fusionlab.nn.components.MultiResolutionAttentionFusion

class fusionlab.nn.components.MultiResolutionAttentionFusion

Bases: Layer, NNLearner

MultiResolutionAttentionFusion layer applying multi-head attention fusion over features [1].

This layer merges or fuses features at different resolutions or sources via multi-head attention. The input is projected to shape (B, T, D), and the output shares the same shape.

\[\mathbf{Z} = \text{MHA}(\mathbf{X}, \mathbf{X})\]

Parameters:

• units (int) – Dimension of the key, query, and value projections.

• num_heads (int) – Number of attention heads.

Notes

Typically used in multi-resolution contexts where time steps or multiple feature sets are merged.

call(`inputs`, training=False)

Forward pass of the multi-head attention layer.

get_config()

Returns config for serialization.

from_config(`config`)

Reconstructs the layer from a config.

Examples

>>> from fusionlab.nn.components import MultiResolutionAttentionFusion
>>> import tensorflow as tf
>>> x = tf.random.normal((32, 10, 64))
>>> # Instantiate multi-resolution attention
>>> mraf = MultiResolutionAttentionFusion(
...     units=64,
...     num_heads=4
... )
>>> # Forward pass => (32, 10, 64)
>>> y = mraf(x)

See also

HierarchicalAttention

Combines short and long-term sequences with attention.

ExplainableAttention

Another attention layer returning attention scores.

References

[1]

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). “Attention is all you need.” In Advances in Neural Information Processing Systems (pp. 5998-6008).

__init__(units, num_heads)

Initialize the MultiResolutionAttentionFusion layer.

Parameters:

• units (int) – Dimensionality for the attention projections.

• num_heads (int) – Number of heads for multi-head attention.

Methods

__init__(units, num_heads)	Initialize the MultiResolutionAttentionFusion 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 applying multi-head attention to fuse features.
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)	Instantiate a new MultiResolutionAttentionFusion layer from config.
get_build_config()	Returns a dictionary with the layer's input shape.
get_config()	Returns configuration dictionary with 'units' and 'num_heads'.
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__(units, num_heads)

Initialize the MultiResolutionAttentionFusion layer.

Parameters:

• units (int) – Dimensionality for the attention projections.

• num_heads (int) – Number of heads for multi-head attention.

call(inputs, training=False)

Forward pass applying multi-head attention to fuse features.

Parameters:

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

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

Returns:

Tensor of shape (B, T, D), representing fused features.

Return type:

tf.Tensor

get_config()

Returns configuration dictionary with ‘units’ and ‘num_heads’.

Returns:

Configuration for serialization.

Return type:

dict

classmethod from_config(config)

Instantiate a new MultiResolutionAttentionFusion layer from config.

Parameters:

config (dict) – Configuration dictionary.

Returns:

A new instance of this layer.

Return type:

MultiResolutionAttentionFusion

help(**kwargs)

my_params = MultiResolutionAttentionFusion(units, num_heads)