training loss is 'nan'

[008karan]

In training i am getting only nan loss. after 40 epoch also no update.

epoch 0, loss_tr=nan err_tr=1.000000 loss_te=nan err_te=1.000000 err_te_snt=1.000000
epoch 8, loss_tr=nan err_tr=1.000000 loss_te=nan err_te=1.000000 err_te_snt=1.000000
epoch 16, loss_tr=nan err_tr=1.000000 loss_te=nan err_te=1.000000 err_te_snt=1.000000
epoch 24, loss_tr=nan err_tr=1.000000 loss_te=nan err_te=1.000000 err_te_snt=1.000000
epoch 32, loss_tr=nan err_tr=1.000000 loss_te=nan err_te=1.000000 err_te_snt=1.000000
epoch 40, loss_tr=nan err_tr=1.000000 loss_te=nan err_te=1.000000 err_te_snt=1.000000
epoch 0, loss_tr=nan err_tr=0.998643 loss_te=nan err_te=0.998567 err_te_snt=0.998567
epoch 0, loss_tr=nan err_tr=0.998643 loss_te=nan err_te=0.998567 err_te_snt=0.998567
epoch 8, loss_tr=nan err_tr=0.998555 loss_te=nan err_te=0.998567 err_te_snt=0.998567
epoch 16, loss_tr=nan err_tr=0.998525 loss_te=nan err_te=0.998567 err_te_snt=0.998567
epoch 24, loss_tr=nan err_tr=0.998604 loss_te=nan err_te=0.998567 err_te_snt=0.998567
epoch 32, loss_tr=nan err_tr=0.998457 loss_te=nan err_te=0.998567 err_te_snt=0.998567
epoch 40, loss_tr=nan err_tr=0.998613 loss_te=nan err_te=0.998567 err_te_snt=0.998567

after debugging I found training batches getting generated as I printed their tensor. but when i am printing pout its nan

  for i in range(N_batches):

    [inp,lab]=create_batches_rnd(batch_size,data_folder,wav_lst_tr,snt_tr,wlen,lab_dict,0.2)
    pout=DNN2_net(DNN1_net(CNN_net(inp)))

    pred=torch.max(pout,dim=1)[1]
    loss = cost(pout, lab.long())
    err = torch.mean((pred!=lab.long()).float())
    print('***********',pout)

output:

        [-6.4934, -6.5646, -6.5842,  ..., -6.5785, -6.5211, -6.5626],
        [-6.5141, -6.5833, -6.5316,  ..., -6.6234, -6.4951, -6.5934],
        ...,
        [-6.5460, -6.5525, -6.5630,  ..., -6.5581, -6.5142, -6.5896],
        [-6.4957, -6.5235, -6.4879,  ..., -6.6145, -6.5316, -6.6193],
        [-6.5091, -6.5749, -6.5799,  ..., -6.5857, -6.5860, -6.6161]],
       device='cuda:0', grad_fn=<LogSoftmaxBackward>)
*********** tensor([[nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan],
        ...,
        [nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan]], device='cuda:0',
       grad_fn=<LogSoftmaxBackward>)
*********** tensor([[nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan],
        ...,
        [nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan]], device='cuda:0',
       grad_fn=<LogSoftmaxBackward>)

for 1st file its showing the tensor but after its nan. very weird behaviour. My dataset contain 10 audio files of 10sec length for each speaker. please help!

[008karan]

When increased the chunking size from 200ms to 600ms training started correctly. Below 600ms its giving nan and above 700ms range becoming zero in accessing random chunk as snt_len become less than wlen . Can you tell why so @mravanelli

[mravanelli]

It looks like a gradient issue. Sometimes one can solve them just by adding gradient clipping.

[AnnaWang288]

I encountered the same problem, how did you solve it in the end?

[natank1]

I encountered this . See https://github.com/mravanelli/SincNet/issues/102