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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
)
Attributes | |
---|---|
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
catalog_documentation() -> List[CatalogFeatureDocumentation]
Returns the feature documentation to be shown in the catalog.
cls_from_name
@classmethod
cls_from_name( python_class_name: str ) -> Type['FeatureConnector']
Returns the feature class for the given Python class.
decode_batch_example
decode_batch_example(
example_data
)
See base class for details.
decode_example
decode_example(
tfexample_data
)
See base class for details.
decode_example_np
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
decode_ragged_example(
example_data
)
See base class for details.
encode_example
encode_example(
example_data
)
See base class for details.
from_config
@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
@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
@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
@classmethod
from_proto( feature_proto: feature_pb2.Feature ) -> T
Instantiates a feature from its proto representation.
get_names_filepath
@classmethod
get_names_filepath( data_dir, feature_name: str ) ->
tfds.core.Path
get_serialized_info
get_serialized_info()
See base class for details.
get_tensor_info
get_tensor_info() -> feature_lib.TensorInfo
See base class for details.
get_tensor_spec
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
int2str(
int_value
)
Conversion integer => class name string.
load_metadata
load_metadata(
data_dir, feature_name=None
)
See base class for details.
repr_html
repr_html(
ex: int
) -> str
Class labels are displayed with their name.
repr_html_batch
repr_html_batch(
ex: np.ndarray
) -> str
Returns the HTML str representation of the object (Sequence).
repr_html_ragged
repr_html_ragged(
ex: np.ndarray
) -> str
Returns the HTML str representation of the object (Nested sequence).
save_config
save_config(
root_dir: str
) -> None
Exports the FeatureConnector
to a file.
Args | |
---|---|
root_dir
|
path/to/dir containing the features.json
|
save_metadata
save_metadata(
data_dir, feature_name=None
)
See base class for details.
str2int
str2int(
str_value
)
Conversion class name string => integer.
to_json
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 into_json_content
. Can contain nested sub-features (like fortfds.features.FeaturesDict
andtfds.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
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
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. |
Class Variables | |
---|---|
ALIASES |
['tensorflow_datasets.core.features.feature.Tensor']
|