Public Keras utilities.
Modules
experimental module: Public API for tf.keras.utils.experimental namespace.
Classes
class CustomObjectScope: Exposes custom classes/functions to Keras deserialization internals.
class GeneratorEnqueuer: Builds a queue out of a data generator.
class OrderedEnqueuer: Builds a Enqueuer from a Sequence.
class Progbar: Displays a progress bar.
class Sequence: Base object for fitting to a sequence of data, such as a dataset.
class SequenceEnqueuer: Base class to enqueue inputs.
class SidecarEvaluator: A class designed for a dedicated evaluator task.
class custom_object_scope: Exposes custom classes/functions to Keras deserialization internals.
Functions
array_to_img(...): Converts a 3D Numpy array to a PIL Image instance.
audio_dataset_from_directory(...): Generates a tf.data.Dataset from audio files in a directory.
deserialize_keras_object(...): Turns the serialized form of a Keras object back into an actual object.
disable_interactive_logging(...): Turn off interactive logging.
enable_interactive_logging(...): Turn on interactive logging.
get_custom_objects(...): Retrieves a live reference to the global dictionary of custom objects.
get_file(...): Downloads a file from a URL if it not already in the cache.
get_registered_name(...): Returns the name registered to an object within the Keras framework.
get_registered_object(...): Returns the class associated with name if it is registered with Keras.
get_source_inputs(...): Returns the list of input tensors necessary to compute tensor.
image_dataset_from_directory(...): Generates a tf.data.Dataset from image files in a directory.
img_to_array(...): Converts a PIL Image instance to a Numpy array.
is_interactive_logging_enabled(...): Check if interactive logging is enabled.
load_img(...): Loads an image into PIL format.
model_to_dot(...): Convert a Keras model to dot format.
normalize(...): Normalizes a Numpy array.
pack_x_y_sample_weight(...): Packs user-provided data into a tuple.
pad_sequences(...): Pads sequences to the same length.
plot_model(...): Converts a Keras model to dot format and save to a file.
register_keras_serializable(...): Registers an object with the Keras serialization framework.
save_img(...): Saves an image stored as a Numpy array to a path or file object.
serialize_keras_object(...): Serialize a Keras object into a JSON-compatible representation.
set_random_seed(...): Sets all random seeds for the program (Python, NumPy, and TensorFlow).
split_dataset(...): Split a dataset into a left half and a right half (e.g. train / test).
text_dataset_from_directory(...): Generates a tf.data.Dataset from text files in a directory.
timeseries_dataset_from_array(...): Creates a dataset of sliding windows over a timeseries provided as array.
to_categorical(...): Converts a class vector (integers) to binary class matrix.
unpack_x_y_sample_weight(...): Unpacks user-provided data tuple.