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Weight Init Update DCGANs PyTorch #149

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Weight Init Update DCGANs PyTorch #149

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22 changes: 12 additions & 10 deletions ML/Pytorch/GANs/2. DCGAN/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ def __init__(self, channels_img, features_d):
super(Discriminator, self).__init__()
self.disc = nn.Sequential(
# input: N x channels_img x 64 x 64
nn.Conv2d(channels_img, features_d, kernel_size=4, stride=2, padding=1),
nn.Conv2d(channels_img, features_d, kernel_size=4, stride=2, padding=1, bias=False),
nn.LeakyReLU(0.2),
# _block(in_channels, out_channels, kernel_size, stride, padding)
self._block(features_d, features_d * 2, 4, 2, 1),
Expand All @@ -36,8 +36,8 @@ def _block(self, in_channels, out_channels, kernel_size, stride, padding):
padding,
bias=False,
),
# nn.BatchNorm2d(out_channels),
nn.LeakyReLU(0.2),
nn.BatchNorm2d(out_channels),
nn.LeakyReLU(0.2, True),
)

def forward(self, x):
Expand All @@ -54,7 +54,7 @@ def __init__(self, channels_noise, channels_img, features_g):
self._block(features_g * 8, features_g * 4, 4, 2, 1), # img: 16x16
self._block(features_g * 4, features_g * 2, 4, 2, 1), # img: 32x32
nn.ConvTranspose2d(
features_g * 2, channels_img, kernel_size=4, stride=2, padding=1
features_g * 2, channels_img, kernel_size=4, stride=2, padding=1, bias=False
),
# Output: N x channels_img x 64 x 64
nn.Tanh(),
Expand All @@ -70,19 +70,21 @@ def _block(self, in_channels, out_channels, kernel_size, stride, padding):
padding,
bias=False,
),
# nn.BatchNorm2d(out_channels),
nn.BatchNorm2d(out_channels),
nn.ReLU(),
)

def forward(self, x):
return self.net(x)


def initialize_weights(model):
# Initializes weights according to the DCGAN paper
for m in model.modules():
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d, nn.BatchNorm2d)):
nn.init.normal_(m.weight.data, 0.0, 0.02)
def initialize_weights(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1: # this will prevent Discriminator form converging to 0
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)


def test():
Expand Down