gan.py 文件源码

def __init__(self):
        super(Discriminator, self).__init__()

        self.conv0 = nn.Conv1d(nc, ndf, 4, 2, 1, bias=False)
        self.conv1 = nn.Conv1d(ndf, ndf * 2, 4, 2, 1, bias=False)
        self.conv2 = nn.Conv1d(ndf * 2, ndf * 4, 4, 2, 1, bias=False)
        self.conv3 = nn.Conv1d(ndf * 4, ndf * 8, 4, 2, 1, bias=False)

        self.fc0_size = 512 * 128
        self.fc0 = nn.Linear(self.fc0_size, 100)

        self.relu = nn.LeakyReLU(0.2, inplace=True)

        self.bn1 = nn.BatchNorm1d(ndf * 2)
        self.bn2 = nn.BatchNorm1d(ndf * 4)
        self.bn3 = nn.BatchNorm1d(ndf * 8)

        self.sigmoid = nn.Sigmoid()

        self.apply(weights_init)

        self.optimizer = optim.Adam(self.parameters(), lr=learning_rate, betas=(beta_1, beta_2))
        #self.optimizer = optim.RMSprop(self.parameters(), lr=learning_rate, alpha=beta_2)