RNN训练的回调

! [ -e /content ] && pip install -Uqq fastai  # 在Colab上升级fastai

from __future__ import annotations
from fastai.basics import *

from nbdev.showdoc import *

使用语言模型的输出添加自回归（AR）和目标自回归（TAR）正则化的回调

@docs
class ModelResetter(Callback):
    "`Callback` that resets the model at each validation/training step"
    def before_train(self):    self.model.reset()
    def before_validate(self): self.model.reset()
    def after_fit(self):       self.model.reset()
    _docs = dict(before_train="Reset the model before training",
                 before_validate="Reset the model before validation",
                 after_fit="Reset the model after fitting")

class RNNCallback(Callback):
    "Save the raw and dropped-out outputs and only keep the true output for loss computation"
    def after_pred(self): self.learn.pred,self.raw_out,self.out = [o[-1] if is_listy(o) else o for o in self.pred]

class RNNRegularizer(Callback):
    "Add AR and TAR regularization"
    order,run_valid = RNNCallback.order+1,False
    def __init__(self, alpha=0., beta=0.): store_attr()
    def after_loss(self):
        if not self.training: return
        if self.alpha: self.learn.loss_grad += self.alpha * self.rnn.out.float().pow(2).mean()
        if self.beta:
            h = self.rnn.raw_out
            if len(h)>1: self.learn.loss_grad += self.beta * (h[:,1:] - h[:,:-1]).float().pow(2).mean()

def rnn_cbs(alpha=0., beta=0.):
    "All callbacks needed for (optionally regularized) RNN training"
    reg = [RNNRegularizer(alpha=alpha, beta=beta)] if alpha or beta else []
    return [ModelResetter(), RNNCallback()] + reg

导出 -

from nbdev import nbdev_export
nbdev_export()

Converted 00_torch_core.ipynb.
Converted 01_layers.ipynb.
Converted 01a_losses.ipynb.
Converted 02_data.load.ipynb.
Converted 03_data.core.ipynb.
Converted 04_data.external.ipynb.
Converted 05_data.transforms.ipynb.
Converted 06_data.block.ipynb.
Converted 07_vision.core.ipynb.
Converted 08_vision.data.ipynb.
Converted 09_vision.augment.ipynb.
Converted 09b_vision.utils.ipynb.
Converted 09c_vision.widgets.ipynb.
Converted 10_tutorial.pets.ipynb.
Converted 10b_tutorial.albumentations.ipynb.
Converted 11_vision.models.xresnet.ipynb.
Converted 12_optimizer.ipynb.
Converted 13_callback.core.ipynb.
Converted 13a_learner.ipynb.
Converted 13b_metrics.ipynb.
Converted 14_callback.schedule.ipynb.
Converted 14a_callback.data.ipynb.
Converted 15_callback.hook.ipynb.
Converted 15a_vision.models.unet.ipynb.
Converted 16_callback.progress.ipynb.
Converted 17_callback.tracker.ipynb.
Converted 18_callback.fp16.ipynb.
Converted 18a_callback.training.ipynb.
Converted 18b_callback.preds.ipynb.
Converted 19_callback.mixup.ipynb.
Converted 20_interpret.ipynb.
Converted 20a_distributed.ipynb.
Converted 21_vision.learner.ipynb.
Converted 22_tutorial.imagenette.ipynb.
Converted 23_tutorial.vision.ipynb.
Converted 24_tutorial.siamese.ipynb.
Converted 24_vision.gan.ipynb.
Converted 30_text.core.ipynb.
Converted 31_text.data.ipynb.
Converted 32_text.models.awdlstm.ipynb.
Converted 33_text.models.core.ipynb.
Converted 34_callback.rnn.ipynb.
Converted 35_tutorial.wikitext.ipynb.
Converted 36_text.models.qrnn.ipynb.
Converted 37_text.learner.ipynb.
Converted 38_tutorial.text.ipynb.
Converted 39_tutorial.transformers.ipynb.
Converted 40_tabular.core.ipynb.
Converted 41_tabular.data.ipynb.
Converted 42_tabular.model.ipynb.
Converted 43_tabular.learner.ipynb.
Converted 44_tutorial.tabular.ipynb.
Converted 45_collab.ipynb.
Converted 46_tutorial.collab.ipynb.
Converted 50_tutorial.datablock.ipynb.
Converted 60_medical.imaging.ipynb.
Converted 61_tutorial.medical_imaging.ipynb.
Converted 65_medical.text.ipynb.
Converted 70_callback.wandb.ipynb.
Converted 71_callback.tensorboard.ipynb.
Converted 72_callback.neptune.ipynb.
Converted 73_callback.captum.ipynb.
Converted 97_test_utils.ipynb.
Converted 99_pytorch_doc.ipynb.
Converted dev-setup.ipynb.
Converted index.ipynb.
Converted quick_start.ipynb.
Converted tutorial.ipynb.