wgan.py 文件源码

def __init__(self, bottom_width=8, ch=512, wscale=0.005):
        w = chainer.initializers.Normal(wscale)
        super(DiscriminatorPFN, self).__init__(
            c0_0=L.Convolution2D(3, ch // 8, 3, 1, 1, initialW=w),
            c0_1=L.Convolution2D(ch // 8, ch // 4, 4, 2, 1, initialW=w),
            c1_0=L.Convolution2D(ch // 4, ch // 4, 3, 1, 1, initialW=w),
            c1_1=L.Convolution2D(ch // 4, ch // 2, 4, 2, 1, initialW=w),
            c2_0=L.Convolution2D(ch // 2, ch // 2, 3, 1, 1, initialW=w),
            c2_1=L.Convolution2D(ch // 2, ch // 1, 4, 2, 1, initialW=w),
            c3_0=L.Convolution2D(ch // 1, ch // 1, 3, 1, 1, initialW=w),
            l4=L.Linear(bottom_width * bottom_width * ch, 1, initialW=w),
            bn0_1=L.BatchNormalization(ch // 4, use_gamma=False),
            bn1_0=L.BatchNormalization(ch // 4, use_gamma=False),
            bn1_1=L.BatchNormalization(ch // 2, use_gamma=False),
            bn2_0=L.BatchNormalization(ch // 2, use_gamma=False),
            bn2_1=L.BatchNormalization(ch // 1, use_gamma=False),
            bn3_0=L.BatchNormalization(ch // 1, use_gamma=False),
        )