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Implementation of Deep Feature Selection algorithm
I downloaded the notebooks and dataset from the project. I executed the codes using jupyter notebook, and the codes were
running successfully until this one:
from pytorch_lightning import Trainer
from test_tube import Experiment
import os
model = CoolSystem(loss_attrs=loss_attrs, class_weights=class_weights)
# PyTorch summarywriter with a few bells and whistles
exp = Experiment(save_dir=os.getcwd())
# train on cpu using only 10% of the data (for demo purposes)
# pass in experiment for automatic tensorboard logging.
trainer = Trainer(experiment=exp, max_nb_epochs=20, gpus=[0])
trainer.fit(model)
and I got the error below:
---------------------------------------------------------------------------
MisconfigurationException Traceback (most recent call last)
<ipython-input-28-de23d35f9938> in <module>()
13
14
---> 15 trainer.fit(model)
4 frames
/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/data_loading.py in reset_train_dataloader(self, model)
128 must be an int. An int k specifies checking validation every k training batches.
129 '''
--> 130 raise MisconfigurationException(m)
131
132 self._percent_range_check('val_check_interval')
MisconfigurationException:
When using an infinite DataLoader (e.g. with an IterableDataset or when DataLoader
does not implement `__len__`) for `train_dataloader`, `Trainer(val_check_interval)`
must be an int. An int k specifies checking validation every k training batches.
After searching some help from Google and Stackoverflow, I still don't get the solution. Is there any way to solve this? Thanks
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