DecoderV1#

class tfts.models.seq2seq.DecoderV1(*args, **kwargs)[source]#

Bases: Layer

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, ...)

Seq2seq decoder with attention mechanism.

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, init_state, teacher=None, scheduled_sampling=0, training=None, **kwargs)[source]#

Seq2seq decoder with attention mechanism.

Parameters:

  • decoder_features – Decoder input features.

  • decoder_init_input – Initial input for the decoder.

  • init_state – Initial state from the encoder.

  • teacher – Ground truth for teacher forcing.

  • scheduled_sampling – Probability of using teacher forcing.

  • training – Whether the model is in training mode.

Returns:

Decoder output.

get_config()[source]#

Returns the config of the object.

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