lstm validation loss not decreasing

The best method I've ever found for verifying correctness is to break your code into small segments, and verify that each segment works. 'Jupyter notebook' and 'unit testing' are anti-correlated. What are "volatile" learning curves indicative of? If you haven't done so, you may consider to work with some benchmark dataset like SQuAD and i used keras framework to build the network, but it seems the NN can't be build up easily. $\begingroup$ As the OP was using Keras, another option to make slightly more sophisticated learning rate updates would be to use a callback like ReduceLROnPlateau, which reduces the learning rate once the validation loss hasn't improved for a given number of epochs. remove regularization gradually (maybe switch batch norm for a few layers). keras lstm loss-function accuracy Share Improve this question See if you inverted the training set and test set labels, for example (happened to me once -___-), or if you imported the wrong file. Training accuracy is ~97% but validation accuracy is stuck at ~40%. How to handle a hobby that makes income in US. Connect and share knowledge within a single location that is structured and easy to search. LSTM training loss does not decrease nlp sbhatt (Shreyansh Bhatt) October 7, 2019, 5:17pm #1 Hello, I have implemented a one layer LSTM network followed by a linear layer. normalize or standardize the data in some way. import imblearn import mat73 import keras from keras.utils import np_utils import os. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Connect and share knowledge within a single location that is structured and easy to search. Even if you can prove that there is, mathematically, only a small number of neurons necessary to model a problem, it is often the case that having "a few more" neurons makes it easier for the optimizer to find a "good" configuration. I tried using "adam" instead of "adadelta" and this solved the problem, though I'm guessing that reducing the learning rate of "adadelta" would probably have worked also. What is going on? Can archive.org's Wayback Machine ignore some query terms? :). And the loss in the training looks like this: Is there anything wrong with these codes? No change in accuracy using Adam Optimizer when SGD works fine. My model architecture is as follows (if not relevant please ignore): I pass the explanation (encoded) and question each through the same lstm to get a vector representation of the explanation/question and add these representations together to get a combined representation for the explanation and question. I am getting different values for the loss function per epoch. 2 Usually when a model overfits, validation loss goes up and training loss goes down from the point of overfitting. Or the other way around? Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? I just copied the code above (fixed the scaler bug) and reran it on CPU. It thus cannot overfit to accommodate them while losing the ability to respond correctly to the validation examples - which, after all, are generated by the same process as the training examples.

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