# -*- coding: utf-8 -*-
"""
Created on Sun Nov 12 14:10:16 2017
@author: jssyhhghf
"""
# CNN
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#载入数据集
mnist = input_data.read_data_sets(r"E:\anaconda\tensorflow\tensor_mnist-master\MNIST_data",one_hot=True)
#每个批次的大小
batch_size = 100
#计算一共有多少个批次
n_batch=mnist.train.num_examples//batch_size
#参数概要
def variable_summaries(var):
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean',mean)#平均值
with tf.name_scope('stdder'):
stdder = tf.sqrt(tf.reduce_mean(tf.square(var-mean)))
tf.summary.scalar('stdder',stdder) #标准差
tf.summary.scalar('max',tf.reduce_max(var))#最大值
tf.summary.scalar('min',tf.reduce_min(var))#最小值
tf.summary.histogram('histogram',var) #直方图
#初始化权值
def weight_variable(shape,name):
initial = tf.truncated_normal(shape,stddev=0.1) #生成一个截断的正态分布
return tf.Variable(initial,name=name)
#初始化偏置
def bias_variable(shape,name):
initial = tf.constant(0.1,shape=shape)
return tf.Variable(initial,name=name)
#卷积层
def conv2d(x,W):
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
#x:tensor[batch,height,width,channels]
#W:卷积核[height,width,inchannels.outchannels]
#strides步长，第0和第3个都是1,1代表x方向的步长，2代表y方向的步长
#池化层
def max_pool_2x2(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
with tf.name_scope('input'):
x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])
with tf.name_scope('x_image'):
#改变x的格式为4D向量
x_image = tf.reshape(x,[-1,28,28,1])
with tf.name_scope('Conv1'):
#初始化第一个卷基层的权值和偏置
with tf.name_scope('W_conv1'):
W_conv1=weight_variable([5,5,1,32],name='W_conv1')#5*5的采样窗口,32个卷积核从一个平面抽取特征
with tf.name_scope('b_conv1'):
b_conv1=bias_variable([32],name='b_conv1')#每一个卷积核一个偏置值
with tf.name_scope('conv2d_1'):
#把x_image和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数
conv2d_1=conv2d(x_image,W_conv1)+b_conv1
with tf.name_scope('relu'):
h_conv1 = tf.nn.relu(conv2d_1)
with tf.name_scope('h_pool1'):
h_pool1 = max_pool_2x2(h_conv1)
with tf.name_scope('Conv2'):
with tf.name_scope('W_conv2'):
#初始化第二个卷基层的权值和偏置
W_conv2=weight_variable([5,5,32,64],name='W_conv2')#5*5的采样窗口,64个卷积核从32个平面抽取特征
with tf.name_scope('b_conv2'):
b_conv2=bias_variable([64],name='b_conv2')#每一个卷积核一个偏置值
with tf.name_scope('conv2d_2'):
conv2d_2=conv2d(h_pool1,W_conv2)+b_conv2
#把h_pool1和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数
with tf.name_scope('relu'):
h_conv2 = tf.nn.relu(conv2d_2)
with tf.name_scope('h_pool2'):
h_pool2 = max_pool_2x2(h_conv2)
#28*28的图片第一次卷积后还是28*28,第一次池化为14*14
#第二次卷积后卫14*14，第二次池化为7*7
#经过上面的操作得到64张7*7的平面
with tf.name_scope('layer1'):
#初始化第一个全连接层的权值
with tf.name_scope('weight'):
W_fc1 = weight_variable([7*7*64,1024],name='W_fc1')
with tf.name_scope('bias'):
b_fc1 = bias_variable([1024],name='b_fc1')
with tf.name_scope('flat'):
#把池化层2的输出扁平化为1维
h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64],name='h_pool')
with tf.name_scope('relu'):
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
with tf.name_scope('keep_prob'):
#keep_prob用来表示神经元的输出概率
keep_prob = tf.placeholder(tf.float32,name='keep_prob')
with tf.name_scope('h_fc1_drop'):
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob,name='h_fc1_drop')
#初始化第二个全连接层
with tf.name_scope('layer2'):
with tf.name_scope('weight'):
W_fc2 = weight_variable([1024,10],name='W_fc2')
with tf.name_scope('bias'):
b_fc2 = bias_variable([10],name='b_fc2')
with tf.name_scope('softmax'):
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
with tf.name_scope('cross_entropy'):
#交叉熵代价函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction),name='cross_entropy')
tf.summary.scalar('cross_entropy',cross_entropy)
with tf.name_scope('train'):
#使用Adamoption进行优化
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
with tf.name_scope('accuracy'):
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
tf.summary.scalar('accuracy',accuracy)
megred = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_writer = tf.summary.FileWriter(r'E:\anaconda\tensorflow\logs\train',sess.graph)
test_writer = tf.summary.FileWriter(r'E:\anaconda\tensorflow\logs\test',sess.graph)
for i in range(1001):
batch_xs,batch_ys = mnist.train.next_batch(batch_size)
sess.run(train_step, feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.5})
summary=sess.run(megred,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0})
train_writer.add_summary(summary,i)
batch_xs,batch_ys = mnist.test.next_batch(batch_size)
summary=sess.run(megred,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0})
test_writer.add_summary(summary,i)
if (i%100==0):
test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})
train_acc = sess.run(accuracy,feed_dict={x:mnist.train.images,y:mnist.train.labels,keep_prob:1.0})
print ('Iter'+str(i)+', Testing Accuracy='+str(test_acc)+',Training Accuracy='+str(train_acc))