Help protect the Great Barrier Reef with TensorFlow on Kaggle Join Challenge

tfds.features.Tensor

FeatureConnector for generic data of arbitrary shape and type.

Inherits From: FeatureConnector

shape Tensor shape
dtype Tensor dtype
encoding Internal encoding. See tfds.features.Encoding for available values.

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

Methods

decode_batch_example

View source

See base class for details.

decode_example

View source

See base class for details.

decode_ragged_example

View source

See base class for details.

encode_example

View source

See base class for details.

from_config

View source

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

View source

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

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

View source

See base class for details.

get_tensor_info

View source

See base class for details.

load_metadata

View source

Restore the feature metadata from disk.

If a dataset is re-loaded and generated files exists on disk, this function will restore the feature metadata from the saved file.

Args
data_dir str, path to the dataset folder to which save the info (ex: ~/datasets/cifar10/1.2.0/)
feature_name str, the name of the feature (from the FeaturesDict key)

repr_html

View source

Returns the HTML str representation of the object.

repr_html_batch

View source

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

repr_html_ragged

View source

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

save_config

View source

Exports the FeatureConnector to a file.

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

save_metadata

View source

Save the feature metadata on disk.

This function is called after the data has been generated (by _download_and_prepare) to save the feature connector info with the generated dataset.

Some dataset/features dynamically compute info during _download_and_prepare. For instance:

  • Labels are loaded from the downloaded data
  • Vocabulary is created from the downloaded data
  • ImageLabelFolder compute the image dtypes/shape from the manual_dir

After the info have been added to the feature, this function allow to save those additional info to be restored the next time the data is loaded.

By default, this function do not save anything, but sub-classes can overwrite the function.

Args
data_dir str, path to the dataset folder to which save the info (ex: ~/datasets/cifar10/1.2.0/)
feature_name str, the name of the feature (from the FeaturesDict key)

to_json

View source

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

View source

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']