theanets.recurrent.batches¶
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theanets.recurrent.
batches
(samples, labels=None, steps=100, batch_size=64, rng=None)¶ Return a callable that generates samples from a dataset.
Parameters: samples : ndarray (time-steps, data-dimensions)
An array of data. Rows in this array correspond to time steps, and columns to variables.
labels : ndarray (time-steps, label-dimensions), optional
An array of data. Rows in this array correspond to time steps, and columns to labels.
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, optionalA 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.
Returns: callable :
A callable that can be used inside a dataset for training a recurrent network.