lstm validation loss not decreasing lstm validation loss not decreasing

To subscribe to this RSS feed, copy and paste this URL into your RSS reader. To verify my implementation of the model and understand keras, I'm using a toyproblem to make sure I understand what's going on. 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. This is a good addition. . keras lstm loss-function accuracy Share Improve this question This paper introduces a physics-informed machine learning approach for pathloss prediction. Finally, the best way to check if you have training set issues is to use another training set. Towards a Theoretical Understanding of Batch Normalization, How Does Batch Normalization Help Optimization? For example you could try dropout of 0.5 and so on. Styling contours by colour and by line thickness in QGIS. 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. Ive seen a number of NN posts where OP left a comment like oh I found a bug now it works.. (See: What is the essential difference between neural network and linear regression), Classical neural network results focused on sigmoidal activation functions (logistic or $\tanh$ functions). Trying to understand how to get this basic Fourier Series, Linear Algebra - Linear transformation question. All the answers are great, but there is one point which ought to be mentioned : is there anything to learn from your data ? Can I tell police to wait and call a lawyer when served with a search warrant? Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift, Adjusting for Dropout Variance in Batch Normalization and Weight Initialization, there exists a library which supports unit tests development for NN, We've added a "Necessary cookies only" option to the cookie consent popup. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? 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). You've decided that the best approach to solve your problem is to use a CNN combined with a bounding box detector, that further processes image crops and then uses an LSTM to combine everything. Try to adjust the parameters $\mathbf W$ and $\mathbf b$ to minimize this loss function. Choosing a clever network wiring can do a lot of the work for you. Continuing the binary example, if your data is 30% 0's and 70% 1's, then your intial expected loss around $L=-0.3\ln(0.5)-0.7\ln(0.5)\approx 0.7$. oytungunes Asks: Validation Loss does not decrease in LSTM? This will avoid gradient issues for saturated sigmoids, at the output. It's interesting how many of your comments are similar to comments I have made (or have seen others make) in relation to debugging estimation of parameters or predictions for complex models with MCMC sampling schemes. The second part makes sense to me, however in the first part you say, I am creating examples de novo, but I am only generating the data once. @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. 2 Usually when a model overfits, validation loss goes up and training loss goes down from the point of overfitting. How to Diagnose Overfitting and Underfitting of LSTM Models; Overfitting and Underfitting With Machine Learning Algorithms; Articles. Multi-layer perceptron vs deep neural network, My neural network can't even learn Euclidean distance. 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. (LSTM) models you are looking at data that is adjusted according to the data . In my case the initial training set was probably too difficult for the network, so it was not making any progress. 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. (No, It Is Not About Internal Covariate Shift). 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. I followed a few blog posts and PyTorch portal to implement variable length input sequencing with pack_padded and pad_packed sequence which appears to work well. When resizing an image, what interpolation do they use? You can study this further by making your model predict on a few thousand examples, and then histogramming the outputs. One caution about ReLUs is the "dead neuron" phenomenon, which can stymie learning; leaky relus and similar variants avoid this problem. 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. This means writing code, and writing code means debugging. A standard neural network is composed of layers. Accuracy (0-1 loss) is a crappy metric if you have strong class imbalance. I get NaN values for train/val loss and therefore 0.0% accuracy. (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. Scaling the inputs (and certain times, the targets) can dramatically improve the network's training. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. How to match a specific column position till the end of line? Of course, this can be cumbersome. I provide an example of this in the context of the XOR problem here: Aren't my iterations needed to train NN for XOR with MSE < 0.001 too high?. Sometimes, networks simply won't reduce the loss if the data isn't scaled. How can I fix this? Connect and share knowledge within a single location that is structured and easy to search. I think Sycorax and Alex both provide very good comprehensive answers. If nothing helped, it's now the time to start fiddling with hyperparameters. Some examples are. (The author is also inconsistent about using single- or double-quotes but that's purely stylistic. After it reached really good results, it was then able to progress further by training from the original, more complex data set without blundering around with training score close to zero. What am I doing wrong here in the PlotLegends specification? Short story taking place on a toroidal planet or moon involving flying. From this I calculate 2 cosine similarities, one for the correct answer and one for the wrong answer, and define my loss to be a hinge loss, i.e. You just need to set up a smaller value for your learning rate. