1.案例
#!/usr/bin/env python3
# encoding: utf-8
'''
@author: bigcome
@desc:
@time: 2018/11/15 10:15
'''
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#载入数据
mnist = input_data.read_data_sets("MNIST_DATE/",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('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev',stddev) #标准差
tf.summary.scalar('max',tf.reduce_max(var)) #最大值
tf.summary.scalar('min',tf.reduce_min(var))
tf.summary.histogram('histogram',var) #z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)
#初始化偏置值
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')
#池化层
def max_pool_2x2(x):
#ksize [1,x,y,1]
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
#命名空间
with tf.name_scope('input'):
#none表示第一个维度可以是任意长度
x = tf.placeholder(tf.float32,[None,784],name='x-input')
#正确的标签
y = tf.placeholder(tf.float32,[None,10],name='y-input')
with tf.name_scope('x_image'):
# 改变x的格式转为4D的向量[batch, in_height, in_width, in_channels]
x_image = tf.reshape(x,[-1,28,28,1],name='x_image')
#定义第一层卷积
with tf.name_scope('Conv1'):
#初始化第一层卷积的权值和偏置
with tf.name_scope('W_conv1'):
# 5*5的采样窗口，32个卷积核从1个平面抽取特征
W_conv1 = weight_variable([5,5,1,32],name='W_conv1')
with tf.name_scope('b_conv1'):
# 每一个卷积核一个偏置值
b_conv1 = bias_variable([32],name='b_conv1')
# 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
with tf.name_scope('conv2d_1'):
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)
#d定义第二层卷积
with tf.name_scope('Conv2'):
# 初始化第二个卷积层的权值和偏置
with tf.name_scope('W_conv2'):
# 5*5的采样窗口，64个卷积核从32个平面抽取特征
W_conv2 = weight_variable([5,5,32,64],name='W_conv2')
with tf.name_scope('b_conv2'):
# 每一个卷积核一个偏置值
b_conv2 = bias_variable([64],name='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('conv2d_2'):
conv2d_2 = conv2d(h_pool1,W_conv2) + b_conv2
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，第一次池化后变为14*14
#第二次卷积后为14*14，第二次池化后变为了7*7
#进过上面操作后得到64张7*7的平面
#定义第一个全连接层
with tf.name_scope('fc1'):
with tf.name_scope('W_fc1'):
# 初始化第一个全连接层的权值
W_fc1 = weight_variable([7*7*64,1024],name='W_fc1')
#上一层有64个神经元，全连接层有1024个神经元
with tf.name_scope('b_fc1'):
b_fc1 = bias_variable([1024],name='b_fc1')
#把池化层2的输出平化为1维
with tf.name_scope('h_pool2_flat'):
h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64],name='h_pool2_flat')
# 求第一个全连接层的输出
with tf.name_scope('wx_plus_b1'):
wx_plus_b1 = tf.matmul(h_pool2_flat,W_fc1) + b_fc1
with tf.name_scope('relu'):
h_fc1 = tf.nn.relu(wx_plus_b1)
# keep_prob用来表示神经元的输出概率
with tf.name_scope('keep_prob'):
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')
#定义第二个全连接
with tf.name_scope('fc2'):
with tf.name_scope('W_fc2'):
W_fc2 = weight_variable([1024,10],name='W_fc2')
with tf.name_scope('b_fc2'):
b_fc2 = bias_variable([10],name='b_fc2')
with tf.name_scope('wx_plus_b2'):
wx_plus_b2 = tf.matmul(h_fc1_drop,W_fc2) + b_fc2
with tf.name_scope('softmax'):
#计算输出
prediction = tf.nn.softmax(wx_plus_b2)
#交叉熵代价函数
with tf.name_scope('cross_entropy'):
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)
#使用AdamOptimizer 进行优化
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
#求准确率
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
#结果存放在一个布尔列表中
# argmax返回一维张量中最大的值所在的位置
correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
with tf.name_scope('accuracy'):
accuracy = tf.reduce_mean(tf.cast(cross_entropy,tf.float32))
tf.summary.scalar('accuracy',accuracy)
#合并所有的summary
merged = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_writer = tf.summary.FileWriter('log/train',sess.graph)
test_writer = tf.summary.FileWriter('log/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})
# 记录训练集计算的参数
summary = sess.run(merged, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.0})
train_writer.add_summary(summary, i)
# 记录测试集计算的参数
batch_xs, batch_ys = mnist.test.next_batch(batch_size)
summary = sess.run(merged, 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[:10000], y: mnist.train.labels[:10000],
keep_prob: 1.0})
print("Iter " + str(i) + ", Testing Accuracy= " + str(test_acc) + ", Training Accuracy= " + str(train_acc))