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))
我们构造一个 单通道数据,来观察每一层输出的形状
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。 我们将它们增加到 $224 \times 224$
batch_size = 128
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)
训练AlexNet
lr, num_epochs = 0.01, 10
d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
loss 0.332, train acc 0.877, test acc 0.880 4103.4 examples/sec on cuda:0