def __init__(self, *args, **kwargs): self.discriminator = None self.generator = None self.loss = None self.trainer = None BaseGAN.__init__(self, *args, **kwargs) self.x, self.y = self.inputs.next()
def __init__(self, latent=None, x=None, *args, **kwargs): self.discriminator = None self.latent = None self.generator = None self.loss = None self.trainer = None self.session = None self.latent = latent self.x = x BaseGAN.__init__(self, *args, **kwargs)
def __init__(self, latent=None, x=None, *args, **kwargs): self.discriminator = None self.latent = None self.generator = None self.loss = None self.trainer = None self.session = None self.latent = latent self.x = x BaseGAN.__init__(self, *args, **kwargs)
def __init__(self, *args, **kwargs): BaseGAN.__init__(self, *args, **kwargs)
def __init__(self, *args, **kwargs): self.frames = kwargs.pop('frames') BaseGAN.__init__(self, *args, **kwargs)
def __init__(self, *args, **kwargs): self.per_sample_frames = 3 self.frames = kwargs.pop('frames') self.per_sample_frames = kwargs.pop('per_sample_frames') BaseGAN.__init__(self, *args, **kwargs)
def __init__(self, *args, **kwargs): BaseGAN.__init__(self, *args, **kwargs) self.x = self.inputs.next()