Contents
How do you get zero training error?
You can get zero training error by chance, with any model. Say your biased classifier always predicts zero, but your dataset happens to be all labeled zero. Zero training error is impossible in general, because of Bayes error (think: two points in your training data are identical except for the label).
Can validation loss be lower than training?
Reason #2: Training loss is measured during each epoch while validation loss is measured after each epoch. The second reason you may see validation loss lower than training loss is due to how the loss value are measured and reported: Training loss is measured during each epoch.
How to update LSTM networks during training for time series forecasting?
Transform the observations to have a specific scale. Specifically, to rescale the data to values between -1 and 1 to meet the default hyperbolic tangent activation function of the LSTM model. These transforms are inverted on forecasts to return them into their original scale before calculating an error score.
How are long time lags bridged with LSTMs?
Long time lags in certain problems are bridged using LSTMs where they also handle noise, distributed representations, and continuous values. With LSTMs, there is no need to keep a finite number of states from beforehand as required in the hidden Markov model (HMM).
What are the disadvantages of the LSTM network?
The article concludes with a list of disadvantages of the LSTM network and a brief introduction of the upcoming attention-based models that are swiftly replacing LSTMs in the real world. LSTM networks are an extension of recurrent neural networks (RNNs) mainly introduced to handle situations where RNNs fail.
How does LSTM solve the problem of vanishing and exploding gradients?
Note that the gradient equation involves a chain of for an LSTM Back-Propagation while the gradient equation involves a chain of for a basic Recurrent Neural Network. How does LSTM solve the problem of vanishing and exploding gradients?