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tfds.features.ClassLabel

FeatureConnector for integer class labels.

Inherits From: Tensor, FeatureConnector

tfds.features.ClassLabel(
    *, num_classes=None, names=None, names_file=None
)

num_classes int, number of classes. All labels must be < num_classes.
names list<str>, string names for the integer classes. The order in which the names are provided is kept.
names_file str, path to a file with names for the integer classes, one per line.

dtype Return the dtype (or dict of dtype) of this FeatureConnector.
names

num_classes

numpy_dtype

shape Return the shape (or dict of shape) of this FeatureConnector.

Methods

cls_from_name

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

Returns the feature class for the given Python class.

decode_batch_example

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decode_batch_example(
    example_data
)

See base class for details.

decode_example

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decode_example(
    tfexample_data
)

See base class for details.

decode_ragged_example

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decode_ragged_example(
    example_data
)

See base class for details.

encode_example

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encode_example(
    example_data
)

See base class for details.

from_config

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@classmethod
from_config(
    root_dir: str
) -> 'FeatureConnector'

Reconstructs the FeatureConnector from the config file.

Usage:

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

Args
root_dir Directory containing to the features.json file.

Returns
The reconstructed feature instance.

from_json

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@classmethod
from_json(
    value: tfds.typing.Json
) -> 'FeatureConnector'

FeatureConnector factory.

This function should be called from the 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)

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

Returns
The reconstructed FeatureConnector.

from_json_content

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@classmethod
from_json_content(
    value: Union[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 should be called instead.

See existing FeatureConnectors for implementation examples.

Args
value FeatureConnector information represented as either Json or a Feature proto. The content must match what is returned by to_json_content.

Returns
The reconstructed FeatureConnector.

from_proto

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

get_serialized_info

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get_serialized_info()

See base class for details.

get_tensor_info

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get_tensor_info() -> tfds.features.TensorInfo

See base class for details.

int2str

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int2str(
    int_value
)

Conversion integer => class name string.

load_metadata

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load_metadata(
    data_dir, feature_name=None
)

See base class for details.

repr_html

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repr_html(
    ex: int
) -> str

Class labels are displayed with their name.

repr_html_batch

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repr_html_batch(
    ex: np.ndarray
) -> str

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

repr_html_ragged

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repr_html_ragged(
    ex: np.ndarray
) -> str

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

save_config

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save_config(
    root_dir: str
) -> None

Exports the FeatureConnector to a file.

Args
root_dir path/to/dir containing the features.json

save_metadata

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save_metadata(
    data_dir, feature_name=None
)

See base class for details.

str2int

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str2int(
    str_value
)

Conversion class name string => integer.

to_json

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to_json() -> tfds.typing.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 and 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
            }
        }
    }
}

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

to_json_content

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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.

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

to_proto

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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.

Returns
The feature proto describing this feature.

ALIASES ['tensorflow_datasets.core.features.feature.Tensor']