AlexNet网络结构

网络结构

代码实现

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

net = nn.Sequential(
    nn.Conv2d(1,96,kernel_size = 11,stride = 4,padding =1),nn.ReLU(),
    nn.MaxPool2d(kernel_size= 3,stride = 2),
    nn.Conv2d(96,256,kernel_size = 5,padding = 2),nn.ReLU(),
    nn.MaxPool2d(kernel_size = 3,stride = 2),
    nn.Conv2d(256,384,kernel_size = 3,padding = 1),nn.ReLU(),
    nn.Conv2d(384,384,kernel_size = 3,padding = 1),nn.ReLU(),
    nn.Conv2d(384,256,kernel_size = 3,padding = 1),nn.ReLU(),
    nn.MaxPool2d(kernel_size = 3,stride = 2),
    nn.Flatten(),
    nn.Linear(6400,4096),nn.ReLU(),
    nn.Dropout(p=0.5),
    nn.Linear(4096,4096),nn.ReLU(),
    nn.Dropout(p=0.5),
    nn.Linear(4096,10)
)

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

Conv2d out shape : torch.Size([1, 96, 54, 54])
ReLU out shape : torch.Size([1, 96, 54, 54])
MaxPool2d out shape : torch.Size([1, 96, 26, 26])
Conv2d out shape : torch.Size([1, 256, 26, 26])
ReLU out shape : torch.Size([1, 256, 26, 26])
MaxPool2d out shape : torch.Size([1, 256, 12, 12])
Conv2d out shape : torch.Size([1, 384, 12, 12])
ReLU out shape : torch.Size([1, 384, 12, 12])
Conv2d out shape : torch.Size([1, 384, 12, 12])
ReLU out shape : torch.Size([1, 384, 12, 12])
Conv2d out shape : torch.Size([1, 256, 12, 12])
ReLU out shape : torch.Size([1, 256, 12, 12])
MaxPool2d out shape : torch.Size([1, 256, 5, 5])
Flatten out shape : torch.Size([1, 6400])
Linear out shape : torch.Size([1, 4096])
ReLU out shape : torch.Size([1, 4096])
Dropout out shape : torch.Size([1, 4096])
Linear out shape : torch.Size([1, 4096])
ReLU out shape : torch.Size([1, 4096])
Dropout out shape : torch.Size([1, 4096])
Linear out shape : torch.Size([1, 10])