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())

# 查看每个块的输出形状
X = torch.rand(size=(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])

# 训练模型
lr, num_epochs, batch_size = 0.1, 10, 128
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)    
d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())

loss 0.341, train acc 0.874, test acc 0.834
1627.8 examples/sec on cuda:0

1. NiN网络¶

2. 总结¶

1. NiN网络（使用自定义）¶