我是靠谱客的博主 大意跳跳糖,这篇文章主要介绍李沐笔记(使用块的网络VGG),现在分享给大家,希望可以做个参考。

 

 

 

 

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import torch from d2l import torch as d2l from torch import nn # 卷积层个数 输入通道个数 输出通道个数 def vgg_block(num_convs,in_channels,out_channels): layars=[] for _ in range(num_convs): layars.append(nn.Conv2d(in_channels,out_channels,kernel_size=3,padding=1)) layars.append(nn.ReLU()) in_channels = out_channels layars.append(nn.MaxPool2d(kernel_size=2,stride=2)) return nn.Sequential(*layars) 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_channels return 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), nn.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) # 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 ratio = 4 small_conv_arch = [(pair[0], pair[1] // ratio) for pair in conv_arch] net = vgg(small_conv_arch) lr, num_epochs, batch_size = 0.05, 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())

 

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