conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))这个代码是指滤波器吗
时间: 2023-10-24 17:09:30 浏览: 22
不完全是。这段代码是指深度卷积神经网络中的卷积层的结构,每个元组表示一层卷积层的配置,第一个数字表示卷积层的数量,第二个数字表示每个卷积层使用的滤波器数量。具体来说,这个conv_arch表示这个网络共有5个卷积层,第一层有64个滤波器,第二层有128个滤波器,第三、四、五层分别有256、512、512个滤波器。
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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。
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.