def test_contractive():
if dz.tracing.TRACE_GRAPHS:
with pytest.raises(ValueError):
model = dz.recipes.ContractiveAutoEncoder(
ConvolutionalEncoder(), CifarDecoder(), gamma=0.1
)
else:
model = dz.recipes.ContractiveAutoEncoder(
ConvolutionalEncoder(), CifarDecoder(), gamma=0.1
)
cbs = make_callbacks(model)
train(model, cbs)
def test_klsparse():
model = dz.recipes.KlSparseAutoEncoder(
ConvolutionalEncoder(), CifarDecoder(), rho=0.01, beta=0.1
)
cbs = make_callbacks(model)
train(model, cbs)
def test_denoising():
model = dz.recipes.DenoisingAutoEncoder(ConvolutionalEncoder(), CifarDecoder(), gamma=0.1)
cbs = make_callbacks(model)
train(model, cbs)
def test_vae():
model = dz.recipes.VariationalAutoEncoder(ConvolutionalEncoder(), CifarDecoder())
cbs = make_callbacks(model)
train(model, cbs)
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)
def test_default():
model = dz.AutoEncoder(ConvolutionalEncoder(3), CifarDecoder())
cbs = make_callbacks(model)
train(model, cbs)
