vanilla gan discriminator

vanilla_gan_discriminator.py

class Discriminator(nn.Module):
    def __init__(self, input_dim, layers):
        """A discriminator for discerning real from generated samples.

        params:
            input_dim (int): width of the input
            layers (List[int]): A list of layer widths including output width

        Output activation is Sigmoid.
        """
        super(Discriminator, self).__init__()
        self.input_dim = input_dim
        self._init_layers(layers)

    def _init_layers(self, layers):
        """Initialize the layers and store as self.module_list."""
        self.module_list = nn.ModuleList()
        last_layer = self.input_dim
        for index, width in enumerate(layers):
            self.module_list.append(nn.Linear(last_layer, width))
            last_layer = width
            if index + 1 != len(layers):
                self.module_list.append(nn.LeakyReLU())
        else:
            self.module_list.append(nn.Sigmoid())

    def forward(self, input_tensor):
        """Forward pass; map samples to confidence they are real [0, 1]."""
        intermediate = input_tensor
        for layer in self.module_list:
            intermediate = layer(intermediate)
        return intermediate