Introduction to DCGAN using PyTorch

support_blocks.py

class conv_trans_block(nn.Module):
  def __init__(self,in_channels,out_channels,kernal_size=4,stride=2,padding=1):
    super(conv_trans_block,self).__init__()
    self.block=nn.Sequential(
        nn.ConvTranspose2d(in_channels,out_channels,kernal_size,stride,padding),
        nn.BatchNorm2d(out_channels),
        nn.ReLU())
  def forward(self,x):
    return self.block(x)

class conv_block(nn.Module):
  def __init__(self,in_channels,out_channels,kernal_size=4,stride=2,padding=1):
    super(conv_block,self).__init__()
    self.block=nn.Sequential(
        nn.Conv2d(in_channels,out_channels,kernal_size,stride,padding),
        nn.BatchNorm2d(out_channels),
        nn.LeakyReLU(0.2))
  def forward(self,x):
    return self.block(x)