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How to Diagnose Overfitting and Underfitting of LSTM Models My activation function is linear and the optimizer is Rmsprop. In the graph below, I train for 400 epochs and I use a simple hold out validation set representing the last 10% of the training set, rather than a full cross validation at the moment, so it is not alarming that the validation loss is less than the training. Validation loss increases while validation accuracy is still ... - GitHub Keras stateful LSTM returns NaN for validation loss There are many other options as well to reduce overfitting, assuming you are using Keras, visit this link. lstm loss not decreasing pytorch If you want to prevent overfitting you can reduce the . What should I do when my neural network doesn't learn? LSTM categorical crossentropy validation accuracy remains constant in Lstm. There are 252 buckets. Training a Long Short-term Memory neural network in Keras to emulate a PID controller (this article) . The argument and default value of the compile () method is as follows. Why is my validation loss lower than my training loss? Time Series Analysis: KERAS LSTM Deep Learning - Part 2 LSTM training loss decrease, but the validation loss doesn't change! Training LSTM, loss not decreasing. The pattern looks like a sine wave with decreasing amplitude. def visualize_loss (history, title): loss = history . Why validation accuracy doesn't change in stateful LSTM Generally speaking that's a much bigger problem than having an accuracy of 0.37 (which of course is also a problem as it implies a model that does worse than a simple coin toss). How to use Learning Curves to Diagnose Machine Learning Model Performance Also consider a decay rate of 1e-6. LSTM stands for long short-term memory. Multi-Sequence LSTM-RNN Deep Learning and - ProQuest i trained model almost 8 times with different pretraied models and parameters but validation loss never decreased from 0.84 . Code, training, and validation graphs are below.