tensorflow2.0搭建卷积神经网络,tensorflow实现cnn
学习神经网络有一段时间了,从常见的BP神经网络到LSTM长短期记忆网络都有一定的了解,但是从来没有系统的记录过整个神经网络的结构。我相信这些小记录可以帮助我更深入地了解神经网络。
00-1010隐藏层1、卷积层2、池层3、全连接层、卷积层、池层、全连接层介绍。从常见的BP神经网络到LSTM长期和短期记忆网络,所有的代码都被神经网络学习了一段时间,但整个神经网络的结构从未被系统地记录下来。我相信这些小记录可以帮助我更深入地了解神经网络。
目录
卷积神经网络(CNN)是一种具有卷积计算和深层结构的前馈神经网络,是深度学习的代表性算法之一。
其主要结构分为输入层、隐藏层和输出层。
在tensorboard中,其结构如图所示:
对于卷积神经网络,其输入层和输出层与普通卷积神经网络相同。
但其隐含层可分为三部分,分别是卷积层(输入数据的特征提取)、池层(特征选择和信息过滤)和全连接层(相当于传统前馈神经网络中的隐含层)。
简介
隐含层介绍
卷积输入图像被放入一组卷积滤波器中,每个滤波器激活图像中的一些特征。
假设黑白图像的大小为5*5,如下所示:
使用以下卷积器进行卷积:
卷积结果是:
卷积过程可以提取特征,利用卷积神经网络根据特征完成分类。
在张量流中,卷积层的重要作用是:
tf.nn.conv2d(input,filter,strides,padding,use_cudnn_on_gpu=None,name=None)
其中包括:
1.输入是输入的数量,形状是[批次、高度、宽度、通道]。
2.滤波器是使用的卷积核;
3.strides是步长,其格式为[1,step,step,1]。步长是指图像卷积各个维度上的步长;
4.Padding:字符串数量,只能是“SAME”、“valid”和“VALID”之一。相同意味着图像面积在卷积前后不变。
1、卷积层
池层用于卷积层的特征提取,输出的特征图将被传递到池层进行特征选择和信息过滤。
常见的池化就是最大池化,指的是把这些卷积后的数据的最大值拿出来,也就是把它的最大特征拿出来。
假设池窗口为2X2,步长为2。
原始图像是:
该池变成:
在tensorflow中,池层的重要功能有:
tf.nn.max_pool(值,ksize,步幅,填充,数据格式,名称)
1.值:池层的输入。池层一般遵循卷积层,形状为【批次、高度、宽度、通道】。
2.ksize:池化窗口的大小,取一个四维向量,一般为[1,in_height,in_width,1]。
3.步幅:类似卷积,窗口滑动在每个维度的步长也是[1,步幅,步幅,1]。
4.填充:类似于卷积,您可以使用“有效的”
quo; 或者’SAME’。
这是tensorboard中卷积层和池化层的连接结构:
3、全连接层
全连接层与普通神经网络的结构相同,如图所示:
具体实现代码
卷积层、池化层与全连接层实现代码
def conv2d(x,W,step,pad): #用于进行卷积,x为输入值,w为卷积核return tf.nn.conv2d(x,W,strides = [1,step,step,1],padding = pad)
def max_pool_2X2(x,step,pad): #用于池化,x为输入值,step为步数
return tf.nn.max_pool(x,ksize = [1,2,2,1],strides= [1,step,step,1],padding = pad)
def weight_variable(shape): #用于获得W
initial = tf.truncated_normal(shape,stddev = 0.1) #从截断的正态分布中输出随机值
return tf.Variable(initial)
def bias_variable(shape): #获得bias
initial = tf.constant(0.1,shape=shape) #生成普通值
return tf.Variable(initial)
def add_layer(inputs,in_size,out_size,n_layer,activation_function = None,keep_prob = 1):
#用于添加全连接层
layer_name = layer_%s%n_layer
with tf.name_scope(layer_name):
with tf.name_scope("Weights"):
Weights = tf.Variable(tf.truncated_normal([in_size,out_size],stddev = 0.1),name = "Weights")
tf.summary.histogram(layer_name+"/weights",Weights)
with tf.name_scope("biases"):
biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name = "biases")
tf.summary.histogram(layer_name+"/biases",biases)
with tf.name_scope("Wx_plus_b"):
Wx_plus_b = tf.matmul(inputs,Weights) + biases
tf.summary.histogram(layer_name+"/Wx_plus_b",Wx_plus_b)
if activation_function == None :
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
print(activation_function)
outputs = tf.nn.dropout(outputs,keep_prob)
tf.summary.histogram(layer_name+"/outputs",outputs)
return outputs
def add_cnn_layer(inputs, in_z_dim, out_z_dim, n_layer, conv_step = 1, pool_step = 2, padding = "SAME"):
#用于生成卷积层和池化层
layer_name = layer_%s%n_layer
with tf.name_scope(layer_name):
with tf.name_scope("Weights"):
W_conv = weight_variable([5,5,in_z_dim,out_z_dim])
with tf.name_scope("biases"):
b_conv = bias_variable([out_z_dim])
with tf.name_scope("conv"):
#卷积层
h_conv = tf.nn.relu(conv2d(inputs, W_conv, conv_step, padding)+b_conv)
with tf.name_scope("pooling"):
#池化层
h_pool = max_pool_2X2(h_conv, pool_step, padding)
return h_pool
全部代码
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data",one_hot = "true")
def conv2d(x,W,step,pad):
return tf.nn.conv2d(x,W,strides = [1,step,step,1],padding = pad)
