NiN块
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())
NiN模型
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 0.331, train acc 0.878, test acc 0.878 3182.2 examples/sec on cuda:0