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|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/class_label_feature.py#L31-L217) |

`FeatureConnector` for integer class labels.

Inherits From: [`Tensor`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/Tensor), [`FeatureConnector`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/FeatureConnector)

    tfds.features.ClassLabel(
        *,
        num_classes: (int | None) = None,
        names: (Iterable[str] | None) = None,
        names_file: (epath.PathLike | None) = None,
        doc: https://www.tensorflow.org/datasets/api_docs/python/tfds/features/DocArg = None
    )

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| ## Args ||
|---|---|
| `num_classes` | Number of classes. All labels must be \< num_classes. |
| `names` | String names for the integer classes. The order in which the names are provided is kept. |
| `names_file` | Path to a file with names for the integer classes, one per line. |
| `doc` | Documentation of this feature (e.g. description). |

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| ## Attributes ||
|---|---|
| `doc` | <br /> <br /> |
| `dtype` | Return the dtype (or dict of dtype) of this FeatureConnector. |
| `names` | <br /> <br /> |
| `np_dtype` | <br /> <br /> |
| `num_classes` | <br /> <br /> |
| `numpy_dtype` | <br /> <br /> |
| `shape` | Return the shape (or dict of shape) of this FeatureConnector. |
| `tf_dtype` | <br /> <br /> |

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## Methods

### `catalog_documentation`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L1002-L1031)

    catalog_documentation() -> List[CatalogFeatureDocumentation]

Returns the feature documentation to be shown in the catalog.

### `cls_from_name`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L401-L428)

    @classmethod
    cls_from_name(
        python_class_name: str
    ) -> Type['FeatureConnector']

Returns the feature class for the given Python class.

### `decode_batch_example`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/tensor_feature.py#L318-L327)

    decode_batch_example(
        example_data
    )

See base class for details.

### `decode_batch_example_np`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L832-L834)

    decode_batch_example_np(
        batch
    )

See decode_batch_example.

### `decode_example`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/tensor_feature.py#L288-L300)

    decode_example(
        tfexample_data
    )

See base class for details.

### `decode_example_np`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/tensor_feature.py#L302-L316)

    decode_example_np(
        example_data: type_utils.NpArrayOrScalar
    ) -> type_utils.NpArrayOrScalar

Decode the example data into NumPy-compatible input.

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| Args ||
|---|---|
| `example_data` | Value to convert to NumPy. |

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| Returns ||
|---|---|
| `np_data` | Data as NumPy-compatible type: either a Python primitive (bytes, int, etc) or a NumPy array. |

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### `decode_ragged_example`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/tensor_feature.py#L329-L338)

    decode_ragged_example(
        example_data
    )

See base class for details.

### `decode_ragged_example_np`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/tensor_feature.py#L340-L346)

    decode_ragged_example_np(
        example_data
    )

See base class for details.

### `encode_example`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/class_label_feature.py#L153-L174)

    encode_example(
        example_data
    )

See base class for details.

### `from_config`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L646-L665)

    @classmethod
    from_config(
        root_dir: str
    ) -> FeatureConnector

Reconstructs the FeatureConnector from the config file.

#### Usage:

    features = FeatureConnector.from_config('path/to/dir')

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| Args ||
|---|---|
| `root_dir` | Directory containing the features.json file. |

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| Returns ||
|---|---|
| The reconstructed feature instance. ||

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### `from_json`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L434-L480)

    @classmethod
    from_json(
        value: Json
    ) -> FeatureConnector

FeatureConnector factory.

This function should be called from the [`tfds.features.FeatureConnector`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/FeatureConnector)
base class. Subclass should implement the `from_json_content`.

#### Example:

    feature = tfds.features.FeatureConnector.from_json(
        {'type': 'Image', 'content': {'shape': [32, 32, 3], 'dtype': 'uint8'} }
    )
    assert isinstance(feature, tfds.features.Image)

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| Args ||
|---|---|
| `value` | `dict(type=, content=)` containing the feature to restore. Match dict returned by `to_json`. |

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| Returns ||
|---|---|
| The reconstructed FeatureConnector. ||

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### `from_json_content`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/class_label_feature.py#L204-L210)

    @classmethod
    from_json_content(
        value: Union[https://www.tensorflow.org/datasets/api_docs/python/tfds/typing/Json, feature_pb2.ClassLabel]
    ) -> 'ClassLabel'

FeatureConnector factory (to overwrite).

Subclasses should overwrite this method. This method is used when
importing the feature connector from the config.

This function should not be called directly. [`FeatureConnector.from_json`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/FeatureConnector#from_json)
should be called instead.

See existing FeatureConnectors for implementation examples.

