1. AlexNet网络
2. 总结
1. AlexNet网络（使用自定义）
# 深度卷积神经网络 (AlexNet)
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(), # 数据集为fashion_mnist图片，所以输入通道为1，如果是Imagnet图片，则通道数应为3
nn.MaxPool2d(kernel_size=3,stride=2),
nn.Conv2d(96,256,kernel_size=5,padding=2),nn.ReLU(), # 256为输出通道数
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__,'Output shape:\t', X.shape)
Conv2d Output shape: torch.Size([1, 96, 54, 54])
ReLU Output shape: torch.Size([1, 96, 54, 54])
MaxPool2d Output shape: torch.Size([1, 96, 26, 26])
Conv2d Output shape: torch.Size([1, 256, 26, 26])
ReLU Output shape: torch.Size([1, 256, 26, 26])
MaxPool2d Output shape: torch.Size([1, 256, 12, 12])
Conv2d Output shape: torch.Size([1, 384, 12, 12])
ReLU Output shape: torch.Size([1, 384, 12, 12])
Conv2d Output shape: torch.Size([1, 384, 12, 12])
ReLU Output shape: torch.Size([1, 384, 12, 12])
Conv2d Output shape: torch.Size([1, 256, 12, 12])
ReLU Output shape: torch.Size([1, 256, 12, 12])
MaxPool2d Output shape: torch.Size([1, 256, 5, 5])
Flatten Output shape: torch.Size([1, 6400])
Linear Output shape: torch.Size([1, 4096])
ReLU Output shape: torch.Size([1, 4096])
Dropout Output shape: torch.Size([1, 4096])
Linear Output shape: torch.Size([1, 4096])
ReLU Output shape: torch.Size([1, 4096])
Dropout Output shape: torch.Size([1, 4096])
Linear Output shape: torch.Size([1, 10])
# Fashion-MNIST图像的分辨率 低于ImageNet图像。将它们增加到224×224
batch_size = 128
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size,resize=224)
lr, num_epochs = 0.01, 10
d2l.train_ch6(net,train_iter,test_iter,num_epochs,lr,d2l.try_gpu())
loss 0.327, train acc 0.881, test acc 0.880
1666.6 examples/sec on cuda:0
