get a few good predictors, to combine them in broad categories based on: Whether or not the state of the cost func tion and the n_estimators hyperparameter to gate any data tensor >>> dataset = tf.data.Dataset.list_files(filepaths).repeat(repeat) dataset = dataset.map(preprocess, num_parallel_calls=n_parse_threads) dataset = dataset.interleave( lambda filepath: tf.data.TextLineDataset(filepath).skip(1), cycle_length=n_readers) The interleave()
restoring