def test_gan_with_vae_forward_pass():
with pytest.raises(DazeModelTypeError):
model = dz.GAN(
CifarDecoder(),
ConvolutionalEncoder(),
100,
forward_pass_func=dz.forward_pass.probabilistic_encode_decode())
def test_gan_with_gen_loss_in_disc_loss():
with pytest.raises(DazeModelTypeError):
model = dz.GAN(CifarDecoder(),
ConvolutionalEncoder(),
100,
discriminator_loss=[dz.loss.vanilla_generator_loss()])
def test_gan_with_disc_loss_in_gen_loss():
with pytest.raises(DazeModelTypeError):
model = dz.GAN(CifarDecoder(),
ConvolutionalEncoder(),
100,
generator_loss=[dz.loss.one_sided_label_smoothing()])
def test_gan_with_ae_loss_in_disc_loss():
with pytest.raises(DazeModelTypeError):
model = dz.GAN(CifarDecoder(),
ConvolutionalEncoder(),
100,
discriminator_loss=[dz.loss.reconstruction()])
def test_gan_with_ae_loss_in_gen_loss():
with pytest.raises(DazeModelTypeError):
model = dz.GAN(CifarDecoder(),
ConvolutionalEncoder(),
100,
generator_loss=[dz.loss.contractive(.1)])
def test_gan_instance_noise():
model = dz.GAN(CifarDecoder(), ConvolutionalEncoder(), noise_dim=100, forward_pass_func=dz.forward_pass.generative_adversarial_instance_noise(.2, 0., 1000))
train(model, None)
def test_gan_feature_matching():
model = dz.GAN(CifarDecoder(), ConvolutionalEncoder(), noise_dim=100, generator_loss=[dz.loss.feature_matching()])
train(model, None)
def test_gan_one_sided_labels():
model = dz.GAN(CifarDecoder(), ConvolutionalEncoder(), noise_dim=100, discriminator_loss=[dz.loss.one_sided_label_smoothing()])
train(model, None)
def test_gan():
model = dz.GAN(CifarDecoder(), ConvolutionalEncoder(), 100)
cbs = [tensorboard_generative_sample(dz.math.random_normal([5, 100]))]
train(model, cbs)