Phil
07/31/2023, 4:44 PMnf.fit(df=Y_train_df, val_size=12)
The final error message is:
File ~/Documents/pyTorch/pytorch/lib/python3.9/site-packages/neuralforecast/common/_base_windows.py:460, in BaseWindows.training_step(self, batch, batch_idx)
458 print("insample_y", torch.isnan(insample_y).sum())
459 print("outsample_y", torch.isnan(outsample_y).sum())
--> 460 print("output", torch.isnan(output).sum())
461 raise Exception("Loss is NaN, training stopped.")
463 self.log("train_loss", loss, prog_bar=True, on_epoch=True)
TypeError: isnan(): argument 'input' (position 1) must be Tensor, not tuple
I am running this tutorial on a MacBook Pro 13.5 (22G74) with python version 3.9. Any idea what is going on?Mark
07/31/2023, 4:54 PMPhil
07/31/2023, 4:55 PM# Horizon and quantiles
level = np.arange(0, 100, 2)
qs = [[50-lv/2, 50+lv/2] if lv!=0 else [50] for lv in level]
quantiles = np.sort(np.concatenate(qs)/100)
# HINT := BaseNetwork + Distribution + Reconciliation
nhits = NHITS(h=horizon,
input_size=24,
loss=GMM(n_components=10, quantiles=quantiles),
hist_exog_list=['month'],
max_steps=2000,
early_stop_patience_steps=10,
val_check_steps=50,
scaler_type='robust',
learning_rate=1e-3,
valid_loss=sCRPS(quantiles=quantiles))
model = HINT(h=horizon, S=S_df.values,
model=nhits, reconciliation='BottomUp')
Mark
07/31/2023, 4:58 PMPhil
07/31/2023, 4:58 PMMark
07/31/2023, 5:00 PMPhil
07/31/2023, 5:00 PMMark
07/31/2023, 5:04 PMPhil
07/31/2023, 5:06 PMMark
07/31/2023, 5:06 PMPhil
07/31/2023, 5:08 PMMark
07/31/2023, 5:08 PMPhil
07/31/2023, 5:08 PMMark
07/31/2023, 5:29 PMPhil
07/31/2023, 5:29 PMMark
07/31/2023, 5:29 PMPhil
07/31/2023, 5:30 PM