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discriminator.py
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discriminator.py
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import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator,self).__init__()
self.main = nn.Sequential(
nn.Conv2d(1,64,4,2,1,bias =False),
nn.LeakyReLU(negative_slope= 0.2,inplace=True),
nn.Conv2d(64,128,4,2,1,bias =False),
nn.BatchNorm2d(128),
nn.LeakyReLU(negative_slope= 0.2,inplace=True),
nn.Conv2d(128,256,4,2,1,bias =False),
nn.BatchNorm2d(256),
nn.LeakyReLU(negative_slope= 0.2,inplace=True),
nn.Conv2d(256,512,4,2,1,bias =False),
nn.BatchNorm2d(512),
nn.LeakyReLU(negative_slope= 0.2,inplace=True),
nn.Conv2d(512,1,4,1,0,bias =False),
nn.Sigmoid()
)
def forward(self,x):
output = self.main(x)
return output.view(-1) #creating into vector<flattern>