merlin.models.tf.DualEncoderBlock

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

Bases: merlin.models.tf.core.combinators.ParallelBlock

__init__(query_block: merlin.models.tf.core.base.Block, item_block: merlin.models.tf.core.base.Block, 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, l2_normalization: bool = False, **kwargs)[source]

Prepare the Query and Item towers of a Retrieval block :param query_block: The Block instance that combines user features :type query_block: Block :param item_block: Optional Block instance that combines items features. :type item_block: Block :param pre: Optional Block instance to apply before the call method of the Two-Tower block :type pre: Optional[BlockType], optional :param post: Optional Block instance to apply on both outputs of Two-tower model :type post: Optional[BlockType], optional :param aggregation: The Aggregation operation to apply after processing the call method

to output a single Tensor.

Parameters
  • schema (Optional[Schema], optional) – The Schema object with the input features.

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

  • strict (bool, optional) – If enabled, check that the input of the ParallelBlock instance is a dictionary.

  • l2_normalization (bool) – Apply L2 normalization to the user and item representations before computing dot interactions. Defaults to False.

Methods

__init__(query_block, item_block[, pre, …])

Prepare the Query and Item towers of a Retrieval block :param query_block: The Block instance that combines user features :type query_block: Block :param item_block: Optional Block instance that combines items features. :type item_block: Block :param pre: Optional Block instance to apply before the call method of the Two-Tower block :type pre: Optional[BlockType], optional :param post: Optional Block instance to apply on both outputs of Two-tower model :type post: Optional[BlockType], optional :param aggregation: The Aggregation operation to apply after processing the call method to output a single Tensor. :type aggregation: Optional[TabularAggregationType], optional :param schema: The Schema object with the input features. :type schema: Optional[Schema], optional :param name: Name of the layer. :type name: Optional[str], optional :param strict: If enabled, check that the input of the ParallelBlock instance is a dictionary. :type strict: bool, optional :param l2_normalization: Apply L2 normalization to the user and item representations before computing dot interactions. Defaults to False. :type l2_normalization: bool.

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.

item_block()

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.

query_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.

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.

query_block()merlin.models.tf.blocks.retrieval.base.TowerBlock[source]
item_block()merlin.models.tf.blocks.retrieval.base.TowerBlock[source]
classmethod from_config(config, custom_objects=None)[source]