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Chapter7 Modern Convolutional Neural Networks
7.2 Network Using Blocks: VGG import matplotlib.pyplot as plt
import torch
from torch import nn
from d2l import torch as d2ldef vgg_block(num_convs, in_channels, out_channels):layers []for _ in range(num_convs):layers.append(nn.Conv2d(in_channels, out_channels,kernel_size3, padding1))layers.append(nn.ReLU())in_channels out_channelslayers.append(nn.MaxPool2d(kernel_size2,stride2))return nn.Sequential(*layers)#可变参数#指定每个vgg块的卷积层个数和输出通道个数
conv_arch ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))def vgg(conv_arch):conv_blks []in_channels 1# 卷积层部分for (num_convs, out_channels) in conv_arch:conv_blks.append(vgg_block(num_convs, in_channels, out_channels))in_channels out_channelsreturn nn.Sequential(*conv_blks, nn.Flatten(),# 全连接层部分nn.Linear(out_channels * 7 * 7, 4096), nn.ReLU(), nn.Dropout(0.5),#the spatial dimensions of the input tensor after the convolutional blocks are reduced to 7x7nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(0.5),nn.Linear(4096, 10))net vgg(conv_arch)
X torch.randn(size(1, 1, 224, 224))
for blk in net:X blk(X)print(blk.__class__.__name__,output shape:\t,X.shape)ratio 4
small_conv_arch [(pair[0], pair[1] // ratio) for pair in conv_arch]#由于VGG-11比AlexNet计算量更大因此构建一个通道数较少的网络
net vgg(small_conv_arch)#训练
lr, num_epochs, batch_size 0.05, 10, 128
train_iter, test_iter d2l.load_data_fashion_mnist(batch_size, resize224)
d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
plt.show()训练结果:
参考文献:VGG原始论文
