# theanets.recurrent.batches¶

theanets.recurrent.batches(arrays, steps=100, batch_size=64, rng=None)

Create a callable that generates samples from a dataset.

Parameters: arrays : list of ndarray (time-steps, data-dimensions) Arrays of data. Rows in these arrays are assumed to correspond to time steps, and columns to variables. Multiple arrays can be given; in such a case, these arrays usually correspond to [input, output]—for example, for a recurrent regression problem—or [input, output, weights]—for a weighted regression or classification problem. steps : int, optional Generate samples of this many time steps. Defaults to 100. batch_size : int, optional Generate this many samples per call. Defaults to 64. This must match the batch_size parameter that was used when creating the recurrent network that will process the data. rng : numpy.random.RandomState or int, optional A random number generator, or an integer seed for a random number generator. If not provided, the random number generator will be created with an automatically chosen seed. callable : A callable that can be used inside a dataset for training a recurrent network.