軟件工程學習進度第九周暨暑假學習進度之第九周彙總

時間 2019-11-18

標籤軟件工程學習進度第九暑假彙總简体版

原文原文鏈接

本週學習的是U-net神經網絡，據瞭解，U-net論文的做者是參加ISBI競賽取得效果後發表的文章供你們學習，論文原文連接：git

U-net最先用做生物圖像識別，後來在目標檢測、圖像轉換以及Tone Mapping,Reverse Tone Mapping不少地方都有應用，它區別於早期卷積神經網絡的特色是早期的卷積層結果和最後基層的結果是採用級聯的形式做爲新的神經網絡層。而U-net網絡名稱來源於這樣一張圖：

（圖片來源於百度）

前面的下采樣，提取出信息，後面進行具備「新特性」圖像的重建，由於整個形狀酷似U型因此被稱爲U-net

由圖中能夠看出，U-net主要由conv+relu,maxpooling,up-conv,conv1*1幾個部分構成，使用TensorFlow函數化這幾個部分結果以下：

一、conv+relu

1 def conv_relu_layer(net,numfilters,name): 
2     network = tf.layers.conv2d(net, 
3                         activation=tf.nn.relu, 
4                         filters= numfilters,     
5                         kernel_size=(3,3), 
6                         padding='Valid', 
7                         name= "{}_conv_relu".format(name))
8      return network

二、maxpoolinggithub

1 def maxpool(net,name):
2     network = tf.layers.max_pooling2d(net,
3                     pool_size= (2,2),
4                     strides = (2,2),
5                     padding = 'valid',
6                     name = "{}_maxpool".format(name))
7     return network

三、up-conv網絡

1 def up_conv(net,numfilters,name):
2     network = tf.layers.conv2d_transpose(net,
3                     filters = numfilters,
4                     kernel_size= (2,2),
5                     strides= (2,2),
6                     padding= 'valid',
7                     activation= tf.nn.relu,
8                     name = "{}_up_conv".format(name))
9     return network

四、copy-prop架構

1 def copy_crop(skip_connect,net):
2     skip_connect_shape = skip_connect.get_shape()
3     net_shape = net.get_shape()
4     print(net_shape[1])
5     size = [-1,net_shape[1].value,net_shape[2].value,-1]
6     skip_connect_crop = tf.slice(skip_connect,[0,0,0,0],size)
7     concat = tf.concat([skip_connect_crop,net],axis=3)
8     return concat

五、conv1*1app

def conv1x1(net,numfilters,name):
    return tf.layers.conv2d(net,filters=numfilters,strides=(1,1),kernel_size=(1,1),name = "{}_conv1x1".format(name),padding='SAME')

 1 #define input data
 2 input  = tf.placeholder(dtype=tf.float32,shape = (64,572,572,3))
 3 
 4 
 5 #define downsample path
 6 network = conv_relu_layer(input,numfilters=64,name='lev1_layer1')
 7 skip_con1 = conv_relu_layer(network,numfilters=64,name='lev1_layer2')
 8 network = maxpool(skip_con1,'lev2_layer1')
 9 network = conv_relu_layer(network,128,'lev2_layer2')
10 skip_con2 = conv_relu_layer(network,128,'lev2_layer3')
11 network = maxpool(skip_con2,'lev3_layer1')
12 network = conv_relu_layer(network,256,'lev3_layer1')
13 skip_con3 = conv_relu_layer(network,256,'lev3_layer2')
14 network = maxpool(skip_con3,'lev4_layer1')
15 network = conv_relu_layer(network,512,'lev4_layer2')
16 skip_con4 = conv_relu_layer(network,512,'lev4_layer3')
17 network = maxpool(skip_con4,'lev5_layer1')
18 network = conv_relu_layer(network,1024,'lev5_layer2')
19 network = conv_relu_layer(network,1024,'lev5_layer3')
20 
21 #define upsample path
22 network = up_conv(network,512,'lev6_layer1')
23 network = copy_crop(skip_con4,network)
24 network = conv_relu_layer(network,numfilters=512,name='lev6_layer2')
25 network = conv_relu_layer(network,numfilters=512,name='lev6_layer3')
26 
27 network = up_conv(network,256,name='lev7_layer1')
28 network = copy_crop(skip_con3,network)
29 network = conv_relu_layer(network,256,name='lev7_layer2')
30 network = conv_relu_layer(network,256,'lev7_layer3')
31 
32 
33 network = up_conv(network,128,name='lev8_layer1')
34 network = copy_crop(skip_con2,network)
35 network = conv_relu_layer(network,128,name='lev8_layer2')
36 network = conv_relu_layer(network,128,'lev8_layer3')
37 
38 
39 network = up_conv(network,64,name='lev9_layer1')
40 network = copy_crop(skip_con1,network)
41 network = conv_relu_layer(network,64,name='lev9_layer2')
42 network = conv_relu_layer(network,64,name='lev9_layer3')
43 network = conv1x1(network,2,name='lev9_layer4')