merlin.models.tf.SequenceEmbeddingFeatures

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

Bases: merlin.models.tf.inputs.embedding.EmbeddingFeatures

Input block for embedding-lookups for categorical features. This module produces 3-D tensors, this is useful for sequential models like transformers.

Parameters

• feature_config (Dict[str, FeatureConfig]) – This specifies what TableConfig to use for each feature. For shared embeddings, the same TableConfig can be used for multiple features.

• mask_zero (bool) – Whether or not the input value 0 is a special “padding” value that should be masked out.

• padding_idx (int) – The symbol to use for padding.

• pre (Union[str, TabularTransformation, List[str], List[TabularTransformation]], optional) – Transformations to apply on the inputs when the module is called (so before call).

• post (Union[str, TabularTransformation, List[str], List[TabularTransformation]], optional) – Transformations to apply on the inputs after the module is called (so after call).

• aggregation (Union[str, TabularAggregation], optional) –

  Aggregation to apply after processing the call-method to output a single Tensor.

  Next to providing a class that extends TabularAggregation, it’s also possible to provide the name that the class is registered in the tabular_aggregation_registry. Out of the box this contains: “concat”, “stack”, “element-wise-sum” & “element-wise-sum-item-multi”.

• schema (Optional[DatasetSchema]) – DatasetSchema containing the columns used in this block.

• name (Optional[str]) – Name of the layer.

• add_default_pre (bool, default True) – Whether or not to add a default preprocessing block.

__init__(feature_config: Dict[str, tensorflow.python.tpu.tpu_embedding_v2_utils.FeatureConfig], mask_zero: bool = True, padding_idx: int = 0, pre: Optional[Union[merlin.models.tf.core.base.Block, str, Sequence[str]]] = None, post: Optional[Union[merlin.models.tf.core.base.Block, str, Sequence[str]]] = None, aggregation: Optional[Union[str, merlin.models.tf.core.tabular.TabularAggregation]] = None, schema: Optional[merlin.schema.schema.Schema] = None, name: Optional[str] = None, add_default_pre=True, **kwargs)

Initializes the block.

Methods

__init__(feature_config[, mask_zero, ...])	Initializes the block.
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.
apply_to_all(inputs[, columns_to_filter])
as_tabular([name])
build(input_shapes)
build_from_config(config)
calculate_batch_size_from_input_shapes(...)
call(inputs, **kwargs)
call_outputs(outputs[, training])
check_schema([schema])
compute_call_output_shape(input_shapes)	Computes the output shapes given the input shapes.
compute_mask(inputs[, mask])	Computes a mask tensor from the inputs.
compute_output_shape(input_shapes)
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
connect(*block[, block_name, context])	Connect the block to other blocks sequentially.
connect_branch(*branches[, add_rest, post, ...])	Connect the block to one or multiple branches.
connect_debug_block([append])	Connect the block to a debug block.
connect_with_residual(block[, activation])	Connect the block to other blocks sequentially with a residual connection.
connect_with_shortcut(block[, ...])	Connect the block to other blocks sequentially with a shortcut connection.
copy()
count_params()	Count the total number of scalars composing the weights.
embedding_table_dataset(table_name[, ...])	Creates a Dataset for the embedding table
embedding_table_df(table_name[, ...])	Retrieves a dataframe with the embedding table
export_embedding_table(table_name, export_path)	Exports the embedding table to parquet file
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)
from_features(features[, pre, post, ...])	Initializes a TabularLayer instance where the contents of features will be filtered out
from_layer(layer)
from_schema(schema[, embedding_options, ...])	Instantiates embedding features from the schema
get_build_config()
get_config()	Gets the configuration dictionary for this block.
get_embedding_table(table_name[, ...])
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_item_ids_from_inputs(inputs)
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_padding_mask_from_item_id(inputs[, ...])
get_weights()	Returns the current weights of the layer, as NumPy arrays.
lookup_feature(name, val, **kwargs)	Looks up the embedding for a specific feature from the pre-trained embedding tables.
parse(*block)
parse_block(input)
post_call(inputs[, transformations, ...])	Method that's typically called after the forward method for post-processing.
pre_call(inputs[, transformations])	Method that's typically called before the forward method for pre-processing.
prepare([block, post, aggregation])	Transform the inputs of this block.
register_features(feature_shapes)
repeat([num])	Repeat the block num times.
repeat_in_parallel([num, prefix, names, ...])	Repeat the block num times in parallel.
repr_add()
repr_extra()
repr_ignore()
select_by_name(name)
select_by_tag(tags)
set_aggregation(value)	param value
set_post(value)
set_pre(value)
set_schema([schema])
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
super()
table_config(feature_name)
with_name_scope(method)	Decorator to automatically enter the module name scope.

Attributes

REQUIRES_SCHEMA
activity_regularizer	Optional regularizer function for the output of this layer.
aggregation	rtype: TabularAggregation, optional
compute_dtype	The dtype of the layer's computations.
context
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.
has_schema
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.
is_input
is_tabular
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.
post	rtype: SequentialTabularTransformations, optional
pre	rtype: SequentialTabularTransformations, optional
registry
schema
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.

lookup_feature(name, val, **kwargs)

Looks up the embedding for a specific feature from the pre-trained embedding tables.

Parameters

• name (str) – The name of the feature to lookup.

• val (tf.Tensor) – The tensor of feature values to look up in the embedding tables.

Returns

The corresponding embedding tensor.

Return type

tf.Tensor

compute_call_output_shape(input_shapes)

Computes the output shapes given the input shapes.

Parameters

input_shapes (dict) – Dictionary mapping input names to their shapes.

Returns

Dictionary mapping output names to their shapes.

Return type

dict

compute_mask(inputs, mask=None)

Computes a mask tensor from the inputs.

Parameters

• inputs (dict) – Dictionary mapping input names to their values.

• mask (tf.Tensor, optional) – An optional mask to apply to the inputs.

Returns

A mask tensor, or None if mask_zero is False.

Return type

dict or None

get_config()

Gets the configuration dictionary for this block.

Returns

The configuration dictionary.

Return type

dict