Created
November 29, 2017 05:05
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pytorch alexnet
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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 |
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