merlin.models.tf.AverageEmbeddingsByWeightFeature

class merlin.models.tf.AverageEmbeddingsByWeightFeature(*args, **kwargs)

Bases: keras.engine.base_layer.Layer

__init__(weight_feature_name: str, axis=1, **kwargs)

Computes the weighted average of a Tensor based on one of the input features. Typically used as a combiner for EmbeddingTable for aggregating sequential embedding features

Parameters

• weight_feature_name (str) – Name of the feature to be used as weight for average

• axis (int, optional) – Axis for reduction, by default 1 (assuming the 2nd dim is the sequence length)

Methods

__init__(weight_feature_name[, axis])	Computes the weighted average of a Tensor based on one of the input features.
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)
call(inputs, features)
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)
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 layer from its config.
from_schema_convention(schema[, ...])	Infers the weight features corresponding to sequential embedding features based on the feature name suffix.
get_build_config()
get_config()
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_weights()	Returns the current weights of the layer, as NumPy arrays.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
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 added using the add_metric() API.
name	Name of the layer (string), set in the constructor.
name_scope	Returns a tf.name_scope instance for this class.
non_trainable_variables
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
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.

call(inputs, features)

compute_output_shape(input_shape)

static from_schema_convention(schema: merlin.schema.schema.Schema, weight_features_name_suffix: str = '_weight')

Infers the weight features corresponding to sequential embedding features based on the feature name suffix. For example, if a sequential categorical feature is called item_id_seq, if there is another feature in the schema called item_id_seq_weight, then it will be used for weighted average. If a weight feature cannot be found for a given seq cat. feature then standard mean is used as combiner

Parameters

• schema (Schema) – The feature schema

• weight_features_name_suffix (str) – Suffix to look for a corresponding weight feature

Returns

A dict where the key is the sequential categorical feature name and the value is an instance of WeightedAverageByFeature with the corresponding weight feature name

Return type

Dict[str, WeightedAverageByFeature]

get_config()