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])