #第一个卷积层
    h_pool1 = max_pool(h_conv1)                                  #第一个池化层

with tf.name_scope('conv_layer2'):
    W_conv2 = weight_variable([5, 5, 32, 64], name="W_conv2")
    b_conv2 = bias_variable([64], name="b_conv2")
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)      #第二个卷积层
    h_pool2 = max_pool(h_conv2)                                   #第二个池化层

with tf.name_scope('full_connect_layer'):
    W_fc1 = weight_variable([7 * 7 * 64, 1024], name="W_fc1")
    b_fc1 = bias_variable([1024], name="b_fc1")
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])              #reshape成向量
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)    #第一个全连接层

with tf.name_scope('dropout_layer'):
    keep_prob = tf.placeholder("float")
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)                  #dropout层

with tf.name_scope('softmax_layer'):
    W_fc2 = weight_variable([1024, 10], name="W_fc2")
    b_fc2 = bias_variable([10], name="b_fc2")
    # W_wide= weight_variable([28*28,10], name="")

    y_predict=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)   #softmax层




with tf.name_scope('sgd'):
    cross_entropy = -tf.reduce_sum(y_actual*tf.log(y_predict))     #交叉熵
    train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy)    #梯度下降法

with tf.name_scope('accuracy'):
    correct_prediction = tf.equal(tf.argmax(y_predict,1), tf.argmax(y_actual,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))                 #精确度计算
    # tf.scalar_summary("acc", accuracy)
saver = tf.train.Saver()

for var in tf.trainable_variables():
    tf.histogram_summary(var.name, var)

tf.scalar_summary("loss", cross_entropy)


merged_summary_op = tf.merge_all_summaries()

with tf.Session() as sess:
    summary_writer = tf.train.SummaryWriter('/tmp/mnist_cnn_logs', graph=tf.get_default_graph())

    sess.run(tf.initialize_all_variables())


    for i in range(20000):
        batch = mnist.train.next_batch(50)
        if i%100 == 0:                  #训练100次，验证一次
            train_acc = accuracy.eval(feed_dict={x:batch[0], y_actual: batch[1], keep_prob: 1.0})
            print('step',i,'training accuracy',train_acc)
            # tf.runable_variable()

            c,summary_str = sess.run([train_step,merged_summary_op], feed_dict={x: batch[0], y_actual: batch[1], keep_prob: 0.5})

            summary_writer.add_summary(summary_str, i)

    test_acc=accuracy.eval(feed_dict={x: mnist.test.images, y_actual: mnist.test.labels, keep_prob: 1.0})
    print("test accuracy",test_acc)
    save_path = saver.save(sess, "tmp/model.ckpt")
    print "Model saved in file: ", save_path