Callback for RNN training

Callback that uses the outputs of language models to add AR and TAR regularization

ModelResetter

 ModelResetter (after_create=None, before_fit=None, before_epoch=None,
                before_train=None, before_batch=None, after_pred=None,
                after_loss=None, before_backward=None,
                after_cancel_backward=None, after_backward=None,
                before_step=None, after_cancel_step=None, after_step=None,
                after_cancel_batch=None, after_batch=None,
                after_cancel_train=None, after_train=None,
                before_validate=None, after_cancel_validate=None,
                after_validate=None, after_cancel_epoch=None,
                after_epoch=None, after_cancel_fit=None, after_fit=None)

Callback that resets the model at each validation/training step

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RNNCallback

 RNNCallback (after_create=None, before_fit=None, before_epoch=None,
              before_train=None, before_batch=None, after_pred=None,
              after_loss=None, before_backward=None,
              after_cancel_backward=None, after_backward=None,
              before_step=None, after_cancel_step=None, after_step=None,
              after_cancel_batch=None, after_batch=None,
              after_cancel_train=None, after_train=None,
              before_validate=None, after_cancel_validate=None,
              after_validate=None, after_cancel_epoch=None,
              after_epoch=None, after_cancel_fit=None, after_fit=None)

Save the raw and dropped-out outputs and only keep the true output for loss computation

source

RNNRegularizer

 RNNRegularizer (alpha=0.0, beta=0.0)

Add AR and TAR regularization

source

rnn_cbs

 rnn_cbs (alpha=0.0, beta=0.0)

All callbacks needed for (optionally regularized) RNN training