models.py

import chainer
import chainer.functions as F
import chainer.links as L

class GeneratorCIFAR(chainer.Chain):

    def __init__(self, size=None):

        super().__init__(
            dc1=L.Deconvolution2D(None, 256, 4, stride=1, pad=0, nobias=True),
            dc2=L.Deconvolution2D(256, 128, 4, stride=2, pad=1, nobias=True),
            dc3=L.Deconvolution2D(128, 64, 4, stride=2, pad=1, nobias=True),
            dc4=L.Deconvolution2D(64, 3, 4, stride=2, pad=1, nobias=True),
            bn_dc1=L.BatchNormalization(256),
            bn_dc2=L.BatchNormalization(128),
            bn_dc3=L.BatchNormalization(64)
        )

    def __call__(self, z):
        h = F.reshape(z, (z.shape[0], -1, 1, 1))
        h = F.relu(self.bn_dc1(self.dc1(h)))
        h = F.relu(self.bn_dc2(self.dc2(h)))
        h = F.relu(self.bn_dc3(self.dc3(h)))
        h = F.tanh(self.dc4(h))
        return h

class GeneratorMNIST(chainer.Chain):

    def __init__(self, size=None):

        super().__init__(
            dc1=L.Deconvolution2D(None, 256, 4, stride=1, pad=0, nobias=True),
            dc2=L.Deconvolution2D(256, 128, 4, stride=2, pad=1, nobias=True),
            dc3=L.Deconvolution2D(128, 64, 4, stride=2, pad=2, nobias=True),
            dc4=L.Deconvolution2D(64, 1, 4, stride=2, pad=1, nobias=True),
            bn_dc1=L.BatchNormalization(256),
            bn_dc2=L.BatchNormalization(128),
            bn_dc3=L.BatchNormalization(64)
        )

    def __call__(self, z):
        h = F.reshape(z, (z.shape[0], -1, 1, 1))
        h = F.relu(self.bn_dc1(self.dc1(h)))
        h = F.relu(self.bn_dc2(self.dc2(h)))
        h = F.relu(self.bn_dc3(self.dc3(h)))
        h = F.tanh(self.dc4(h))
        return h

class Discriminator(chainer.Chain):

    def __init__(self):
        super().__init__(
            c0 = L.Convolution2D(None, 64, 4, stride=2, pad=1, nobias=True),
            c1 = L.Convolution2D(64, 128, 4, stride=2, pad=1, nobias=True),
            c2 = L.Convolution2D(128, 256, 4, stride=2, pad=1, nobias=True),
            c3 = L.Convolution2D(256, 512, 4, stride=2, pad=1, nobias=True),
            l4l = L.Linear(None, 1),
            bn0 = L.BatchNormalization(64),
            bn1 = L.BatchNormalization(128),
            bn2 = L.BatchNormalization(256),
            bn3 = L.BatchNormalization(512)
        )
        
    def __call__(self, x):
        h = F.leaky_relu(self.c0(x))   
        h = F.leaky_relu(self.bn1(self.c1(h)))
        h = F.leaky_relu(self.bn2(self.c2(h)))
        h = F.leaky_relu(self.bn3(self.c3(h)))
        l = self.l4l(h)
        return l