fusionlab.nn.components.DynamicTimeWindow

class fusionlab.nn.components.DynamicTimeWindow

Bases: Layer, NNLearner

DynamicTimeWindow layer that slices the last max_window_size steps from the input sequence.

This helps in focusing on the most recent time steps if the sequence is longer than max_window_size.

\[\mathbf{Z} = \mathbf{X}[:, -W:, :]\]

where W = max_window_size.

Parameters:

max_window_size (int) – Number of time steps to keep from the end of the sequence.

Notes

This can be used for models that only need the last few time steps instead of the entire sequence.

call(`inputs`, training=False)

Slice the last max_window_size steps.

get_config()

Returns configuration dictionary.

from_config(`config`)

Recreates the layer from config.

Examples

>>> from fusionlab.nn.components import DynamicTimeWindow
>>> import tensorflow as tf
>>> x = tf.random.normal((32, 50, 64))
>>> # Keep last 10 time steps
>>> dtw = DynamicTimeWindow(max_window_size=10)
>>> y = dtw(x)
>>> y.shape
TensorShape([32, 10, 64])

See also

MultiResolutionAttentionFusion

Another layer that can be used after slicing to fuse temporal features.

References

[1]

Lim, B., & Zohren, S. (2021). “Time-series forecasting with deep learning: a survey.” Philosophical Transactions of the Royal Society A, 379(2194), 20200209.

__init__(max_window_size)

Initialize the DynamicTimeWindow layer.

Parameters:

max_window_size (int) – Number of steps to slice from the end of the sequence.

Methods

__init__(max_window_size)	Initialize the DynamicTimeWindow layer.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
build(input_shape)	Creates the variables of the layer (for subclass implementers).
build_from_config(config)	Builds the layer's states with the supplied config dict.
call(inputs[, training])	Forward pass that slices the last max_window_size steps.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a new DynamicTimeWindow layer from config.
get_build_config()	Returns a dictionary with the layer's input shape.
get_config()	Returns configuration dictionary.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_params([deep])	Get the parameters for this learner.
get_weights()	Returns the current weights of the layer, as NumPy arrays.
help(**kwargs)
load(file_path[, format])	Load the learner's state from a specified file in the desired format.
load_own_variables(store)	Loads the state of the layer.
save([file_path, format, overwrite, ...])	Save the learner's state to a specified file in the desired format.
save_own_variables(store)	Saves the state of the layer.
set_params(**params)	Set the parameters of this learner.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
summary()	Provide a summary of the learner's parameters.
with_name_scope(method)	Decorator to automatically enter the module name scope.

Attributes

activity_regularizer	Optional regularizer function for the output of this layer.
compute_dtype	The dtype of the layer's computations.
dtype	The dtype of the layer weights.
dtype_policy	The dtype policy associated with this layer.
dynamic	Whether the layer is dynamic (eager-only); set in the constructor.
inbound_nodes	Return Functional API nodes upstream of this layer.
input	Retrieves the input tensor(s) of a layer.
input_mask	Retrieves the input mask tensor(s) of a layer.
input_shape	Retrieves the input shape(s) of a layer.
input_spec	InputSpec instance(s) describing the input format for this layer.
losses	List of losses added using the add_loss() API.
metrics	List of metrics attached to the layer.
my_params
name	Name of the layer (string), set in the constructor.
name_scope	Returns a tf.name_scope instance for this class.
non_trainable_variables	Sequence of non-trainable variables owned by this module and its submodules.
non_trainable_weights	List of all non-trainable weights tracked by this layer.
outbound_nodes	Return Functional API nodes downstream of this layer.
output	Retrieves the output tensor(s) of a layer.
output_mask	Retrieves the output mask tensor(s) of a layer.
output_shape	Retrieves the output shape(s) of a layer.
stateful
submodules	Sequence of all sub-modules.
supports_masking	Whether this layer supports computing a mask using compute_mask.
trainable
trainable_variables	Sequence of trainable variables owned by this module and its submodules.
trainable_weights	List of all trainable weights tracked by this layer.
updates
variable_dtype	Alias of Layer.dtype, the dtype of the weights.
variables	Returns the list of all layer variables/weights.
weights	Returns the list of all layer variables/weights.

__init__(max_window_size)

Initialize the DynamicTimeWindow layer.

Parameters:

max_window_size (int) – Number of steps to slice from the end of the sequence.

call(inputs, training=False)

Forward pass that slices the last max_window_size steps.

Parameters:

• inputs (tf.Tensor) – Tensor of shape \((B, T, D)\).

• training (bool, optional) – Unused. Defaults to False.

Returns:

A sliced tensor of shape \((B, W, D)\) where W = max_window_size.

Return type:

tf.Tensor

get_config()

Returns configuration dictionary.

Returns:

Contains ‘max_window_size’.

Return type:

dict

classmethod from_config(config)

Creates a new DynamicTimeWindow layer from config.

Parameters:

config (dict) – Must include ‘max_window_size’.

Returns:

A new instance of this layer.

Return type:

DynamicTimeWindow

help(**kwargs)

my_params = DynamicTimeWindow(max_window_size)