#!/usr/bin/env python
#coding:utf8
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
def weight_variable(shape):
    initial = tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(initial)
def bias_variable(shape):
    initial = tf.constant(0.1,shape=shape)
    return tf.Variable(initial)
def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
def max_pool_2x2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],
            strides=[1,2,2,1],padding='SAME')
def mnist_cnn():
    mnist = input_data.read_data_sets('../../data/MNIST',one_hot=True)
    #first_layer
    x = tf.placeholder(tf.float32,[None,784])
    y_ = tf.placeholder(tf.float32,[None,10])

    W_conv1 = weight_variable([5,5,1,32])
    b_conv1 = bias_variable([32])

    
    x_images =  tf.reshape(x,[-1,28,28,1])

    h_conv1 = tf.nn.relu(conv2d(x_images,W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    #second_layer
    W_conv2 = weight_variable([5,5,32,64])
    b_conv2 = bias_variable([64])

    h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    #full_connected_layer
    W_fc1 = weight_variable([7*7*64,1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1) + b_fc1)
    
    #dropout
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)

    #output_layer
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])

    y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
    #train &  eval
    cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
    train = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    
    correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    
    config = tf.ConfigProto()
    config.gpu_options.allow_growth=True
    with tf.Session(config=config) as sess:
        sess.run(tf.initialize_all_variables())
        for i in range(100000):
            batch = mnist.train.next_batch(50)
            if i % 100 == 0:
                print accuracy.eval(feed_dict={x:mnist.test.images, y_:mnist.test.labels, keep_prob: 1.0})
            train.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
if __name__=='__main__':
    mnist_cnn()
