theanets.trainer.SampleTrainer¶
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class
theanets.trainer.
SampleTrainer
(network)[source]¶ This trainer replaces network weights with samples from the input.
Methods
__init__
(network)x.__init__(…) initializes x; see help(type(x)) for signature itertrain
(train[, valid])Train a model using a training and validation set. reservoir
(xs, n, rng)Select a random sample of n items from xs. -
itertrain
(train, valid=None, **kwargs)[source]¶ Train a model using a training and validation set.
This method yields a series of monitor values to the caller. After every iteration, a pair of monitor dictionaries is generated: one evaluated on the training dataset, and another evaluated on the validation dataset. The validation monitors might not be updated during every training iteration; in this case, the most recent validation monitors will be yielded along with the training monitors.
Parameters: - train :
Dataset
A set of training data for computing updates to model parameters.
- valid :
Dataset
A set of validation data for computing monitor values and determining when the loss has stopped improving.
Yields: - training : dict
A dictionary mapping monitor names to values, evaluated on the training dataset.
- validation : dict
A dictionary containing monitor values evaluated on the validation dataset.
- train :
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