def max_pool_2X2(x,step,pad):
return tf.nn.max_pool(x,ksize = [1,2,2,1],strides= [1,step,step,1],padding = pad)
def weight_variable(shape):
initial = tf.truncated_normal(shape,stddev = 0.1) #从截断的正态分布中输出随机值
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1,shape=shape) #生成普通值
return tf.Variable(initial)
def add_layer(inputs,in_size,out_size,n_layer,activation_function = None,keep_prob = 1):
layer_name = layer_%s%n_layer
with tf.name_scope(layer_name):
with tf.name_scope("Weights"):
Weights = tf.Variable(tf.truncated_normal([in_size,out_size],stddev = 0.1),name = "Weights")
tf.summary.histogram(layer_name+"/weights",Weights)
with tf.name_scope("biases"):
biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name = "biases")
tf.summary.histogram(layer_name+"/biases",biases)
with tf.name_scope("Wx_plus_b"):
Wx_plus_b = tf.matmul(inputs,Weights) + biases
tf.summary.histogram(layer_name+"/Wx_plus_b",Wx_plus_b)
if activation_function == None :
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
print(activation_function)
outputs = tf.nn.dropout(outputs,keep_prob)
tf.summary.histogram(layer_name+"/outputs",outputs)
return outputs
def add_cnn_layer(inputs, in_z_dim, out_z_dim, n_layer, conv_step = 1, pool_step = 2, padding = "SAME"):
layer_name = layer_%s%n_layer
with tf.name_scope(layer_name):
with tf.name_scope("Weights"):
W_conv = weight_variable([5,5,in_z_dim,out_z_dim])
with tf.name_scope("biases"):
b_conv = bias_variable([out_z_dim])
with tf.name_scope("conv"):
h_conv = tf.nn.relu(conv2d(inputs, W_conv, conv_step, padding)+b_conv)
with tf.name_scope("pooling"):
h_pool = max_pool_2X2(h_conv, pool_step, padding)
return h_pool
def compute_accuracy(x_data,y_data):
global prediction
y_pre = sess.run(prediction,feed_dict={xs:x_data,keep_prob:1})
correct_prediction = tf.equal(tf.arg_max(y_data,1),tf.arg_max(y_pre,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
result = sess.run(accuracy,feed_dict = {xs:batch_xs,ys:batch_ys,keep_prob:1})
return result
keep_prob = tf.placeholder(tf.float32)
xs = tf.placeholder(tf.float32,[None,784])
ys = tf.placeholder(tf.float32,[None,10])
x_image = tf.reshape(xs,[-1,28,28,1])
h_pool1 = add_cnn_layer(x_image, in_z_dim = 1, out_z_dim = 32, n_layer = "cnn1",)
h_pool2 = add_cnn_layer(h_pool1, in_z_dim = 32, out_z_dim = 64, n_layer = "cnn2",)
h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])
h_fc1_drop = add_layer(h_pool2_flat, 7*7*64, 1024, "layer1", activation_function = tf.nn.relu, keep_prob = keep_prob)
prediction = add_layer(h_fc1_drop, 1024, 10, "layer2", activation_function = tf.nn.softmax, keep_prob = 1)
with tf.name_scope("loss"):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=ys,logits = prediction),name = loss)
tf.summary.scalar("loss",loss)
train = tf.train.AdamOptimizer(1e-4).minimize(loss)
init = tf.initialize_all_variables()
merged = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(init)
write = tf.summary.FileWriter("logs/",sess.graph)
for i in range(5000):
batch_xs,batch_ys = mnist.train.next_batch(100)
sess.run(train,feed_dict = {xs:batch_xs,ys:batch_ys,keep_prob:0.5})
if i % 100 == 0:
print(compute_accuracy(mnist.test.images,mnist.test.labels))
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