使用pytorch实现CNN

使用pytorch构建神经网络系列

第四章 使用pytorch实现CNN

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1.卷积层

input channel = 1
Number of kernel = 3
kernel_size=3* 3
stride=1
padding=0

layer = nn.Conv2d(1,3,kernel_size=3, stride=1, padding=0) # 1 指的是input channel，3指的是kernel数量
x = torch.rand(1,1,28,28)
out = layer.forward(x)
out.shape
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2
3
4

torch.Size([1, 3, 26, 26])
1

padding = 1

layer = nn.Conv2d(1,3,kernel_size=3, stride=1, padding=1) # 1 指的是input channel，3指的是kernel数量
x = torch.rand(1,1,28,28)
out = layer.forward(x)
out.shape
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2
3
4

torch.Size([1, 3, 28, 28])
1

stride=2, padding=1

layer = nn.Conv2d(1,3,kernel_size=3, stride=2, padding=1) # 1 指的是input channel，3指的是kernel数量
x = torch.rand(1,1,28,28)
out = layer(x)
out.shape
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2
3
4

torch.Size([1, 3, 14, 14])
1

Inner weight & bias

layer.weight
1

Parameter containing:
tensor([[[[ 0.2707, -0.0963,  0.1320],
          [-0.3079,  0.2748, -0.2578],
          [-0.2599, -0.2093,  0.1575]]],


        [[[-0.1442, -0.1604, -0.0696],
          [-0.1756, -0.2574, -0.0920],
          [-0.0144,  0.2705,  0.1098]]],


        [[[-0.2493, -0.1132,  0.3068],
          [-0.0886, -0.1119,  0.2752],
          [ 0.0672, -0.2881, -0.1102]]]], requires_grad=True)
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layer.weight.shape, layer.bias.shape
1

(torch.Size([3, 1, 3, 3]), torch.Size([3]))
1

2.Pooling

reduce size

Max pooling

y = out
layer = nn.MaxPool2d(2,stride=2)
out = layer(y)
y.shape,out.shape
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(torch.Size([1, 3, 14, 14]), torch.Size([1, 3, 7, 7]))
1

upsampling
放大采样：

x = out
out = F.interpolate(x, scale_factor=2, mode='nearest')
out.shape
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3

torch.Size([1, 3, 14, 14])
1

3.ReLU

inplace= True 节省内存空间

layer = nn.ReLU(inplace= True)
1

4.batch norm

利于我们搜索最优解

Feature scaling
image Normalization：
RGB三通道数据分别减去均值除以标准差：

Normalization：

Batch Normalization
belta 和 gama 需要记录信息进行梯度更新，均值方差不需要

x = torch.rand(100,16,784) #16 channels
layer = nn.BatchNorm1d(16)
out = layer(x)
layer.running_mean, layer.running_var
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(tensor([0.0500, 0.0502, 0.0500, 0.0499, 0.0501, 0.0501, 0.0500, 0.0499, 0.0501,
         0.0498, 0.0500, 0.0497, 0.0502, 0.0501, 0.0500, 0.0499]),
 tensor([0.9083, 0.9083, 0.9083, 0.9084, 0.9084, 0.9083, 0.9083, 0.9083, 0.9083,
         0.9084, 0.9083, 0.9083, 0.9084, 0.9083, 0.9084, 0.9083]))
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Pipeline:

'affine’设置为True表示需要梯度更新belta 和 gama

只在training的时候更新belta 和 gama，在test的时候要切换到evaluation：

layer.eval()
1

Advantages
​Converge faster
Better performance
Robust

5.ResNet

6.nn.modules

直接调用一些基本的类
nn.Linear
nn.BatchNorm2d
nn.Conv2d

Every Layer is nn.Module
Container
net(x)

parameters
查看参数：

modules
modules: all nodes
children: direct children
嵌套

to(device)

save and load

train/test

implement own layer
可以在Sequential 中直接调用：

own linear layer
是创建自己的网络层

7.数据增强

Flip，Rotate，Random Move & Crop，GAN
torchvision包：
transforms.Compose
Flip

Rotate

缩放：
transforms.Resize
裁剪：
transforms.RandomCrop
增加noise
！Data argumentation will help But not too much

参考：网易云课程