卷积神经网络常见的层

类型	名称	作用
Conv	卷积层	提取特征
ReLU	激活层	激活
Pool	池化	——
BatchNorm	批量归一化	——
Linear(Full Connect)	全连接层	——
Dropout	——	——
ConvTranspose	反卷积	——

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pytorch中各种层的用法

卷积 Convolution

参考：https://pytorch.org/docs/stable/_modules/torch/nn/modules/conv.html#Conv1d

卷积类型	作用
torrch.nn.Conv1d	一维卷积
torch.nn.Conv2d	二维卷积
torch.nn.Conv3d	三维卷积
torch.nn.ConvTranspose1d	一维反卷积
torch.nn.ConvTranspose2d	二维反卷积
torch.nn.ConvTranspose3d	三维反卷积

示例：

m = nn.Conv1d(in_channels=16,
              out_channels=33,
              kernel_size=3,
              padding=1,
              stride=1)
input = torch.randn(1, 16, 50)
output = m(input)
print(output.size())

# nn.Conv2d()
m = nn.Conv2d(in_channels=16,
              out_channels=33,
              kernel_size=3,
              stride=2)
input = torch.randn(20, 16, 50, 100)
output = m(input)
print(output.size())


m = nn.Conv2d(in_channels=16,
              out_channels=33,
              kernel_size=(3, 5),
              stride=(2, 1),
              padding=(4, 2),
              dilation=(3, 1))
output = m(input)
print(output.size())

# nn.ConvTranspose2d()
# 2d transpose convolution operator
m = nn.ConvTranspose2d(in_channels=16,
                       out_channels=33,
                       kernel_size=3,
                       stride=2)
input = torch.randn(20, 16, 50, 100)
output = m(input)   # (20,33,101,201)
print(output.size())

# exact output size can be also specified as an argument
input = torch.randn(1, 16, 12, 12)
downsample = nn.Conv2d(in_channels=16,
                       out_channels=16,
                       kernel_size=3,
                       stride=2,
                       padding=1)
upsample = nn.ConvTranspose2d(in_channels=16,
                              out_channels=16,
                              kernel_size=3,
                              stride=2,
                              padding=1,
                              output_padding=1)
output = downsample(input)  # 下采样， (1x16x6x6)
print(output.size())

output = upsample(output)   # 上采样， (1x16x12x12)
print(output.size())

输出：

image.png