def _build_discriminator(self): x = Input(shape=self.data_shape, name='data') d = render_gan_discriminator(x, n=self.discriminator_units, conv_repeat=self.discriminator_depth, dense=[512]) self.discriminator = Model([x], [d])
def test_render_gan_discriminator(): fake_shape = (10, 1, 64, 64) real_shape = (10, 1, 64, 64) fake = Input(batch_shape=fake_shape) real = Input(batch_shape=real_shape) output = render_gan_discriminator([fake, real]) model = Model([fake, real], output) model.compile('adam', 'mse') f = np.random.sample(fake_shape) r = np.random.sample(real_shape) y = np.random.sample((fake_shape[0], 1,)) # TODO: fix different batch sizes for input and output # y = np.random.sample((fake_shape[0] + real_shape[0], 1,)) with pytest.raises(ValueError): model.train_on_batch([f, r], y)
def test_render_gan_discriminator(): fake_shape = (10, 1, 64, 64) real_shape = (10, 1, 64, 64) fake = Input(batch_shape=fake_shape) real = Input(batch_shape=real_shape) output = render_gan_discriminator([fake, real]) model = Model([fake, real], output) model.compile('adam', 'mse') f = np.random.sample(fake_shape) r = np.random.sample(real_shape) y = np.random.sample(( fake_shape[0], 1, )) # TODO: fix different batch sizes for input and output # y = np.random.sample((fake_shape[0] + real_shape[0], 1,)) with pytest.raises(ValueError): model.train_on_batch([f, r], y)
def _build_discriminator(self): x = Input(shape=self.data_shape, name='data') d = render_gan_discriminator(x, n=self.discriminator_units, conv_repeat=self.discriminator_depth, dense=[512]) self.discriminator = Model([x], [d])