Thanks for that. I use squeeze() on the model output because it was also used in the tutorial code so I just kept it for my regression. What additional problems would I encounter if I kept it in my code?
I’m also concerned that the reverse scaling isn’t doing its job - I expect the reverse-scaled values to be much bigger.
## reverse scaling of test data and model predictions
scaler0 = scaler.fit(np.expand_dims(y_train, axis=1))
ytrue = scaler0.inverse_transform(np.expand_dims(y_test.detach().numpy().flatten(),axis=0)).flatten()
ypred = scaler0.inverse_transform(np.expand_dims(y_test_pred.detach().numpy(),axis=0)).flatten()

A hyperparameter optimization library sounds useful because I don’t want to manually find the best learning rate, no. hidden layers, etc.