callbacks.csv_logger

callbacks.csv_logger

Callbacks that saves the tracked metrics during training

CSV Logger

`class` `CSVLogger`[source][test]

CSVLogger(learn:Learner, filename:str='history', append:bool=False) :: LearnerCallback Tests found for CSVLogger:

pytest -sv tests/test_callbacks_csv_logger.py::test_logger [source]

To run tests please refer to this guide.

A LearnerCallback that saves history of metrics while training learn into CSV filename.

First let’s show an example of use, with a training on the usual MNIST dataset.

path = untar_data(URLs.MNIST_TINY)
data = ImageDataBunch.from_folder(path)
learn = Learner(data, simple_cnn((3, 16, 16, 2)), metrics=[accuracy, error_rate], callback_fns=[CSVLogger])

learn.fit(3)

Total time: 00:02

epoch	train_loss	valid_loss	accuracy	error_rate	time
0	0.643073	0.536124	0.931330	0.068670	00:00
1	0.523130	0.246320	0.952790	0.047210	00:00
2	0.402764	0.147998	0.938484	0.061516	00:00

Training details have been saved in ‘history.csv’.

Note that it only saves float/int metrics, so time currently is not saved. This could be saved but requires changing the recording - you can submit a PR fixing that.

learn.path.ls()

[PosixPath('/home/stas/.fastai/data/mnist_tiny/models'),
 PosixPath('/home/stas/.fastai/data/mnist_tiny/labels.csv'),
 PosixPath('/home/stas/.fastai/data/mnist_tiny/cleaned.csv'),
 PosixPath('/home/stas/.fastai/data/mnist_tiny/train'),
 PosixPath('/home/stas/.fastai/data/mnist_tiny/test'),
 PosixPath('/home/stas/.fastai/data/mnist_tiny/valid'),
 PosixPath('/home/stas/.fastai/data/mnist_tiny/export.pkl'),
 PosixPath('/home/stas/.fastai/data/mnist_tiny/history.csv')]

Note that, as with all LearnerCallback, you can access the object as an attribute of learn after it has been created. Here it’s learn.csv_logger.

`read_logged_file`[source][test]

read_logged_file() Tests found for read_logged_file:

Some other tests where read_logged_file is used:

pytest -sv tests/test_callbacks_csv_logger.py::test_logger [source]

To run tests please refer to this guide.

Read the content of saved file

learn.csv_logger.read_logged_file()

	epoch	train_loss	valid_loss	accuracy	error_rate
0	0	0.643073	0.536124	0.931330	0.068670
1	1	0.523130	0.246320	0.952790	0.047210
2	2	0.402764	0.147998	0.938484	0.061516

Optionally you can set append=True to log results of consequent stages of training.

# don't forget to remove the old file
if learn.csv_logger.path.exists(): os.remove(learn.csv_logger.path)

learn = Learner(data, simple_cnn((3, 16, 16, 2)), metrics=[accuracy, error_rate],
                callback_fns=[partial(CSVLogger, append=True)])

# stage-1
learn.fit(3)

Total time: 00:02

epoch	train_loss	valid_loss	accuracy	error_rate	time
0	0.604549	0.493241	0.766810	0.233190	00:00
1	0.486367	0.226189	0.948498	0.051502	00:00
2	0.377847	0.127142	0.958512	0.041488	00:00

# stage-2
learn.fit(3)

Total time: 00:02

epoch	train_loss	valid_loss	accuracy	error_rate	time
0	0.189441	0.118532	0.954220	0.045780	00:00
1	0.177432	0.110913	0.965665	0.034335	00:00
2	0.168626	0.097646	0.968526	0.031474	00:00

learn.csv_logger.read_logged_file()

	epoch	train_loss	valid_loss	accuracy	error_rate
0	0	0.604549	0.493241	0.766810	0.233190
1	1	0.486367	0.226189	0.948498	0.051502
2	2	0.377847	0.127142	0.958512	0.041488
3	epoch	train_loss	valid_loss	accuracy	error_rate
4	0	0.189441	0.118532	0.954220	0.045780
5	1	0.177432	0.110913	0.965665	0.034335
6	2	0.168626	0.097646	0.968526	0.031474

Calback methods

You don’t call these yourself - they’re called by fastai’s Callback system automatically to enable the class’s functionality.

`on_train_begin`[source][test]

on_train_begin(**kwargs:Any) No tests found for on_train_begin. To contribute a test please refer to this guide and this discussion.

Prepare file with metric names.

`on_epoch_end`[source][test]

on_epoch_end(epoch:int, smooth_loss:Tensor, last_metrics:MetricsList, **kwargs:Any) → bool No tests found for on_epoch_end. To contribute a test please refer to this guide and this discussion.