merlin.systems.dag.ops.tensorflow.PredictTensorflow

class merlin.systems.dag.ops.tensorflow.PredictTensorflow(model_or_path, custom_objects: Optional[dict] = None, backend='tensorflow')[source]

Bases: merlin.systems.dag.ops.operator.InferenceOperator

TensorFlow Model Prediction Operator.

__init__(model_or_path, custom_objects: Optional[dict] = None, backend='tensorflow')[source]

Instantiate a PredictTensorflow inference operator.

Parameters

model_or_path (Tensorflow model or string) – This can be a tensorflow model or a path to a tensorflow model.
custom_objects (dict, optional) – Any custom objects that need to be loaded with the model, by default None.

Methods

`__init__`(model_or_path[, custom_objects, …])	Instantiate a PredictTensorflow inference operator.
`column_mapping`(col_selector)	Compute which output columns depend on which input columns
`compute_column_schema`(col_name, input_schema)
`compute_input_schema`(root_schema, …)	Use the input schema supplied during object creation.
`compute_output_schema`(input_schema, col_selector)	Use the output schema supplied during object creation.
`compute_selector`(input_schema, selector[, …])	Provides a hook method for sub-classes to override to implement custom column selection logic.
`create_node`(selector)	_summary_
`export`(path, input_schema, output_schema[, …])	Export the class object as a config and all related files to the user defined path.
`from_model_registry`(registry, **kwargs)	Loads the InferenceOperator from the provided ModelRegistry.
`from_path`(path, **kwargs)
`load_artifacts`(artifact_path)	Hook method that provides a way to load saved artifacts for the operator
`output_column_names`(col_selector)	Given a set of columns names returns the names of the transformed columns this operator will produce
`transform`(col_selector, transformable)	Run model inference.
`validate_schemas`(parents_schema, …[, …])	Provides a hook method that sub-classes can override to implement schema validation logic.

Attributes

`dependencies`	Defines an optional list of column dependencies for this operator.
`dynamic_dtypes`
`export_name`	Provides a clear common english identifier for this operator.
`exportable_backends`
`is_subgraph`
`label`
`output_dtype`
`output_properties`
`output_tags`
`scalar_shape`
`supported_formats`
`supports`	Returns what kind of data representation this operator supports

transform(col_selector: merlin.dag.selector.ColumnSelector, transformable: merlin.core.protocols.Transformable) → merlin.core.protocols.Transformable[source]

Run model inference. Returning predictions.

Parameters

col_selector (ColumnSelector) – Unused ColumunSelector input
transformable (Transformable) – Input features to model

Returns

Model Predictions

Return type

Transformable

property export_name

Provides a clear common english identifier for this operator.

Returns: Name of the current class as spelled in module.
Return type: String

classmethod from_path(path, **kwargs)[source]

compute_input_schema(root_schema: merlin.schema.schema.Schema, parents_schema: merlin.schema.schema.Schema, deps_schema: merlin.schema.schema.Schema, selector: merlin.dag.selector.ColumnSelector) → merlin.schema.schema.Schema [source]: Use the input schema supplied during object creation.

compute_output_schema(input_schema: merlin.schema.schema.Schema, col_selector: merlin.dag.selector.ColumnSelector, prev_output_schema: Optional[merlin.schema.schema.Schema] = None) → merlin.schema.schema.Schema [source]: Use the output schema supplied during object creation.