/tf_datascheduler

TensorFlow dynamic datascheduler for mixing datasets while training

Primary LanguageJupyter Notebook

TensorFlow dynamic data scheduler

combines several datasets according to weights, dynamically during training of a TF model.

It is composed of two classes:

ScheduleDataset: It encapsulates the same functionality as tf.data.experimental.sample_from_datasets, but weights are internally represented as tensors so that they are part of the computational graph and can be changed during training with ScheduleDataset_Callback.

ScheduleDataset_Callback: A callback class that, when used in .fit, dynamically changes the weights of a ScheduleDataset according to a schedule defined by the dataset_weights_fn function. See associated demo notebook for examples.

See the demo notebook for further details.