merlin.models.tf.ItemRetrievalTask

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

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

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)[source]

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_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)	Computes the output shape of the layer.
`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.
`finalize_state`()	Finalizes the layers state after updating layer weights.
`from_config`(config)
`from_schema`(schema[, feature_name, …])	Create from Schema.
`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_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)[source]

property retrieval_scorer

set_retrieval_cache_query(value: bool)[source]