tensorflow笔记DAY03【连载】
Tensorflow完成CNN
- 一、原始版本
- 二、加上tensorboard版本
一、原始版本
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets('MNIST_data',one_hot=True)
每个批次的大小
batch_size=100
#共有批次数
n_batch=mnist.train.num_examples//batch_size
#初始化权值
def weight_variable(shape):
initial=tf.truncated_normal(shape,stddev=0.1)
#生成一个截断的正态分布
return tf.Variable(initial)
#初始化偏置
def bias_variable(shape):
initial=tf.constant(0.1,shape=shape)
return tf.Variable(initial)
#卷积层
def conv2d(x,W):
#x 的shape是 [batch,in_height,in_width,in_channels]
#W 是卷积核 shape[filter_height,filter_width,in_channels,out_channels]
#strides=[1,x方向步长,y方向步长,1]
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] 池化窗口
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
#定义placeholder
x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])
#将x转为4d向量[batch,in_height,in_width,in_channels]
x_image=tf.reshape(x,[-1,28,28,1])
#初始化第一个卷积层的权值和偏置
W_conv1=weight_variable([5,5,1,32]) #5*5的采样窗口，32个卷积核从1个平面抽取特征,1是输入通道，如果是彩色要改成3
b_conv1=bias_variable([32]) #每个卷积核一个偏置值
#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
W_conv1=weight_variable([5,5,1,32]) #5*5的采样窗口，32个卷积核从1个平面抽取特征,1是输入通道，如果是彩色要改成3
b_conv1=bias_variable([32]) #每个卷积核一个偏置值
#初始化第二个卷积层的权值和偏置
W_conv2=weight_variable([5,5,32,64]) #5*5的采样窗口，64个卷积核从32个平面抽取特征
b_conv2=bias_variable([64])
#把h_pool1和权值向量进行卷积，再加上偏置，应用与relu
h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2)
h_pool2=max_pool_2x2(h_conv2)
#初始化第一个全连接层的权值
W_fc1=weight_variable([7*7*64,1024]) #上一层有7*7*64个神经元
b_fc1=bias_variable([1024])
#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])
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1)
#keep_prob用来表示神经元的输出概率
keep_prob=tf.placeholder(tf.float32)
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob)
#初始化第二个全连接层
W_fc2=weight_variable([1024,10])
b_fc2=bias_variable([10])
#计算输出
prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)
#交叉熵
cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
#使用Adam优化
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
#结果存放在布尔列表中
correct_prediction=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
#求准确率
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) #转格式为float32
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(21):
for batch in range(n_batch):
batch_xs,batch_ys=mnist.train.next_batch(batch_size)
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.7})
acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})
print('Iter: '+str(epoch)+", Testing Accuracy: "+str(acc))
二、加上tensorboard版本
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets('MNIST_data',one_hot=True)
#每个批次的大小
batch_size=100
#共有批次数
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)) #最小值
tf.summary.scalar('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)
#初始化偏置
def bias_variable(shape,name):
initial=tf.constant(0.1,shape=shape)
return tf.Variable(initial,name)
#卷积层
def conv2d(x,W):
#x 的shape是 [batch,in_height,in_width,in_channels]
#W 是卷积核 shape[filter_height,filter_width,in_channels,out_channels]
#strides=[1,x方向步长,y方向步长,1]
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] 池化窗口
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
with tf.name_scope('input'):
#定义placeholder
x=tf.placeholder(tf.float32,[None,784],name='x-input')
y=tf.placeholder(tf.float32,[None,10],name='y-input')
with tf.name_scope('x_image'):
#将x转为4d向量[batch,in_height,in_width,in_channels]
x_image=tf.reshape(x,[-1,28,28,1],name='x_image')
with tf.name_scope('Conv1'):
#初始化第一个卷积层的权值和偏置
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1个平面抽取特征,1是输入通道，如果是彩色要改成3
with tf.name_scope('b_conv1'):
b_conv1=bias_variable([32],name='b_conv1') #每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) #进行max-pooling
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'):
W_conv2=weight_variable([5,5,32,64],name='W_conv1') #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')
#把h_pool1和权值向量进行卷积，再加上偏置，应用与relu
with tf.name_scope('Conv2d_2'):
#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_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的图片经过第一次卷积=》28*28，池化=》14*14
第二次卷积后是=》14*14，池化=》7*7
所以得到的是64张7*7的特征平面
'''
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7*7*64个神经元
with tf.name_scope('b_fc1'):
b_fc1=bias_variable([1024],name='b_fc1')
#把池化层2的输出扁平化为1维
with tf.name_scope('h_pool2_flat'):
h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64],name='h_pool2_flat')
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')
with tf.name_scope('fc2'):
#初始化第二个全连接层
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)
with tf.name_scope('cross_entropy'):
#交叉熵
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Adam优化
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
#结果存放在布尔列表中
correct_prediction=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1)) #argmax 返回一维张量中最大的值所在的位置
with tf.name_scope('accuracy'):
#求准确率
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) #转格式为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('logs/train',sess.graph)
test_writer=tf.summary.FileWriter('logs/test',sess.graph)
for i in range(1001):
batch_xs,batch_ys=mnist.train.next_batch(batch_size) #每次抽100个样本
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)
#测试集
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))
#打开tensorboard方法
#输入tensorboard --logdir=F:\python\tf\tensorflow\CNN\logs
做此记录，方便查看