I’m learning how to implement AlexNet to train a set of medical images.

Here’s the model.

class AlexNet(nn.Module):

`def __init__(self, num_classes=1000): super(AlexNet, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv2d(64, 192, kernel_size=5, padding=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), ) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(256 * 6 * 6, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Linear(4096, num_classes), ) def forward(self, x): x = self.features(x) x = x.view(x.size(0), 256 * 6 * 6) x = self.classifier(x) return x`

I encountered this error:

expected stride to be a single integer value or a list of 3 values to match the convolution dimensions, but got stride=[4, 4]

Someone suggested me that I can put a fixed stride size on the nn.Conv2d.forward function. I want to do it without rewriting the Conv2d function or modify the library.

I know this is probably a very simple Python problem, but I have no idea how to come up with the keyword for this problem. Thanks in advance.