Additional training functions¶
from fastai.gen_doc.nbdoc import *
from fastai.train import *
from fastai.vision import *
These methods are automatically added to all Learner objects created after importing this module. They provide convenient access to a number of callbacks, without requiring them to be manually created.
show_doc(fit_one_cycle)
fit_one_cycle[source][test]
fit_one_cycle(learn:Learner,cyc_len:int,max_lr:Union[float,Collection[float],slice]=*slice(None, 0.003, None),moms:Point=(0.95, 0.85),div_factor:float=25.0,pct_start:float=0.3,final_div:float=None,wd:float=None,callbacks:Optional[Collection[Callback]]=None,tot_epochs:int=None,start_epoch:int=None*)
Tests found for fit_one_cycle:
pytest -sv tests/test_train.py::test_fit_one_cycle[source]
Some other tests where fit_one_cycle is used:
pytest -sv tests/test_tabular_train.py::test_empty_cont[source]pytest -sv tests/test_text_train.py::test_qrnn_works_if_split_fn_provided[source]pytest -sv tests/test_text_train.py::test_qrnn_works_with_no_split[source]
To run tests please refer to this guide.
Fit a model following the 1cycle policy.
show_doc(one_cycle_scheduler)
one_cycle_scheduler[source][test]
one_cycle_scheduler(lr_max:float, ****kwargs**:Any) →OneCycleScheduler
No tests found for one_cycle_scheduler. To contribute a test please refer to this guide and this discussion.
Instantiate a OneCycleScheduler with lr_max.
See OneCycleScheduler for details.
show_doc(lr_find)
See LRFinder for details.
show_doc(to_fp16)
to_fp16[source][test]
to_fp16(learn:Learner,loss_scale:float=*None,max_noskip:int=1000,dynamic:bool=False,clip:float=None,flat_master:bool=False,max_scale:float=16777216*) →Learner
No tests found for to_fp16. To contribute a test please refer to this guide and this discussion.
Put learn in FP16 precision mode.
See MixedPrecision for details.
show_doc(to_fp32)
to_fp32[source][test]
to_fp32(learn:Learner)
No tests found for to_fp32. To contribute a test please refer to this guide and this discussion.
Put learn back to FP32 precision mode.
show_doc(mixup)
mixup[source][test]
mixup(learn:Learner,alpha:float=*0.4,stack_x:bool=False,stack_y:bool=True*) →Learner
No tests found for mixup. To contribute a test please refer to this guide and this discussion.
Add mixup https://arxiv.org/abs/1710.09412 to learn.
show_doc(ClassificationInterpretation)
class ClassificationInterpretation[source][test]
ClassificationInterpretation(learn:Learner,probs:Tensor,y_true:Tensor,losses:Tensor,ds_type:DatasetType=*<DatasetType.Valid: 2>*)
Tests found for ClassificationInterpretation:
pytest -sv tests/test_vision_train.py::test_ClassificationInterpretation[source]
Some other tests where ClassificationInterpretation is used:
pytest -sv tests/test_tabular_train.py::test_confusion_tabular[source]pytest -sv tests/test_vision_train.py::test_interp[source]
To run tests please refer to this guide.
Interpretation methods for classification models.
See MixUpCallback for more details.
Additional callbacks¶
We'll show examples below using our MNIST sample. As usual the on_something methods are directly called by the fastai library, no need to call them yourself.
path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)
show_doc(ShowGraph, title_level=3)
class ShowGraph[source][test]
ShowGraph(learn) ::LearnerCallback
No tests found for ShowGraph. To contribute a test please refer to this guide and this discussion.
Update a graph of learner stats and metrics after each epoch.
learn = cnn_learner(data, models.resnet18, metrics=accuracy, callback_fns=ShowGraph)
learn.fit(3)
show_doc(ShowGraph.on_epoch_end)
on_epoch_end[source][test]
on_epoch_end(n_epochs:int,last_metrics:MetricsList, ****kwargs**) →bool
No tests found for on_epoch_end. To contribute a test please refer to this guide and this discussion.
