I’ve used nervana distiller to train resnet50 baseline model wit imagenet 1k dataset. During training, validation accuracy remains zero while loss is decreasing.
— validate (epoch=84)-----------
200 samples (128 per mini-batch)
==> Top1: 0.000    Top5: 0.000    Loss: 14.722
==> Best [Top1: 0.000   Top5: 0.000   Sparsity:0.00   NNZ-Params: 25502912 on epoch: 84]
time python3 compress_classifier.py --arch resnet50 --data ../../../data/imagenet_1k -p=50 --lr=0.3 --epochs=180 --compress=../ssl/resnet50_imagenet_baseline_training.yaml -j=1 --deterministic 
What could be the reason?
my yaml file contains:
lr_schedulers:
training_lr:
class: MultiStepLR
milestones: [30, 60, 90, 100]
gamma: 0.1
policies:
- lr_scheduler:
instance_name: training_lr
starting_epoch: 0
ending_epoch: 200
frequency: 1