tfds.features.ClassLabel

FeatureConnector for integer class labels.

Inherits From: Tensor, FeatureConnector

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

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

Methods

decode_batch_example

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See base class for details.

decode_example

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See base class for details.

decode_ragged_example

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See base class for details.

encode_example

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See base class for details.

from_config

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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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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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FeatureConnector factory (to overwrite).

Subclasses should overwritte this method. importing the feature connector from the config.

This function should not be called directly. FeatureConnector.from_json should be called instead.

This function See existing FeatureConnector for example of implementation.

Args
value FeatureConnector information. Match the dict returned by to_json_content.

Returns
The reconstructed FeatureConnector.

get_serialized_info

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See base class for details.

get_tensor_info

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See base class for details.

int2str

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Conversion integer => class name string.

load_metadata

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See base class for details.

repr_html

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Class labels are displayed with their name.

repr_html_batch

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

repr_html_ragged

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

save_config

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Exports the FeatureConnector to a file.

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

save_metadata

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See base class for details.

str2int

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Conversion class name string => integer.

to_json

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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",
            "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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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
Dict containing the FeatureConnector metadata. Will be forwarded to from_json_content when reconstructing the feature.

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