lstm validation loss not decreasing

Neglecting to do this (and the use of the bloody Jupyter Notebook) are usually the root causes of issues in NN code I'm asked to review, especially when the model is supposed to be deployed in production. Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. Training accuracy is ~97% but validation accuracy is stuck at ~40%. Is it possible to rotate a window 90 degrees if it has the same length and width? Theoretically Correct vs Practical Notation, Replacing broken pins/legs on a DIP IC package, Partner is not responding when their writing is needed in European project application. What could cause my neural network model's loss increases dramatically? 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. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Sometimes, networks simply won't reduce the loss if the data isn't scaled. (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. Has 90% of ice around Antarctica disappeared in less than a decade? For example $-0.3\ln(0.99)-0.7\ln(0.01) = 3.2$, so if you're seeing a loss that's bigger than 1, it's likely your model is very skewed. $\begingroup$ 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. thanks, I will try increasing my training set size, I was actually trying to reduce the number of hidden units but to no avail, thanks for pointing out! By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. You can easily (and quickly) query internal model layers and see if you've setup your graph correctly. When resizing an image, what interpolation do they use? ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. You need to test all of the steps that produce or transform data and feed into the network. If this works, train it on two inputs with different outputs. So I suspect, there's something going on with the model that I don't understand. 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). But why is it better? (For example, the code may seem to work when it's not correctly implemented. I'm asking about how to solve the problem where my network's performance doesn't improve on the training set. 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. If so, how close was it? The Marginal Value of Adaptive Gradient Methods in Machine Learning, Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. 1) Train your model on a single data point. I like to start with exploratory data analysis to get a sense of "what the data wants to tell me" before getting into the models. For example, it's widely observed that layer normalization and dropout are difficult to use together. 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). Instead of training for a fixed number of epochs, you stop as soon as the validation loss rises because, after that, your model will generally only get worse . The first step when dealing with overfitting is to decrease the complexity of the model. And these elements may completely destroy the data. I reduced the batch size from 500 to 50 (just trial and error). The NN should immediately overfit the training set, reaching an accuracy of 100% on the training set very quickly, while the accuracy on the validation/test set will go to 0%. 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. Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? It took about a year, and I iterated over about 150 different models before getting to a model that did what I wanted: generate new English-language text that (sort of) makes sense. Problem is I do not understand what's going on here. as a particular form of continuation method (a general strategy for global optimization of non-convex functions). As you commented, this in not the case here, you generate the data only once. Other people insist that scheduling is essential. I regret that I left it out of my answer. Short story taking place on a toroidal planet or moon involving flying. I am training an LSTM to give counts of the number of items in buckets. Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? Decrease the initial learning rate using the 'InitialLearnRate' option of trainingOptions. Finally, I append as comments all of the per-epoch losses for training and validation. Then you can take a look at your hidden-state outputs after every step and make sure they are actually different. ", As an example, I wanted to learn about LSTM language models, so I decided to make a Twitter bot that writes new tweets in response to other Twitter users. so given an explanation/context and a question, it is supposed to predict the correct answer out of 4 options. I struggled for a while with such a model, and when I tried a simpler version, I found out that one of the layers wasn't being masked properly due to a keras bug. $f(\mathbf x) = \alpha(\mathbf W \mathbf x + \mathbf b)$, $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$, $\mathbf y = \begin{bmatrix}1 & 0 & 0 & \cdots & 0\end{bmatrix}$. the opposite test: you keep the full training set, but you shuffle the labels. Can archive.org's Wayback Machine ignore some query terms? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Why do many companies reject expired SSL certificates as bugs in bug bounties? The essential idea of curriculum learning is best described in the abstract of the previously linked paper by Bengio et al. Be advised that validation, as it is calculated at the end of each epoch, uses the "best" machine trained in that epoch (that is, the last one, but if constant improvement is the case then the last weights should yield the best results - at least for training loss, if not for validation), while the train loss is calculated as an average of the . Replacing broken pins/legs on a DIP IC package. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". Make sure you're minimizing the loss function, Make sure your loss is computed correctly. The suggestions for randomization tests are really great ways to get at bugged networks. Why are physically impossible and logically impossible concepts considered separate in terms of probability? Styling contours by colour and by line thickness in QGIS. Not the answer you're looking for? The safest way of standardizing packages is to use a requirements.txt file that outlines all your packages just like on your training system setup, down to the keras==2.1.5 version numbers. Otherwise, you might as well be re-arranging deck chairs on the RMS Titanic. I just copied the code above (fixed the scaler bug) and reran it on CPU. Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? "The Marginal Value of Adaptive Gradient Methods in Machine Learning" by Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, Benjamin Recht, But on the other hand, this very recent paper proposes a new adaptive learning-rate optimizer which supposedly closes the gap between adaptive-rate methods and SGD with momentum. I had this issue - while training loss was decreasing, the validation loss was not decreasing. Care to comment on that? Any advice on what to do, or what is wrong? Instead, make a batch of fake data (same shape), and break your model down into components. Residual connections can improve deep feed-forward networks. Using indicator constraint with two variables. The reason that I'm so obsessive about retaining old results is that this makes it very easy to go back and review previous experiments. Other networks will decrease the loss, but only very slowly. Why does Mister Mxyzptlk need to have a weakness in the comics? 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. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. I have prepared the easier set, selecting cases where differences between categories were seen by my own perception as more obvious. Even when a neural network code executes without raising an exception, the network can still have bugs! You just need to set up a smaller value for your learning rate. Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. remove regularization gradually (maybe switch batch norm for a few layers). This is actually a more readily actionable list for day to day training than the accepted answer - which tends towards steps that would be needed when doing more serious attention to a more complicated network. If you re-train your RNN on this fake dataset and achieve similar performance as on the real dataset, then we can say that your RNN is memorizing. How to match a specific column position till the end of line? Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? Curriculum learning is a formalization of @h22's answer. Solutions to this are to decrease your network size, or to increase dropout. How to match a specific column position till the end of line? Ive seen a number of NN posts where OP left a comment like oh I found a bug now it works.. "FaceNet: A Unified Embedding for Face Recognition and Clustering" Florian Schroff, Dmitry Kalenichenko, James Philbin. The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup, The model of LSTM with more than one unit. 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. Aren't my iterations needed to train NN for XOR with MSE < 0.001 too high?