If we have last_metrics plot them in our pbar graph
show_doc(GradientClipping)
class GradientClipping[source][test]
GradientClipping(learn:Learner,clip:float=*0.0*) ::LearnerCallback
No tests found for GradientClipping. To contribute a test please refer to this guide and this discussion.
Gradient clipping during training.
learn = cnn_learner(data, models.resnet18, metrics=accuracy,
callback_fns=partial(GradientClipping, clip=0.1))
learn.fit(1)
| epoch | train_loss | valid_loss | accuracy |
|---|---|---|---|
| 1 | 0.131133 | 0.078190 | 0.973013 |
show_doc(GradientClipping.on_backward_end)
on_backward_end[source][test]
on_backward_end(****kwargs**)
No tests found for on_backward_end. To contribute a test please refer to this guide and this discussion.
Clip the gradient before the optimizer step.
show_doc(BnFreeze)
class BnFreeze[source][test]
BnFreeze(learn) ::LearnerCallback
No tests found for BnFreeze. To contribute a test please refer to this guide and this discussion.
Freeze moving average statistics in all non-trainable batchnorm layers.
For batchnorm layers where requires_grad==False, you generally don't want to update their moving average statistics, in order to avoid the model's statistics getting out of sync with its pre-trained weights. You can add this callback to automate this freezing of statistics (internally, it calls eval on these layers).
learn = cnn_learner(data, models.resnet18, metrics=accuracy, callback_fns=BnFreeze)
learn.fit(1)
| epoch | train_loss | valid_loss | accuracy |
|---|---|---|---|
| 1 | 0.132564 | 0.078910 | 0.972031 |
show_doc(BnFreeze.on_epoch_begin)
on_epoch_begin[source][test]
on_epoch_begin(****kwargs**:Any)
No tests found for on_epoch_begin. To contribute a test please refer to this guide and this discussion.
Put bn layers in eval mode just after model.train().
Undocumented Methods - Methods moved below this line will intentionally be hidden¶
New Methods - Please document or move to the undocumented section¶
show_doc(ClassificationInterpretation.plot_top_losses)
_cl_int_plot_top_losses[source][test]
_cl_int_plot_top_losses(k,largest=*True,figsize=(12, 12),heatmap:bool=True,heatmap_thresh:int=16,return_fig:bool=None*) →Optional[Figure]
No tests found for _cl_int_plot_top_losses. To contribute a test please refer to this guide and this discussion.
Show images in top_losses along with their prediction, actual, loss, and probability of predicted class.
show_doc(ClassificationInterpretation.from_learner)
_cl_int_from_learner[source][test]
_cl_int_from_learner(learn:Learner,ds_type:DatasetType=*<DatasetType.Valid: 2>,tta=False*)
Create an instance of ClassificationInterpretation. tta indicates if we want to use Test Time Augmentation.
show_doc(ClassificationInterpretation.top_losses)
show_doc(ClassificationInterpretation.confusion_matrix)
show_doc(ClassificationInterpretation.most_confused)
show_doc(ClassificationInterpretation.plot_confusion_matrix)
plot_confusion_matrix[source][test]
plot_confusion_matrix(normalize:bool=*False,title:str='Confusion matrix',cmap:Any='Blues',slice_size:int=1,norm_dec:int=2,plot_txt:bool=True,return_fig:bool=None, ***kwargs**) →Optional[Figure]
No tests found for plot_confusion_matrix. To contribute a test please refer to this guide and this discussion.
Plot the confusion matrix, with title and using cmap.
show_doc(ClassificationInterpretation.plot_multi_top_losses)
_cl_int_plot_multi_top_losses[source][test]
_cl_int_plot_multi_top_losses(samples:int=*3,figsize:Tuple[int,int]=(8, 8),save_misclassified:bool=False*)
No tests found for _cl_int_plot_multi_top_losses. To contribute a test please refer to this guide and this discussion.
Show images in top_losses along with their prediction, actual, loss, and probability of predicted class in a multilabeled dataset.