lstm validation loss not decreasing

Do new devs get fired if they can't solve a certain bug? The order in which the training set is fed to the net during training may have an effect. Recurrent neural networks can do well on sequential data types, such as natural language or time series data. Choosing and tuning network regularization is a key part of building a model that generalizes well (that is, a model that is not overfit to the training data). My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? I edited my original post to accomodate your input and some information about my loss/acc values. . I am wondering why validation loss of this regression problem is not decreasing while I have implemented several methods such as making the model simpler, adding early stopping, various learning rates, and also regularizers, but none of them have worked properly. (This is an example of the difference between a syntactic and semantic error.). The asker was looking for "neural network doesn't learn" so I majored there. Partner is not responding when their writing is needed in European project application, How do you get out of a corner when plotting yourself into a corner. Residual connections are a neat development that can make it easier to train neural networks. Thanks. (No, It Is Not About Internal Covariate Shift). Reasons why your Neural Network is not working, This is an example of the difference between a syntactic and semantic error, Loss functions are not measured on the correct scale. The best answers are voted up and rise to the top, Not the answer you're looking for? Likely a problem with the data? At its core, the basic workflow for training a NN/DNN model is more or less always the same: define the NN architecture (how many layers, which kind of layers, the connections among layers, the activation functions, etc.). Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Loss was constant 4.000 and accuracy 0.142 on 7 target values dataset. How to match a specific column position till the end of line? Why do many companies reject expired SSL certificates as bugs in bug bounties? it is shown in Fig. The main point is that the error rate will be lower in some point in time. 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 the Difference Between 'Man' And 'Son of Man' in Num 23:19? See: In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. What am I doing wrong here in the PlotLegends specification? Have a look at a few input samples, and the associated labels, and make sure they make sense. It only takes a minute to sign up. Is it possible to create a concave light? Check the accuracy on the test set, and make some diagnostic plots/tables. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. If it is indeed memorizing, the best practice is to collect a larger dataset. +1 Learning like children, starting with simple examples, not being given everything at once! By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? oytungunes Asks: Validation Loss does not decrease in LSTM? Here you can enjoy the soul-wrenching pleasures of non-convex optimization, where you don't know if any solution exists, if multiple solutions exist, which is the best solution(s) in terms of generalization error and how close you got to it. Usually I make these preliminary checks: look for a simple architecture which works well on your problem (for example, MobileNetV2 in the case of image classification) and apply a suitable initialization (at this level, random will usually do). The validation loss slightly increase such as from 0.016 to 0.018. Styling contours by colour and by line thickness in QGIS. history = model.fit(X, Y, epochs=100, validation_split=0.33) I had this issue - while training loss was decreasing, the validation loss was not decreasing. Dropout is used during testing, instead of only being used for training. I think Sycorax and Alex both provide very good comprehensive answers. It could be that the preprocessing steps (the padding) are creating input sequences that cannot be separated (perhaps you are getting a lot of zeros or something of that sort). It means that your step will minimise by a factor of two when $t$ is equal to $m$. If you haven't done so, you may consider to work with some benchmark dataset like SQuAD normalize or standardize the data in some way. Just want to add on one technique haven't been discussed yet. Care to comment on that? The lstm_size can be adjusted . I just tried increasing the number of training epochs to 50 (instead of 12) and the number of neurons per layer to 500 (instead of 100) and still couldn't get the model to overfit. Of course details will change based on the specific use case, but with this rough canvas in mind, we can think of what is more likely to go wrong. Note that it is not uncommon that when training a RNN, reducing model complexity (by hidden_size, number of layers or word embedding dimension) does not improve overfitting. (+1) Checking the initial loss is a great suggestion. Is it possible to create a concave light? How to handle a hobby that makes income in US. It can also catch buggy activations. pixel values are in [0,1] instead of [0, 255]). Why does $[0,1]$ scaling dramatically increase training time for feed forward ANN (1 hidden layer)? For me, the validation loss also never decreases. Accuracy on training dataset was always okay. The best answers are voted up and rise to the top, Not the answer you're looking for? Thank you for informing me regarding your experiment. If your training/validation loss are about equal then your model is underfitting. Hence validation accuracy also stays at same level but training accuracy goes up. I am training an LSTM to give counts of the number of items in buckets. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. The best answers are voted up and rise to the top, Not the answer you're looking for? I don't know why that is. Large non-decreasing LSTM training loss. Loss functions are not measured on the correct scale (for example, cross-entropy loss can be expressed in terms of probability or logits) The loss is not appropriate for the task (for example, using categorical cross-entropy loss for a regression task). Many of the different operations are not actually used because previous results are over-written with new variables. Learn more about Stack Overflow the company, and our products. Okay, so this explains why the validation score is not worse. Stack Exchange network consists of 181 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. No change in accuracy using Adam Optimizer when SGD works fine. Stack Exchange network consists of 181 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. However I'd still like to understand what's going on, as I see similar behavior of the loss in my real problem but there the predictions are rubbish. . Your learning rate could be to big after the 25th epoch. (But I don't think anyone fully understands why this is the case.) The funny thing is that they're half right: coding, It is really nice answer. An application of this is to make sure that when you're masking your sequences (i.e. Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? To verify my implementation of the model and understand keras, I'm using a toyproblem to make sure I understand what's going on. