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())

这段代码是一个使用VGG网络在Fashion-MNIST数据集上进行训练的示例。首先，它将给定的卷积结构(conv_arch)缩小了4倍(ratio=4)，得到了一个较小的卷积结构(small_conv_arch)。然后，它使用这个较小的卷积结构构造了一个VGG网络(net)。接着，它使用d2l.load_data_fashion_mnist函数加载Fashion-MNIST数据集，并指定了批量大小和图像大小。最后，它使用d2l.train_ch6函数对构造的VGG网络进行训练，并在训练和测试数据集上进行了评估。

相关问题

small_conv_arch = [(pair[0],pair[1]//ratio) for pair in conv_arch]

This code takes the convolutional architecture specified in the variable `conv_arch` and down-samples it by a factor of `ratio`.

Specifically, it creates a new list `small_conv_arch` where each pair of `(num_filters, kernel_size)` in `conv_arch` has its second element (the kernel size) divided by `ratio`. The first element (the number of filters) remains the same.

This results in a smaller architecture that reduces the spatial dimensions of the input more quickly, while maintaining the same number of filters.

import torch from torch import nn import d2l def vgg_block(num_convs, in_channels, out_channels): layers = [] for _ in range(num_convs): layers.append(nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)) layers.append(nn.ReLU()) in_channels = out_channels layers.append(nn.MaxPool2d(kernel_size=2,stride=2)) return nn.Sequential(*layers) conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512)) def vgg(conv_arch): conv_blks = [] in_channels = 3 # 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, 2048), nn.ReLU(), nn.Dropout(0.5), nn.Linear(2048, 10)) 这部分代码的参数如何修改会提高准确率，net = vgg(conv_arch) X = torch.randn(size=(1, 3, 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] net = vgg(small_conv_arch)这一部分代码有需要改哪些参数会提高准确率，lr, num_epochs, batch_size = 0.01, 60, 64 train_iter, test_iter = d2l.load_data_cifar10(batch_size, resize=224) d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())这一部分代码中的参怎么改会提高准确率？

对于第一部分代码，可以修改卷积层数量、卷积核大小、全连接层数量以及Dropout概率等参数来提高准确率。例如，可以增加卷积层数量、减小卷积核大小、增加全连接层数量或者减小Dropout概率等。

对于第二部分代码，可以调整小网络的结构、增加卷积层数量或者增加训练数据量等来提高准确率。

对于第三部分代码，可以尝试调整学习率、训练轮数、批量大小等参数来提高准确率。例如，可以增加学习率、增加训练轮数或者减小批量大小等。