def test_ae_with_vae_forward_pass():
with pytest.raises(DazeModelTypeError):
model = dz.AutoEncoder(
ConvolutionalEncoder(3),
CifarDecoder(),
forward_pass_func=dz.forward_pass.probabilistic_encode_decode(),
loss_funcs=[dz.loss.latent_l1()])
def test_get_batch_encodings_np():
x, _ = dz.data.cifar10.load(70, "f32")
x /= 255
model = dz.AutoEncoder(ConvolutionalEncoder(latent_dim=2), CifarDecoder())
encodings = model.get_batch_encodings(x)
assert isinstance(encodings, tf.Tensor)
assert encodings.numpy().shape[0] == 70
assert encodings.numpy().shape[1] == 2
def test_ae_with_gan_loss_func():
with pytest.raises(DazeModelTypeError):
model = dz.AutoEncoder(ConvolutionalEncoder(3),
CifarDecoder(),
loss_funcs=[dz.loss.feature_matching()])
def test_ae_with_vae_loss_func():
with pytest.raises(DazeModelTypeError):
model = dz.AutoEncoder(ConvolutionalEncoder(3),
CifarDecoder(),
loss_funcs=[dz.loss.kl()])
def test_ae_with_gan_forward_pass():
with pytest.raises(DazeModelTypeError):
model = dz.AutoEncoder(
ConvolutionalEncoder(3),
CifarDecoder(),
forward_pass_func=dz.forward_pass.generative_adversarial())
def test_get_batch_encodings_unknown():
with pytest.raises(ValueError):
model = dz.AutoEncoder(ConvolutionalEncoder(latent_dim=2),
CifarDecoder())
encodings = model.get_batch_encodings([1.0, 2.0, 3.0])
def test_default():
model = dz.AutoEncoder(ConvolutionalEncoder(3), CifarDecoder())
cbs = make_callbacks(model)
train(model, cbs)