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.core.base.Block, item_tower: Optional[merlin.models.tf.core.base.Block] = None, query_tower_tag=Tags.USER, item_tower_tag=Tags.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.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)
`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`	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`	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.

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