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| Args ||
|---|---|
| `value` | FeatureConnector information represented as either Json or a Feature proto. The content must match what is returned by `to_json_content`. |
| `doc` | Documentation of this feature (e.g. description). |

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| Returns ||
|---|---|
| The reconstructed FeatureConnector. ||

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### `from_proto`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L618-L632)

    @classmethod
    from_proto(
        feature_proto: feature_pb2.Feature
    ) -> T

Instantiates a feature from its proto representation.

### `get_names_filepath`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/class_label_feature.py#L215-L217)

    @classmethod
    get_names_filepath(
        data_dir, feature_name: str
    ) -> https://www.tensorflow.org/datasets/api_docs/python/tfds/core/Path

### `get_serialized_info`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/tensor_feature.py#L145-L181)

    get_serialized_info()

See base class for details.

### `get_tensor_info`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/tensor_feature.py#L133-L143)

    get_tensor_info() -> feature_lib.TensorInfo

See base class for details.

### `get_tensor_spec`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L361-L370)

    get_tensor_spec() -> TreeDict[tf.TensorSpec]

Returns the tf.TensorSpec of this feature (not the element spec!).

Note that the output of this method may not correspond to the element spec
of the dataset. For example, currently this method does not support
RaggedTensorSpec.

### `int2str`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/class_label_feature.py#L139-L151)

    int2str(
        int_value: int
    ) -> str

Conversion integer =\> class name string.

### `load_metadata`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/class_label_feature.py#L183-L190)

    load_metadata(
        data_dir, feature_name=None
    ) -> Optional[list[str]]

See base class for details.

### `repr_html`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/class_label_feature.py#L195-L202)

    repr_html(
        ex: int
    ) -> str

Class labels are displayed with their name.

### `repr_html_batch`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L859-L874)

    repr_html_batch(
        ex: np.ndarray
    ) -> str

Returns the HTML str representation of the object (Sequence).

### `repr_html_ragged`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L876-L878)

    repr_html_ragged(
        ex: np.ndarray
    ) -> str

Returns the HTML str representation of the object (Nested sequence).

### `save_config`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L634-L644)

    save_config(
        root_dir: str
    ) -> None

Exports the `FeatureConnector` to a file.

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| Args ||
|---|---|
| `root_dir` | `path/to/dir` containing the `features.json` |

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### `save_metadata`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/class_label_feature.py#L176-L181)

    save_metadata(
        data_dir, feature_name=None
    ) -> None

See base class for details.

### `str2int`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/class_label_feature.py#L124-L137)

    str2int(
        str_value: str
    ) -> int

Conversion class name string =\> integer.

### `to_json`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L482-L545)

    to_json() -> Json

Exports the FeatureConnector to Json.

Each feature is serialized as a `dict(type=..., content=...)`.

- `type`: The cannonical name of the feature (`module.FeatureName`).
- `content`: is specific to each feature connector and defined in `to_json_content`. Can contain nested sub-features (like for [`tfds.features.FeaturesDict`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/FeaturesDict) and [`tfds.features.Sequence`](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/Sequence)).

#### For example:

    tfds.features.FeaturesDict({
        'input': tfds.features.Image(),
        'target': tfds.features.ClassLabel(num_classes=10),
    })

#### Is serialized as:

    {
        "type": "tensorflow_datasets.core.features.features_dict.FeaturesDict",
        "content": {
            "input": {
                "type": "tensorflow_datasets.core.features.image_feature.Image",
                "content": {
                    "shape": [null, null, 3],
                    "dtype": "uint8",
                    "encoding_format": "png"
                }
            },
            "target": {
                "type":
                "tensorflow_datasets.core.features.class_label_feature.ClassLabel",
                "content": {
                  "num_classes": 10
                }
            }
        }
    }

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| Returns ||
|---|---|
| A `dict(type=, content=)`. Will be forwarded to `from_json` when reconstructing the feature. ||

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### `to_json_content`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/class_label_feature.py#L212-L213)

    to_json_content() -> feature_pb2.ClassLabel

FeatureConnector factory (to overwrite).

This function should be overwritten by the subclass to allow re-importing
the feature connector from the config. See existing FeatureConnector for
example of implementation.

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| Returns ||
|---|---|
| The FeatureConnector metadata in either a dict, or a Feature proto. This output is used in `from_json_content` when reconstructing the feature. ||

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### `to_proto`

[View source](https://github.com/tensorflow/datasets/blob/v4.9.10/tensorflow_datasets/core/features/feature.py#L589-L616)

    to_proto() -> feature_pb2.Feature

Exports the FeatureConnector to the Feature proto.

For features that have a specific schema defined in a proto, this
function needs to be overriden. If there's no specific proto schema,
then the feature will be represented using JSON.

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| Returns ||
|---|---|
| The feature proto describing this feature. ||

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| ## Class Variables ||
|---|---|
| ALIASES | `['tensorflow_datasets.core.features.feature.Tensor']` |

<br />