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. This will help you make sure that your model structure is correct and that there are no extraneous issues. Training loss goes up and down regularly. But some recent research has found that SGD with momentum can out-perform adaptive gradient methods for neural networks. Connect and share knowledge within a single location that is structured and easy to search. @Lafayette, alas, the link you posted to your experiment is broken, Understanding LSTM behaviour: Validation loss smaller than training loss throughout training for regression problem, How Intuit democratizes AI development across teams through reusability. Curriculum learning is a formalization of @h22's answer. I get NaN values for train/val loss and therefore 0.0% accuracy. First one is a simplest one. To learn more, see our tips on writing great answers. Some examples: When it first came out, the Adam optimizer generated a lot of interest. Scaling the inputs (and certain times, the targets) can dramatically improve the network's training. Then training proceed with online hard negative mining, and the model is better for it as a result. How to tell which packages are held back due to phased updates, How do you get out of a corner when plotting yourself into a corner. Even when a neural network code executes without raising an exception, the network can still have bugs! So this would tell you if your initialization is bad. Often the simpler forms of regression get overlooked. Other people insist that scheduling is essential. But for my case, training loss still goes down but validation loss stays at same level. How can I fix this? Keras also allows you to specify a separate validation dataset while fitting your model that can also be evaluated using the same loss and metrics. Then I realized that it is enough to put Batch Normalisation before that last ReLU activation layer only, to keep improving loss/accuracy during training. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? Why do many companies reject expired SSL certificates as bugs in bug bounties? If the label you are trying to predict is independent from your features, then it is likely that the training loss will have a hard time reducing. My recent lesson is trying to detect if an image contains some hidden information, by stenography tools. Activation value at output neuron equals 1, and the network doesn't learn anything, Moving from support vector machine to neural network (Back propagation), Training a Neural Network to specialize with Insufficient Data. The validation loss is similar to the training loss and is calculated from a sum of the errors for each example in the validation set. AFAIK, this triplet network strategy is first suggested in the FaceNet paper. Maybe in your example, you only care about the latest prediction, so your LSTM outputs a single value and not a sequence. Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, multi-variable linear regression with pytorch, PyTorch path generation with RNN - confusion with input, output, hidden and batch sizes, Pytorch GRU error RuntimeError : size mismatch, m1: [1600 x 3], m2: [50 x 20], CNN -> LSTM cascaded models to PyTorch Lightning. If you observed this behaviour you could use two simple solutions. I'm building a lstm model for regression on timeseries. 1) Train your model on a single data point. This problem is easy to identify. This tactic can pinpoint where some regularization might be poorly set. First, it quickly shows you that your model is able to learn by checking if your model can overfit your data. A lot of times you'll see an initial loss of something ridiculous, like 6.5. It only takes a minute to sign up. Hey there, I'm just curious as to why this is so common with RNNs. Not the answer you're looking for? If you don't see any difference between the training loss before and after shuffling labels, this means that your code is buggy (remember that we have already checked the labels of the training set in the step before). There are two features of neural networks that make verification even more important than for other types of machine learning or statistical models. Variables are created but never used (usually because of copy-paste errors); Expressions for gradient updates are incorrect; The loss is not appropriate for the task (for example, using categorical cross-entropy loss for a regression task). How to match a specific column position till the end of line? Styling contours by colour and by line thickness in QGIS. Especially if you plan on shipping the model to production, it'll make things a lot easier. We've added a "Necessary cookies only" option to the cookie consent popup. The best method I've ever found for verifying correctness is to break your code into small segments, and verify that each segment works. What's the difference between a power rail and a signal line? How to interpret intermitent decrease of loss? This can be a source of issues. 'Jupyter notebook' and 'unit testing' are anti-correlated. What should I do when my neural network doesn't learn? The best answers are voted up and rise to the top, Not the answer you're looking for? Is it possible to share more info and possibly some code? Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site. Before checking that the entire neural network can overfit on a training example, as the other answers suggest, it would be a good idea to first check that each layer, or group of layers, can overfit on specific targets. Making statements based on opinion; back them up with references or personal experience. What am I doing wrong here in the PlotLegends specification? Psychologically, it also lets you look back and observe "Well, the project might not be where I want it to be today, but I am making progress compared to where I was $k$ weeks ago. Use MathJax to format equations. The scale of the data can make an enormous difference on training. This is a very active area of research. I am trying to train a LSTM model, but the problem is that the loss and val_loss are decreasing from 12 and 5 to less than 0.01, but the training set acc = 0.024 and validation set acc = 0.0000e+00 and they remain constant during the training. Learn more about Stack Overflow the company, and our products. This means that if you have 1000 classes, you should reach an accuracy of 0.1%. As the OP was using Keras, another option to make slightly more sophisticated learning rate updates would be to use a callback like. Seeing as you do not generate the examples anew every time, it is reasonable to assume that you would reach overfit, given enough epochs, if it has enough trainable parameters. 6 Answers Sorted by: 36 The model is overfitting right from epoch 10, the validation loss is increasing while the training loss is decreasing. ncdu: What's going on with this second size column? Comprehensive list of activation functions in neural networks with pros/cons, "Deep Residual Learning for Image Recognition", Identity Mappings in Deep Residual Networks. Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? I teach a programming for data science course in python, and we actually do functions and unit testing on the first day, as primary concepts. However I don't get any sensible values for accuracy. See: Gradient clipping re-scales the norm of the gradient if it's above some threshold.