Convolutional Discriminator
With the DCGAN's generator ready, the last step before you can proceed to training it is to define the convolutional discriminator.
torch.nn is imported for you under its usual alias. To build the convolutional discriminator, you will use a custom gc_disc_block() function which returns a block of a convolution followed by a batch norm and the leaky ReLU activation. You can inspect dc_disc_block()'s definition below.
def dc_disc_block(in_dim, out_dim, kernel_size, stride):
return nn.Sequential(
nn.Conv2d(in_dim, out_dim, kernel_size, stride=stride),
nn.BatchNorm2d(out_dim),
nn.LeakyReLU(0.2),
)
Questo esercizio fa parte del corso
Deep Learning for Images with PyTorch
Istruzioni dell'esercizio
- Add the first discriminator block using the custom
dc_disc_block()function with3input feature maps and512output feature maps. - Add the convolutional layer with the output size of
1. - In the
forward()method, pass the input through the sequential block you defined in__init__().
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
class DCDiscriminator(nn.Module):
def __init__(self, kernel_size=4, stride=2):
super(DCDiscriminator, self).__init__()
self.disc = nn.Sequential(
# Add first discriminator block
dc_disc_block(3, 512, kernel_size, stride),
dc_disc_block(512, 1024, kernel_size, stride),
# Add a convolution
nn.Conv2d(1024, 1, kernel_size, stride=stride),
)
def forward(self, x):
# Pass input through sequential block
x = ____
return x.view(len(x), -1)