Just trying one of the ECG examples and getting a runtime error. I'm running pytorch 1.5.
$ python 1_train_predictor.py --data ecg --filename chfdb_chf01_275.pkl --emsize 128 --nhid 128 --save_fig --epoch 600
=> Start training from scratch
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Namespace(augment=True, batch_size=64, bptt=50, clip=10, data='ecg', device='cuda', dropout=0.2, emsize=128, epochs=600, eval_batch_size=64, filename='chfdb_chf01_275.pkl', log_interval=10, lr=0.0002, model='LSTM', nhid=128, nlayers=2, prediction_window_size=10, pretrained=False, res_connection=False, resume=False, save_fig=True, save_interval=10, seed=1111, teacher_forcing_ratio=0.7, tied=False, weight_decay=0.0001)
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RuntimeError Traceback (most recent call last)
/home/marc/dev/RNN-Time-series-Anomaly-Detection/1_train_predictor.py in <module>
318
319 epoch_start_time = time.time()
--> 320 train(args,model,train_dataset,epoch)
321 val_loss = evaluate(args,model,test_dataset)
322 print('-' * 89)
/home/marc/dev/RNN-Time-series-Anomaly-Detection/1_train_predictor.py in train(args, model, train_dataset, epoch)
226 outSeq1 = torch.cat(outVals,dim=0)
227 hids1 = torch.cat(hids1,dim=0)
--> 228 loss1 = criterion(outSeq1.view(args.batch_size,-1), targetSeq.view(args.batch_size,-1))
229
230 '''Loss2: Teacher forcing loss'''
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
Just trying one of the ECG examples and getting a runtime error. I'm running pytorch 1.5.