merlin.models.tf.TwoTowerBlock

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

Bases: merlin.models.tf.blocks.retrieval.base.DualEncoderBlock, merlin.models.tf.blocks.retrieval.base.RetrievalMixin

Builds the Two-tower architecture, as proposed in the following `paper https://doi.org/10.1145/3298689.3346996`_ [Xinyang19].

Parameters
  • schema (Schema) – The Schema with the input features

  • query_tower (Block) – The Block that combines user features

  • item_tower (Optional[Block], optional) – The optional Block that combines items features. If not provided, a copy of the query_tower is used.

  • query_tower_tag (Tag) – The tag to select query features, by default Tags.USER

  • item_tower_tag (Tag) – The tag to select item features, by default Tags.ITEM

  • embedding_options (EmbeddingOptions) – Options for the input embeddings. - embedding_dims: Optional[Dict[str, int]] - The dimension of the embedding table for each feature (key), by default None - embedding_dim_default: int - Default dimension of the embedding table, when the feature is not found in embedding_dims, by default 64 - infer_embedding_sizes : bool, Automatically defines the embedding dimension from the feature cardinality in the schema, by default False - infer_embedding_sizes_multiplier: int. Multiplier used by the heuristic to infer the embedding dimension from its cardinality. Generally reasonable values range between 2.0 and 10.0. By default 2.0.

  • post (Optional[Block], optional) – The optional Block to apply on both outputs of Two-tower model

Returns

The Two-tower block

Return type

ParallelBlock

Raises
  • ValueError – The schema is required by TwoTower

  • ValueError – The query_tower is required by TwoTower

__init__(schema: merlin.schema.schema.Schema, query_tower: merlin.models.tf.blocks.core.base.Block, item_tower: Optional[merlin.models.tf.blocks.core.base.Block] = None, query_tower_tag=<Tags.USER: 'user'>, item_tower_tag=<Tags.ITEM: 'item'>, embedding_options: merlin.models.tf.inputs.embedding.EmbeddingOptions = EmbeddingOptions(embedding_dims=None, embedding_dim_default=64, infer_embedding_sizes=False, infer_embedding_sizes_multiplier=2.0, infer_embeddings_ensure_dim_multiple_of_8=False, embeddings_initializers=None, embeddings_l2_reg=0.0, combiner='mean'), post: Optional[Union[merlin.models.tf.blocks.core.base.Block, str, Sequence[str]]] = None, **kwargs)[source]

Methods

__init__(schema, query_tower[, item_tower, …])

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)

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_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

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.

parallel_layers: Union[List[TabularBlock], Dict[str, TabularBlock]]