merlin.systems.dag.ops.tensorflow.PredictTensorflow

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

Bases: InferenceOperator

TensorFlow Model Prediction Operator.

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

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
save_artifacts([artifact_path])	Save artifacts required to be reload operator state from disk
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: ColumnSelector, transformable: Transformable) → Transformable

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)

compute_input_schema(root_schema: Schema, parents_schema: Schema, deps_schema: Schema, selector: ColumnSelector) → Schema

Use the input schema supplied during object creation.

compute_output_schema(input_schema: Schema, col_selector: ColumnSelector, prev_output_schema: Optional[Schema] = None) → Schema

Use the output schema supplied during object creation.