DecoderV1

class tfts.models.wavenet.DecoderV1(*args, **kwargs)

Bases: Layer

Decoder block for WaveNet V1.

Initializes the decoder block.

Parameters:

• filters – Number of filters for convolutional layers.

• dilation_rates – Dilation rates for convolutions.

• dense_hidden_size – Size of the dense hidden layer.

• predict_sequence_length – Length of the predicted sequence.

Inherited-members:

Methods

add_loss(loss)	Can be called inside of the call() method to add a scalar loss.
add_metric(*args, **kwargs)
add_variable(shape, initializer[, dtype, ...])	Add a weight variable to the layer.
add_weight([shape, initializer, dtype, ...])	Add a weight variable to the layer.
build(input_shape, **kwargs)
build_from_config(config)	Builds the layer's states with the supplied config dict.
call(decoder_features, decoder_init_input, ...)	Forward pass for the decoder block.
compute_mask(inputs, previous_mask)
compute_output_shape(input_shape)
compute_output_spec(*args, **kwargs)
count_params()	Count the total number of scalars composing the weights.
from_config(config)	Creates an operation from its config.
get_build_config()	Returns a dictionary with the layer's input shape.
get_config()	Returns the config of the object.
get_weights()	Return the values of layer.weights as a list of NumPy arrays.
load_own_variables(store)	Loads the state of the layer.
quantize(mode[, type_check])
quantized_build(input_shape, mode)
quantized_call(*args, **kwargs)
rematerialized_call(layer_call, *args, **kwargs)	Enable rematerialization dynamically for layer's call method.
save_own_variables(store)	Saves the state of the layer.
set_weights(weights)	Sets the values of layer.weights from a list of NumPy arrays.
stateless_call(trainable_variables, ...[, ...])	Call the layer without any side effects.
symbolic_call(*args, **kwargs)

Attributes

compute_dtype	The dtype of the computations performed by the layer.
dtype	Alias of layer.variable_dtype.
dtype_policy
input	Retrieves the input tensor(s) of a symbolic operation.
input_dtype	The dtype layer inputs should be converted to.
input_spec
losses	List of scalar losses from add_loss, regularizers and sublayers.
metrics	List of all metrics.
metrics_variables	List of all metric variables.
non_trainable_variables	List of all non-trainable layer state.
non_trainable_weights	List of all non-trainable weight variables of the layer.
output	Retrieves the output tensor(s) of a layer.
path	The path of the layer.
quantization_mode	The quantization mode of this layer, None if not quantized.
supports_masking	Whether this layer supports computing a mask using compute_mask.
trainable	Settable boolean, whether this layer should be trainable or not.
trainable_variables	List of all trainable layer state.
trainable_weights	List of all trainable weight variables of the layer.
variable_dtype	The dtype of the state (weights) of the layer.
variables	List of all layer state, including random seeds.
weights	List of all weight variables of the layer.

call(decoder_features, decoder_init_input, encoder_outputs, teacher: Tensor | None = None, scheduled_sampling: float = 0.0, training: bool | None = None, **kwargs: Dict)

Forward pass for the decoder block.

Parameters:

• decoder_features – Tensor containing decoder features.

• decoder_init_input – Initial input for the decoder.

• encoder_outputs – List of encoder outputs.

• teacher – Optional tensor for teacher forcing.

• scheduled_sampling – Probability of using teacher forcing.

• training – Whether the model is in training mode.

Returns:

Decoder output tensor.

get_config()

Returns the config of the object.

An object config is a Python dictionary (serializable) containing the information needed to re-instantiate it.