merlin.models.tf.ParallelBlock

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

Bases: merlin.models.tf.core.tabular.TabularBlock

Merge multiple layers or TabularModule’s into a single output of TabularData.

Parameters

• inputs (Union[tf.keras.layers.Layer, Dict[str, tf.keras.layers.Layer]]) – keras layers to merge into, this can also be one or multiple layers keyed by the name the module should have.

• 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.

• use_layer_name (use the original name of layers provided in inputs as key-index of the) – parallel branches.

• strict – If true, inputs must be a dictionary. Otherwise, an error will be raised.

• automatic_pruning – If true, branches with no output will automatically be pruned.

• **kwargs – Extra arguments to pass to TabularBlock.

__init__(*inputs: Union[keras.engine.base_layer.Layer, Dict[str, keras.engine.base_layer.Layer]], 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, strict: bool = False, automatic_pruning: bool = True, use_layer_name: bool = True, **kwargs)

Methods

__init__(*inputs[, pre, post, aggregation, …])
add_branch(name, 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])
apply_to_branch(branch_name, *block)
as_tabular([name])
build(input_shape)
build_from_config(config)
calculate_batch_size_from_input_shapes(…)
call(inputs, **kwargs)	The call method for ParallelBlock
call_outputs(outputs[, training])
check_schema([schema])
compute_call_output_shape(input_shape)
compute_mask(inputs[, mask])	Computes an output mask tensor.
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.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config[, custom_objects])
from_features(features[, pre, post, …])	Initializes a TabularLayer instance where the contents of features will be filtered out
from_layer(layer)
from_schema(schema[, tags, allow_none])	Instantiate a TabularLayer instance from a DatasetSchema.
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_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.
parse(*block)
parse_block(input)
parse_config(config[, custom_objects])
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 a parallel block by name
select_by_names(names)	Select a list of parallel blocks by names
select_by_tag(tags)	Select layers of parallel blocks by 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()
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	returns: :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.
first
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
layers
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.
parallel_dict
parallel_values
post	returns: :rtype: SequentialTabularTransformations, optional
pre	returns: :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.

property schema

property parallel_values

property parallel_dict

property layers

select_by_name(name: str) → Optional[merlin.models.tf.core.base.Block]

Select a parallel block by name

Returns

The block corresponding to the name

Return type

Block

select_by_names(names: List[str]) → Optional[List[merlin.models.tf.core.base.Block]]

Select a list of parallel blocks by names

Returns

The blocks corresponding to the names

Return type

List[Block]

select_by_tag(tags: Union[str, merlin.schema.tags.Tags, List[Union[str, merlin.schema.tags.Tags]]]) → Optional[merlin.models.tf.core.combinators.ParallelBlock]

Select layers of parallel blocks by tags.

This method will return a ParallelBlock instance with all the branches that have at least one feature that matches any of the tags provided.

For example, this method can be useful when a ParallelBlock has both item and user features in a two-tower model or DLRM, and we want to select only the item or user features.

>>> all_inputs = InputBlockV2(schema)  # InputBlock is also a ParallelBlock
>>> item_inputs = all_inputs.select_by_tag(Tags.ITEM)
['continuous', 'embeddings']
>>> item_inputs.schema["continuous"].column_names
['item_recency']
>>> item_inputs.schema["embeddings"].column_names
['item_id', 'item_category', 'item_genres']

Parameters

tags (str or Tags or List[Union[str, Tags]]) – List of tags that describe which blocks to match

Returns

Return type

ParallelBlock

property first

add_branch(name: str, block: merlin.models.tf.core.base.Block) → merlin.models.tf.core.combinators.ParallelBlock

apply_to_branch(branch_name: str, *block: merlin.models.tf.core.base.Block)

call(inputs, **kwargs)

The call method for ParallelBlock

Parameters

inputs (TabularData) – The inputs for the Parallel Block

Returns

Outputs of the ParallelBlock

Return type

TabularData

compute_call_output_shape(input_shape)

build(input_shape)

get_config()

classmethod parse_config(config, custom_objects=None)

classmethod from_config(config, custom_objects=None)