kedro.extras.datasets.tensorflow.TensorFlowModelDataset¶

class kedro.extras.datasets.tensorflow.TensorFlowModelDataset(filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None)[source]¶

Bases: kedro.io.core.AbstractVersionedDataSet

TensorflowModelDataset loads and saves TensorFlow models. The underlying functionality is supported by, and passes input arguments through to, TensorFlow 2.X load_model and save_model methods.

Example:

from kedro.extras.datasets.tensorflow import TensorFlowModelDataset
import tensorflow as tf
import numpy as np

data_set = TensorFlowModelDataset("saved_model_path")
model = tf.keras.Model()
predictions = model.predict([...])

data_set.save(model)
loaded_model = data_set.load()
new_predictions = loaded_model.predict([...])
np.testing.assert_allclose(predictions, new_predictions, rtol=1e-6, atol=1e-6)

Attributes

`TensorFlowModelDataset.DEFAULT_LOAD_ARGS`
`TensorFlowModelDataset.DEFAULT_SAVE_ARGS`

Methods

`TensorFlowModelDataset.__init__`(filepath[, …])	Creates a new instance of `TensorFlowModelDataset`.
`TensorFlowModelDataset.exists`()	Checks whether a data set’s output already exists by calling the provided _exists() method.
`TensorFlowModelDataset.from_config`(name, config)	Create a data set instance using the configuration provided.
`TensorFlowModelDataset.load`()	Loads data by delegation to the provided load method.
`TensorFlowModelDataset.release`()	Release any cached data.
`TensorFlowModelDataset.resolve_load_version`()	Compute the version the dataset should be loaded with.
`TensorFlowModelDataset.resolve_save_version`()	Compute the version the dataset should be saved with.
`TensorFlowModelDataset.save`(data)	Saves data by delegation to the provided save method.

DEFAULT_LOAD_ARGS = {}¶

DEFAULT_SAVE_ARGS = {'save_format': 'tf'}¶

__init__(filepath, load_args=None, save_args=None, version=None, credentials=None, fs_args=None)[source]¶

Creates a new instance of TensorFlowModelDataset.

Parameters:

filepath (str) – Filepath in POSIX format to a TensorFlow model directory prefixed with a protocol like s3://. If prefix is not provided file protocol (local filesystem) will be used. The prefix should be any protocol supported by fsspec. Note: http(s) doesn’t support versioning.
load_args (Optional[Dict[str, Any]]) – TensorFlow options for loading models. Here you can find all available arguments: https://www.tensorflow.org/api_docs/python/tf/keras/models/load_model All defaults are preserved.
save_args (Optional[Dict[str, Any]]) – TensorFlow options for saving models. Here you can find all available arguments: https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model All defaults are preserved, except for “save_format”, which is set to “tf”.
version (Optional[Version]) – If specified, should be an instance of kedro.io.core.Version. If its load attribute is None, the latest version will be loaded. If its save attribute is None, save version will be autogenerated.
credentials (Optional[Dict[str, Any]]) – Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {‘token’: None}.
fs_args (Optional[Dict[str, Any]]) – Extra arguments to pass into underlying filesystem class constructor (e.g. {“project”: “my-project”} for GCSFileSystem).

Return type:

None

exists()¶

Checks whether a data set’s output already exists by calling the provided _exists() method.

Return type:	`bool`
Returns:	Flag indicating whether the output already exists.
Raises:	`DataSetError` – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)¶

Create a data set instance using the configuration provided.

Parameters:	name (`str`) – Data set name. config (`Dict`[`str`, `Any`]) – Data set config dictionary. load_version (`Optional`[`str`]) – Version string to be used for `load` operation if the data set is versioned. Has no effect on the data set if versioning was not enabled. save_version (`Optional`[`str`]) – Version string to be used for `save` operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.
Return type:	`AbstractDataSet`
Returns:	An instance of an `AbstractDataSet` subclass.
Raises:	`DataSetError` – When the function fails to create the data set from its config.

load()¶

Loads data by delegation to the provided load method.

Return type:	`Any`
Returns:	Data returned by the provided load method.
Raises:	`DataSetError` – When underlying load method raises error.

release()¶

Release any cached data.

Raises:	`DataSetError` – when underlying release method raises error.
Return type:	`None`

resolve_load_version()¶

Compute the version the dataset should be loaded with.

Return type:	`Optional`[`str`]

resolve_save_version()¶

Compute the version the dataset should be saved with.

Return type:	`Optional`[`str`]

save(data)¶

Saves data by delegation to the provided save method.

Parameters:	data (`Any`) – the value to be saved by provided save method.
Raises:	`DataSetError` – when underlying save method raises error.
Return type:	`None`