net.py 文件源码

def __init__(self, bottom_width=4, ch=512, wscale=0.02):
        w = chainer.initializers.Normal(wscale)
        super(Discriminator, self).__init__()
        with self.init_scope():
            self.c0_0 = L.Convolution2D(3, ch//8, 3, 1, 1, initialW=w)
            self.c0_1 = L.Convolution2D(ch//8, ch//4, 4, 2, 1, initialW=w)
            self.c1_0 = L.Convolution2D(ch//4, ch//4, 3, 1, 1, initialW=w)
            self.c1_1 = L.Convolution2D(ch//4, ch//2, 4, 2, 1, initialW=w)
            self.c2_0 = L.Convolution2D(ch//2, ch//2, 3, 1, 1, initialW=w)
            self.c2_1 = L.Convolution2D(ch//2, ch//1, 4, 2, 1, initialW=w)
            self.c3_0 = L.Convolution2D(ch//1, ch//1, 3, 1, 1, initialW=w)
            self.l4 = L.Linear(bottom_width*bottom_width*ch, 1, initialW=w)
            self.bn0_1 = L.BatchNormalization(ch // 4, use_gamma=False)
            self.bn1_0 = L.BatchNormalization(ch // 4, use_gamma=False)
            self.bn1_1 = L.BatchNormalization(ch // 2, use_gamma=False)
            self.bn2_0 = L.BatchNormalization(ch // 2, use_gamma=False)
            self.bn2_1 = L.BatchNormalization(ch // 1, use_gamma=False)
            self.bn3_0 = L.BatchNormalization(ch // 1, use_gamma=False)