mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-17 12:40:36 +08:00
William Falcon
792ad00ff9
* added fixed frequency val batch check * added fixed frequency val batch check * Finished IterableDataset support * flake8 * flake8 * flake8
71 lines
2.3 KiB
Markdown
71 lines
2.3 KiB
Markdown
The lightning validation loop handles everything except the actual computations of your model. To decide what will happen in your validation loop, define the [validation_step function](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#validation_step).
|
|
Below are all the things lightning automates for you in the validation loop.
|
|
|
|
**Note**
|
|
Lightning will run 5 steps of validation in the beginning of training as a sanity check so you don't have to wait until a full epoch to catch possible validation issues.
|
|
|
|
|
|
---
|
|
#### Check validation every n epochs
|
|
If you have a small dataset you might want to check validation every n epochs
|
|
``` {.python}
|
|
# DEFAULT
|
|
trainer = Trainer(check_val_every_n_epoch=1)
|
|
```
|
|
|
|
---
|
|
#### Set how much of the validation set to check
|
|
If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag
|
|
|
|
val_percent_check will be overwritten by overfit_pct if `overfit_pct > 0`
|
|
|
|
``` {.python}
|
|
# DEFAULT
|
|
trainer = Trainer(val_percent_check=1.0)
|
|
|
|
# check 10% only
|
|
trainer = Trainer(val_percent_check=0.1)
|
|
```
|
|
|
|
---
|
|
#### Set how much of the test set to check
|
|
If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag
|
|
|
|
test_percent_check will be overwritten by overfit_pct if `overfit_pct > 0`
|
|
|
|
``` {.python}
|
|
# DEFAULT
|
|
trainer = Trainer(test_percent_check=1.0)
|
|
|
|
# check 10% only
|
|
trainer = Trainer(test_percent_check=0.1)
|
|
```
|
|
|
|
---
|
|
#### Set validation check frequency within 1 training epoch
|
|
For large datasets it's often desirable to check validation multiple times within a training loop.
|
|
Pass in a float to check that often within 1 training epoch.
|
|
Pass in an int k to check every k training batches. Must use an int if using
|
|
an IterableDataset.
|
|
|
|
``` {.python}
|
|
# DEFAULT
|
|
trainer = Trainer(val_check_interval=0.95)
|
|
|
|
# check every .25 of an epoch
|
|
trainer = Trainer(val_check_interval=0.25)
|
|
|
|
# check every 100 train batches (ie: for IterableDatasets or fixed frequency)
|
|
trainer = Trainer(val_check_interval=100)
|
|
```
|
|
|
|
---
|
|
#### Set the number of validation sanity steps
|
|
Lightning runs a few steps of validation in the beginning of training. This avoids crashing in the validation loop sometime deep into a lengthy training loop.
|
|
``` {.python}
|
|
# DEFAULT
|
|
trainer = Trainer(nb_sanity_val_steps=5)
|
|
```
|
|
|
|
You can use `Trainer(nb_sanity_val_steps=0)` to skip the sanity check.
|