NiN模块

网络结构

代码实现

import torch
from torch import nn
from d2l import torch as d2l

def nin_block(in_channels,out_channels,kernel_size,strides,padding):
    return nn.Sequential(
        nn.Conv2d(in_channels,out_channels,kernel_size,strides,padding),
        nn.ReLU(),
        nn.Conv2d(out_channels,out_channels,kernel_size=1),nn.ReLU(),
        nn.Conv2d(out_channels,out_channels,kernel_size=1),nn.ReLU()
    )

net = nn.Sequential(
    nin_block(1,96,kernel_size = 11,strides=4,padding=0),
    nn.MaxPool2d(3,stride=2),
    nin_block(96,256,kernel_size=5,strides=1,padding=2),
    nn.MaxPool2d(3,stride=2),
    nin_block(256,384,kernel_size=3,strides=1,padding=1),
    nn.MaxPool2d(3,stride=2),
    nn.Dropout(0.5),
    nin_block(384,10,kernel_size=3,strides=1,padding=1),
    nn.AdaptiveAvgPool2d((1,1)),
    nn.Flatten()
)

X = torch.randn(1,1,224,224)
for layer in net:
    X = layer(X)
    print(layer.__class__.__name__,'output shape:\t',X.shape)

Sequential output shape: torch.Size([1, 96, 54, 54])
MaxPool2d output shape: torch.Size([1, 96, 26, 26])
Sequential output shape: torch.Size([1, 256, 26, 26])
MaxPool2d output shape: torch.Size([1, 256, 12, 12])
Sequential output shape: torch.Size([1, 384, 12, 12])
MaxPool2d output shape: torch.Size([1, 384, 5, 5])
Dropout output shape: torch.Size([1, 384, 5, 5])
Sequential output shape: torch.Size([1, 10, 5, 5])
AdaptiveAvgPool2d output shape: torch.Size([1, 10, 1, 1])
Flatten output shape: torch.Size([1, 10])