Why training accuracy decreases as epoch increases?
The training (epoch) is organized with batches of data, so that optimization function is calculated within subset of whole dataset. The console output shows the accuracy of the full dataset, so the optimization of a single batch can decrease the accuracy of the other part of the dataset and decrease the global result.
How do I reduce Overfitting in LSTM model?
Dropout Layers can be an easy and effective way to prevent overfitting in your models. A dropout layer randomly drops some of the connections between layers. This helps to prevent overfitting, because if a connection is dropped, the network is forced to Luckily, with keras it’s really easy to add a dropout layer.
How is the persistence forecast used in LSTM?
Models will be developed using the training dataset and will make predictions on the test dataset. The persistence forecast (naive forecast) on the test dataset achieves an error of 136.761 monthly shampoo sales. This provides an acceptable lower bound of performance on the test set.
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.
Why does the loss / accuracy fluctuate during the training?
For batch_size=2 the LSTM did not seem to learn properly (loss fluctuates around the same value and does not decrease). Upd. 4: To see if the problem is not just a bug in the code: I have made an artificial example (2 classes that are not difficult to classify: cos vs arccos). Loss and accuracy during the training for these examples:
How can I test my LSTM without validation?
You would agree to test your data: first compute the Bayes error rate using a KNN (use the trick regression in case you need), in this way you can check whether the input data contain all the information you need. Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary.