# -*- coding=UTF-8 -*-  
import sys  
import os  
import random  
import cv2  
import math  
import time  
import numpy as np  
import tensorflow as tf  
import linecache  
import string  
import skimage  
import imageio  
# 输入数据  
import input_data  
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)  
# 定义网络超参数  
learning_rate = 0.001  
training_iters = 200000  
batch_size = 64  
display_step = 20  
# 定义网络参数  
n_input = 784  # 输入的维度  
n_classes = 10 # 标签的维度  
dropout = 0.8  # Dropout 的概率  
# 占位符输入  
x = tf.placeholder(tf.types.float32, [None, n_input])  
y = tf.placeholder(tf.types.float32, [None, n_classes])  
keep_prob = tf.placeholder(tf.types.float32)  
# 卷积操作  
def conv2d(name, l_input, w, b):  
    return tf.nn.relu(tf.nn.bias_add( \  
    tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'),b) \  
    , name=name)  
# 最大下采样操作  
def max_pool(name, l_input, k):  
    return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], \  
    strides=[1, k, k, 1], padding='SAME', name=name)  
# 归一化操作  
def norm(name, l_input, lsize=4):  
    return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)  
# 定义整个网络   
def alex_net(_X, _weights, _biases, _dropout):  
    _X = tf.reshape(_X, shape=[-1, 28, 28, 1]) # 向量转为矩阵  
    # 卷积层  
    conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])  
    # 下采样层  
    pool1 = max_pool('pool1', conv1, k=2)  
    # 归一化层  
    norm1 = norm('norm1', pool1, lsize=4)  
    # Dropout  
    norm1 = tf.nn.dropout(norm1, _dropout)  
   
    # 卷积  
    conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])  
    # 下采样  
    pool2 = max_pool('pool2', conv2, k=2)  
    # 归一化  
    norm2 = norm('norm2', pool2, lsize=4)  
    # Dropout  
    norm2 = tf.nn.dropout(norm2, _dropout)  
   
    # 卷积  
    conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])  
    # 下采样  
    pool3 = max_pool('pool3', conv3, k=2)  
    # 归一化  
    norm3 = norm('norm3', pool3, lsize=4)  
    # Dropout  
    norm3 = tf.nn.dropout(norm3, _dropout)  
   
    # 全连接层，先把特征图转为向量  
    dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]])   
    dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')   
    # 全连接层  
    dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') 
    # Relu activation  
    # 网络输出层  
    out = tf.matmul(dense2, _weights['out']) + _biases['out']  
    return out  
   
# 存储所有的网络参数  
weights = {  
    'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),  
    'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),  
    'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),  
    'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])),  
    'wd2': tf.Variable(tf.random_normal([1024, 1024])),  
    'out': tf.Variable(tf.random_normal([1024, 10]))  
}  
biases = {  
    'bc1': tf.Variable(tf.random_normal([64])),  
    'bc2': tf.Variable(tf.random_normal([128])),  
    'bc3': tf.Variable(tf.random_normal([256])),  
    'bd1': tf.Variable(tf.random_normal([1024])),  
    'bd2': tf.Variable(tf.random_normal([1024])),  
    'out': tf.Variable(tf.random_normal([n_classes]))  
}  
# 构建模型  
pred = alex_net(x, weights, biases, keep_prob)  
# 定义损失函数和学习步骤  
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))  
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)  
# 测试网络  
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))  
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))  
# 初始化所有的共享变量  
init = tf.initialize_all_variables()  
# 开启一个训练  
with tf.Session() as sess:  
    sess.run(init)  
    step = 1  
    # Keep training until reach max iterations  
    while step * batch_size < training_iters:  
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)  
        # 获取批数据  
        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})  
        if step % display_step == 0:  
            # 计算精度  
            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})  
            # 计算损失值  
            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})  
            print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)  
        step += 1  
    print "Optimization Finished!"  
    # 计算测试精度  
    print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.})  