def __init__(self, dis_input_dim=Encodered_dim, nlayers=dis_layers, hidden_dim=dis_hidden_dim, input_dropout=dis_input_dropout, dropout=dis_dropout):
    super(Discriminator, self).__init__()
    self.dis_input = dis_input_dim
    layers = [nn.Dropout(input_dropout)]
    for i in range(0, (nlayers + 1)):
        input_dim = (self.dis_input if (i == 0) else hidden_dim)
        output_dim = (1 if (i == nlayers) else hidden_dim)
        layers.append(nn.Linear(input_dim, output_dim))
        if (i < nlayers):
            layers.append(nn.LeakyReLU(0.2))
            layers.append(nn.Dropout(dropout))
    layers.append(nn.Sigmoid())
    self.layers = nn.Sequential(*layers)
def __init__(self, dis_input_dim=Encodered_dim, nlayers=dis_layers, hidden_dim=dis_hidden_dim, input_dropout=dis_input_dropout, dropout=dis_dropout):
    super(Discriminator, self).__init__()
    self.dis_input = dis_input_dim
    layers = [nn.Dropout(input_dropout)]
    for i in range(0, (nlayers + 1)):
        input_dim = (self.dis_input if (i == 0) else hidden_dim)
        output_dim = (1 if (i == nlayers) else hidden_dim)
        layers.append(nn.Linear(input_dim, output_dim))
        if (i < nlayers):
            layers.append(nn.LeakyReLU(0.2))
            layers.append(nn.Dropout(dropout))
    layers.append(nn.Sigmoid())
    self.layers = nn.Sequential(*layers)