Class TensorFlowModel (0.3.0)

TensorFlowModel(
    session: typing.Optional[bigframes.session.Session] = None,
    model_path: typing.Optional[str] = None,
)

Imported TensorFlow model.

Parameters
Name	Description
`session`	`BigQuery Session` BQ session to create the model
`model_path`	`str` GCS path that holds the model files.

Methods

__repr__()

Print the estimator's constructor with all non-default parameter values

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

Parameter
Name	Description
`deep`	`bool, default True` Default `True`. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
Type	Description
`Dictionary`	A dictionary of parameter names mapped to their values.

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

Parameter
Name	Description
`X`	`bigframes.dataframe.DataFrame` Input DataFrame, schema is defined by the model.

Returns
Type	Description
`bigframes.dataframe.DataFrame`	Output DataFrame, schema is defined by the model.

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Parameter
Name	Description
`vertex_ai_model_id`	`Optional[str], default None` optional string id as model id in Vertex. If not set, will by default to 'bigframes_{bq_model_id}'. Vertex Ai model id will be truncated to 63 characters due to its limitation.

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.imported.TensorFlowModel

Save the model to BigQuery.

Parameters
Name	Description
`model_name`	`str` the name of the model.
`replace`	`bool, default False` whether to replace if the model already exists. Default to False.

Returns
Type	Description
`TensorFlowModel`	saved model.