vq_autoencoder

VQAutoencoder(encoder: keras.Model, vq: VectorQuantizer, decoder: keras.Model, **kwargs={})

Convenience wrapper around (encoder -> VectorQuantizer -> decoder).

Supports extra reconstruction-side losses and metrics.
Can return discrete code indices from the VQ bottleneck.
Exposes VQ layer metrics alongside base model metrics.

Parameters

Parameters of VQAutoencoder
Name	Type	Default	Description
`encoder`	`keras.Model`	Required	Encoder model producing continuous latents.
`vq`	`VectorQuantizer`	Required	VectorQuantizer layer that discretizes latents.
`decoder`	`keras.Model`	Required	Decoder model mapping bottleneck outputs to reconstructions.

encoder = encoder

vq = vq

decoder = decoder

metrics

call(
    x: keras.KerasTensor,
    training: bool = False,
    return_indices: bool = False,
) -> keras.KerasTensor | tuple[keras.KerasTensor, keras.KerasTensor]

Run encoder -> VQ bottleneck -> decoder.

Parameters

Parameters of call
Name	Type	Default	Description
`x`	`keras.KerasTensor`	Required	Input batch.
`training`	`bool`	False	Whether to run in training mode (affects encoder/decoder/VQ).
`return_indices`	`bool`	False	If True, also return discrete code indices.

Returns

Returns of call
Type	Description
`keras.KerasTensor \| tuple[keras.KerasTensor, keras.KerasTensor]`	Reconstruction, optionally with indices.

compile(
    optimizer: keras.optimizers.Optimizer,
    loss: keras.losses.Loss | None = None,
    metrics: list | None = None,
    extra_losses: list | None = None,
    extra_metrics: list | None = None,
    **kwargs={},
)

Compile with optional extra losses/metrics.

Parameters

Parameters of compile
Name	Type	Default	Description
`optimizer`	`keras.optimizers.Optimizer`	Required	Keras optimizer
`loss`	`keras.losses.Loss \| None`	None	base reconstruction loss (e.g., keras.losses.MeanSquaredError())
`metrics`	`list \| None`	None	standard Keras metrics (Metric instances or callables)
`extra_losses`	`list \| None`	None	list[Callable(y_true, y_pred) -> scalar]
`extra_metrics`	`list \| None`	None	list of Metric OR Callable(y_true, y_pred) -> scalar

compute_loss(x=None, y=None, y_pred=None, sample_weight=None, allow_empty=False)

Compute total loss = recon + extra losses + layer-added losses.

get_config()

Return serialized config for saving/loading.

from_config(config, custom_objects=None)

classmethod

Recreate model from serialized config.

compute_metrics(x, y, y_pred, sample_weight=None)

Update compiled metrics plus extra metric trackers.