#coding:utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time

start = time.clock() #计算开始时间
mnist = input_data.read_data_sets("./data/mnist/input_data/", one_hot=True)
 #MNIST数据输入
"""
权重初始化
初始化为一个接近0的很小的正数
"""
def weight_variable(shape):
    #initial = tf.truncated_normal(shape, stddev = 0.1)
    initial = tf.random_normal(shape, stddev = 0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape = shape)
    return tf.Variable(initial)

"""
卷积和池化，使用卷积步长为1（stride size）,0边距（padding size）
池化用简单传统的2x2大小的模板做max pooling

"""
with tf.variable_scope("data"):
    x = tf.placeholder(tf.float32,[None, 784],name='x_input')
    y_ = tf.placeholder(tf.float32, [None, 10],name='y_input')
 
with tf.name_scope('input_reshape'):
    x_image = tf.reshape(x, [-1, 28, 28, 1])#最后一维代表通道数目，如果是rgb则为3 
    tf.summary.image('input', x_image, 10)

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')
    # tf.nn.max_pool(value, ksize, strides, padding, data_format='NHWC', name=None)
    # x(value)              : [batch, height, width, channels]
    # ksize(pool大小)        : A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.
    # strides(pool滑动大小)   : A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.


"""
第一层 卷积层
x_image(batch, 28, 28, 1) -> h_pool1(batch, 14, 14, 32)
"""
with tf.variable_scope("conv1"):
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    #卷积、激活
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
# x_image -> [batch, in_height, in_width, in_channels]
#            [batch, 28, 28, 1]
# W_conv1 -> [filter_height, filter_width, in_channels, out_channels]
#            [5, 5, 1, 32]
# output  -> [batch, out_height, out_width, out_channels]
#            [batch, 28, 28, 32]
    h_pool1 = max_pool_2x2(h_conv1)
# h_conv1 -> [batch, in_height, in_weight, in_channels]
#            [batch, 28, 28, 32]
# output  -> [batch, out_height, out_weight, out_channels]
#            [batch, 14, 14, 32]

"""
第二层 卷积层

h_pool1(batch, 14, 14, 32) -> h_pool2(batch, 7, 7, 64)
"""
with tf.variable_scope('conv2'):
    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_pool1 -> [batch, 14, 14, 32]
# W_conv2 -> [5, 5, 32, 64]
# output  -> [batch, 14, 14, 64]
    h_pool2 = max_pool_2x2(h_conv2)
# h_conv2 -> [batch, 14, 14, 64]
# output  -> [batch, 7, 7, 64]

"""
第三层 全连接层

h_pool2(batch, 7, 7, 64) -> h_fc1(1, 1024)
"""
with tf.variable_scope('FC1'):
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])

#Dropouth_fc1 -> h_fc1_drop, 训练中启用，测试中关闭
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    keep_prob = tf.placeholder("float")
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

"""
第四层 Softmax输出层
"""
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)

"""
训练和评估模型

ADAM优化器来做梯度最速下降,feed_dict中加入参数keep_prob控制dropout比例

"""

cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv)) #计算交叉熵
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #使用adam优化器来以0.0001的学习率来进行微调
#tf.summary.scalar('train_step',train_step)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) #判断预测标签和实际标签是否匹配
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
# accuracy = tf.reduce_mean(tf.cast(correct_prediction,"float"))
tf.summary.scalar('accuracy', accuracy)
merged = tf.summary.merge_all()
sess = tf.Session() #启动创建的模型
train_writer = tf.summary.FileWriter('./train', sess.graph)

sess.run(tf.global_variables_initializer()) #初始化变量

for i in range(200): #开始训练模型，循环训练5000次
    batch = mnist.train.next_batch(50) #batch大小设置为50
    if i % 100 == 0:
        #train_accuracy = accuracy.eval(session = sess,
        #                               feed_dict = {x:batch[0], y_:batch[1], keep_prob:1.0})
        summary, train_accuracy = sess.run([merged, accuracy], feed_dict={x:batch[0], y_:batch[1], keep_prob:1.0})
        print("step %d, train_accuracy %g" %(i, train_accuracy))

        train_writer.add_summary(summary, i)
    train_step.run(session = sess, feed_dict = {x:batch[0], y_:batch[1],
                   keep_prob:0.5}) #神经元输出保持不变的概率 keep_prob 为0.5

batch1 = mnist.test.next_batch(50) #batch大小设置为50

print("test accuracy %g" %accuracy.eval(session = sess,
      feed_dict = {x:batch1[0], y_:batch1[1],
                   keep_prob:1.0})) #神经元输出保持不变的概率 keep_prob 为 1，即不变，一直保持输'''
#x:mnist.test.images, y_:mnist.test.labels,
train_writer.close()
#写到指定的磁盘路径中
end = time.clock() #计算程序结束时间
#print("running time is %d s") % (end)
print("time running %g" %(end-start))