def _gen(data): """Makes a generator out of a structure of NumPy/EagerTensors.""" index_array = np.arange(num_samples) for _ in range(epochs): if shuffle: np.random.shuffle(index_array) batches = generic_utils.make_batches(num_samples, batch_size) for (batch_start, batch_end) in batches: batch_ids = index_array[batch_start:batch_end] flat_batch_data = training_utils.slice_arrays( nest.flatten(data), batch_ids, contiguous=(not shuffle)) yield nest.pack_sequence_as(data, flat_batch_data)
def _gen(data): """Makes a generator out of a structure of NumPy/EagerTensors.""" index_array = np.arange(num_samples) for _ in range(epochs): if shuffle: np.random.shuffle(index_array) batches = generic_utils.make_batches(num_samples, batch_size) for (batch_start, batch_end) in batches: batch_ids = index_array[batch_start:batch_end] flat_batch_data = training_utils.slice_arrays( nest.flatten(data), batch_ids, contiguous=(not shuffle)) yield nest.pack_sequence_as(data, flat_batch_data)
def slice_array(data): return training_utils.slice_arrays(data, ind.numpy(), contiguous=contiguous)