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]¶
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 usage for the YAML API:
tensorflow_model: type: tensorflow.TensorFlowModelDataset filepath: data/06_models/tensorflow_model.h5 load_args: compile: False save_args: overwrite: True include_optimizer: False credentials: tf_creds
Example usage for the Python API:
from kedro.extras.datasets.tensorflow import TensorFlowModelDataset import tensorflow as tf import numpy as np data_set = TensorFlowModelDataset("data/06_models/tensorflow_model.h5") 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
Methods
exists
()Checks whether a data set's output already exists by calling the provided _exists() method.
from_config
(name, config[, load_version, ...])Create a data set instance using the configuration provided.
load
()Loads data by delegation to the provided load method.
release
()Release any cached data.
Compute the version the dataset should be loaded with.
Compute the version the dataset should be saved with.
save
(data)Saves data by delegation to the provided save method.
- DEFAULT_LOAD_ARGS: Dict[str, Any] = {}¶
- DEFAULT_SAVE_ARGS: Dict[str, Any] = {'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 byfsspec
. 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 ofkedro.io.core.Version
. If itsload
attribute is None, the latest version will be loaded. If itssave
attribute is None, save version will be autogenerated.credentials (
Optional
[Dict
[str
,Any
]]) – Credentials required to get access to the underlying filesystem. E.g. forGCSFileSystem
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”} forGCSFileSystem
).
- 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 forload
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 forsave
operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.
- Return type
- 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
TypeVar
(_DO
)- 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 (
TypeVar
(_DI
)) – the value to be saved by provided save method.- Raises
DataSetError – when underlying save method raises error.
FileNotFoundError – when save method got file instead of dir, on Windows.
NotADirectoryError – when save method got file instead of dir, on Unix.
- Return type
None