merlin.models.tf.SequenceEmbeddingFeatures#

class merlin.models.tf.SequenceEmbeddingFeatures(*args, **kwargs)[source]#

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)[source]#

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)[source]#

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)[source]#

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)[source]#

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()[source]#

Gets the configuration dictionary for this block.

Returns

The configuration dictionary.

Return type

dict