Created
March 4, 2020 10:40
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Siamese Network
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class SiameseNetwork(nn.Module): | |
def __init__(self): | |
super(SiameseNetwork, self).__init__() | |
self.cnn1 = nn.Sequential( | |
nn.ReflectionPad2d(1), | |
nn.Conv2d(1, 4, kernel_size=3), | |
nn.ReLU(inplace=True), | |
nn.BatchNorm2d(4), | |
nn.ReflectionPad2d(1), | |
nn.Conv2d(4, 8, kernel_size=3), | |
nn.ReLU(inplace=True), | |
nn.BatchNorm2d(8), | |
nn.ReflectionPad2d(1), | |
nn.Conv2d(8, 8, kernel_size=3), | |
nn.ReLU(inplace=True), | |
nn.BatchNorm2d(8), | |
) | |
self.fc1 = nn.Sequential( | |
nn.Linear(8*256*256, 500), | |
nn.ReLU(inplace=True), | |
nn.Linear(500, 500), | |
nn.ReLU(inplace=True), | |
nn.Linear(500, 32)) | |
def forward_once(self, x): | |
output = self.cnn1(x) | |
output = output.view(output.size()[0], -1) | |
output = self.fc1(output) | |
return output | |
def forward(self, input1, input2): | |
output1 = self.forward_once(input1) | |
output2 = self.forward_once(input2) | |
return output1, output2 |
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