Modules
experimental
module
legacy
module
Classes
class CustomObjectScope
: Exposes custom classes/functions to Keras deserialization internals.
class FeatureSpace
: One-stop utility for preprocessing and encoding structured data.
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 TimedThread
: Time-based interval Threads.
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(...)
: Retrieve the object by deserializing the config dict.
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(...)
: Retrieve the config dict by serializing the Keras object.
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.
to_ordinal(...)
: Converts a class vector (integers) to an ordinal regression matrix.
unpack_x_y_sample_weight(...)
: Unpacks user-provided data tuple.
warmstart_embedding_matrix(...)
: Warm start embedding matrix with changing vocab.