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
- 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: '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.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]]