Python编程pytorch深度卷积神经网络AlexNet详解

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

net = nn.Sequential(
	# 这里，我们使用一个11*11的更大窗口来捕捉对象
	# 同时，步幅为4，以减少输出的高度和宽度
	# 另外，输出通道的数目远大于LeNet
	nn.Conv2d(1, 96, kernel_size=11, stride=4, padding=1), nn.ReLU(),
	nn.MaxPool2d(kernel_size=3, stride=2)
	# 减少卷积窗口，使用填充为2来使得输入与输出的高和宽一致，且增大输出通道数
	nn.Conv2d(96, 256, kernel_size=5, padding=2), nn.ReLU(),
	nn.MaxPool2d(kernel_size=3, stride=2)
	# 使用三个连续的卷积层和较小的卷积窗口
	# 除了最后的卷积层，输出通道的数量进一步增加
	# 在前两个卷积层之后，汇聚层不用于减少输入的高度和宽度
	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, 384, kernel_size=3, padding=1), nn.ReLU(),
	nn.MaxPool2d(kernel_size=3, stride=2),
	nn.Flatten(),
	# 这里，全连接层的输出数量是LeNet中的好几倍。使用dropout层来减轻过度拟合
	nn.Linear(6400, 4096), nn.ReLU(),
	nn.Dropout(p=0.5),
	nn.Linear(4096, 4096), nn.ReLU(),
	nn.Dropout(p=0.5),
	# 最后是输出层。由于这里使用Fashion-MNIST，所以用类别数位10
	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])

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 0.330, train acc 0.879, test acc 0.877
4163.0 examples/sec on cuda:0