merlin.models.tf.ItemRetrievalTask

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

Bases: merlin.models.tf.prediction_tasks.classification.MultiClassClassificationTask

Prediction-task for item-retrieval.

Parameters

• schema (Schema) – The schema object including features to use and their properties.

• samplers (List[ItemSampler]) – List of samplers for negative sampling, by default [InBatchSampler()]

• target_name (Optional[str]) – If specified, name of the target tensor to retrieve from dataloader. Defaults to None.

• task_name (Optional[str]) – name of the task. Defaults to None.

• task_block (Block) – The Block that applies additional layers op to inputs. Defaults to None.

• post_logits (Optional[PredictionBlock]) – Optional extra pre-call block for post-processing the logits, by default None. You can for example use post_logits = mm.PopularitySamplingBlock(item_fequency) for populariy sampling correction.

• logits_temperature (float) – Parameter used to reduce the model overconfidence, so that logits / T. Defaults to 1.

• cache_query (bool) – Add query embeddings to the context block, by default False

• store_negative_ids (bool) – Returns negative items ids as part of the output, by default False

Returns

The item retrieval prediction task

Return type

PredictionTask

__init__(schema: merlin.schema.schema.Schema, samplers: Sequence[merlin.models.tf.blocks.sampling.base.ItemSampler] = (), target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[keras.engine.base_layer.Layer] = None, post_logits: Optional[merlin.models.tf.core.base.Block] = None, logits_temperature: float = 1.0, cache_query: bool = False, store_negative_ids: bool = False, **kwargs)

Methods

__init__(schema[, samplers, target_name, ...])
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.
build(input_shape[, features_shape])
build_from_config(config)
build_task(input_shape, schema, body, **kwargs)
call(inputs[, training, eval_sampling])
child_name(name)
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
create_default_metrics()
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)
from_schema(schema[, feature_name, ...])	Create from Schema.
get_build_config()
get_config()	Return a Python dict containing the configuration of the model.
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_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_weights()	Returns the current weights of the layer, as NumPy arrays.
pre_call(inputs, **kwargs)	Apply PredictionTask to inputs to get predictions scores
pre_loss(outputs, **kwargs)	Apply call_outputs method of pre block to transform predictions and targets before computing loss and metrics.
set_retrieval_cache_query(value)
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

Attributes

DEFAULT_LOSS
DEFAULT_METRICS
activity_regularizer	Optional regularizer function for the output of this layer.
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.
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.
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.
pre_eval_topk
retrieval_scorer
stateful
submodules	Sequence of all sub-modules.
supports_masking	Whether this layer supports computing a mask using compute_mask.
task_name
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.

DEFAULT_LOSS = 'categorical_crossentropy'

DEFAULT_METRICS: Union[Sequence[Union[keras.metrics.base_metric.Metric, Type[keras.metrics.base_metric.Metric]]], keras.metrics.base_metric.Metric, Type[keras.metrics.base_metric.Metric]] = [TopKMetricsAggregator()]

call(inputs, training=False, eval_sampling=False, **kwargs)

property retrieval_scorer

set_retrieval_cache_query(value: bool)

get_config()

Return a Python dict containing the configuration of the model.

classmethod from_config(config)