tfds.features.ClassLabel

FeatureConnector for integer class labels.

Inherits From: Tensor, FeatureConnector

tfds.features.ClassLabel(
    *,
    num_classes: tfds.typing.Dim = None,
    names=None,
    names_file=None,
    doc: tfds.features.DocArg = 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.
doc Documentation of this feature (e.g. description).

doc

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

np_dtype

num_classes

numpy_dtype

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

Methods

catalog_documentation

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catalog_documentation() -> List[CatalogFeatureDocumentation]

Returns the feature documentation to be shown in the catalog.

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

View source

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_example_np

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

Encode the feature dict into NumPy-compatible input.

Args
example_data Value to convert to NumPy.

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

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/dir')

Args
root_dir Directory containing the features.json file.

Returns
The reconstructed feature instance.

from_json

View source

@classmethod
from_json(
    value: 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

View source

@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.
doc Documentation of this feature (e.g. description).

Returns
The reconstructed FeatureConnector.

from_proto

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

Instantiates a feature from its proto representation.

get_names_filepath

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@classmethod
get_names_filepath(
    data_dir, feature_name: str
) -> tfds.core.Path

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() -> feature_lib.TensorInfo

See base class for details.

get_tensor_spec

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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

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

View source

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

View source

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 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

View source

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

View source

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']