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Also it makes debugging a nightmare: you got a validation score during training, and then later on you use a different loader and get different accuracy on the same darn dataset. so given an explanation/context and a question, it is supposed to predict the correct answer out of 4 options. The lstm_size can be adjusted . I'm asking about how to solve the problem where my network's performance doesn't improve on the training set. $\endgroup$ What's the channel order for RGB images? Asking for help, clarification, or responding to other answers. Large non-decreasing LSTM training loss. Why is this sentence from The Great Gatsby grammatical? Decrease the initial learning rate using the 'InitialLearnRate' option of trainingOptions. What to do if training loss decreases but validation loss does not decrease? I added more features, which I thought intuitively would add some new intelligent information to the X->y pair. If I run your code (unchanged - on a GPU), then the model doesn't seem to train. "FaceNet: A Unified Embedding for Face Recognition and Clustering" Florian Schroff, Dmitry Kalenichenko, James Philbin. Just as it is not sufficient to have a single tumbler in the right place, neither is it sufficient to have only the architecture, or only the optimizer, set up correctly. If this doesn't happen, there's a bug in your code. Any time you're writing code, you need to verify that it works as intended. It also hedges against mistakenly repeating the same dead-end experiment. Instead, start calibrating a linear regression, a random forest (or any method you like whose number of hyperparameters is low, and whose behavior you can understand). I edited my original post to accomodate your input and some information about my loss/acc values. Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? This will help you make sure that your model structure is correct and that there are no extraneous issues. (Keras, LSTM), Changing the training/test split between epochs in neural net models, when doing hyperparameter optimization, Validation accuracy/loss goes up and down linearly with every consecutive epoch. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Writing good unit tests is a key piece of becoming a good statistician/data scientist/machine learning expert/neural network practitioner. Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. For me, the validation loss also never decreases. The suggestions for randomization tests are really great ways to get at bugged networks. Making statements based on opinion; back them up with references or personal experience. This problem is easy to identify. In particular, you should reach the random chance loss on the test set. Then make dummy models in place of each component (your "CNN" could just be a single 2x2 20-stride convolution, the LSTM with just 2 normalize or standardize the data in some way. Does a summoned creature play immediately after being summoned by a ready action? Then you can take a look at your hidden-state outputs after every step and make sure they are actually different. I used to think that this was a set-and-forget parameter, typically at 1.0, but I found that I could make an LSTM language model dramatically better by setting it to 0.25. Neural networks are not "off-the-shelf" algorithms in the way that random forest or logistic regression are. How can change in cost function be positive? Any advice on what to do, or what is wrong? 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. The best answers are voted up and rise to the top, Not the answer you're looking for? Learn more about Stack Overflow the company, and our products. How to react to a students panic attack in an oral exam? Can I add data, that my neural network classified, to the training set, in order to improve it? MathJax reference. If decreasing the learning rate does not help, then try using gradient clipping. Asking for help, clarification, or responding to other answers. $\begingroup$ 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. Why are physically impossible and logically impossible concepts considered separate in terms of probability? The asker was looking for "neural network doesn't learn" so I majored there. The best answers are voted up and rise to the top, Not the answer you're looking for? Is there a solution if you can't find more data, or is an RNN just the wrong model? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Deep learning is all the rage these days, and networks with a large number of layers have shown impressive results. 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. Additionally, the validation loss is measured after each epoch. There are two features of neural networks that make verification even more important than for other types of machine learning or statistical models. rev2023.3.3.43278. Thanks for contributing an answer to Stack Overflow! Hey there, I'm just curious as to why this is so common with RNNs. How to match a specific column position till the end of line? Loss was constant 4.000 and accuracy 0.142 on 7 target values dataset. Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? Replacing broken pins/legs on a DIP IC package. This can be done by comparing the segment output to what you know to be the correct answer. Does Counterspell prevent from any further spells being cast on a given turn? The differences are usually really small, but you'll occasionally see drops in model performance due to this kind of stuff. Why do many companies reject expired SSL certificates as bugs in bug bounties? Try to set up it smaller and check your loss again. . Where does this (supposedly) Gibson quote come from? How to handle a hobby that makes income in US. \alpha(t + 1) = \frac{\alpha(0)}{1 + \frac{t}{m}} 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. import imblearn import mat73 import keras from keras.utils import np_utils import os. How to tell which packages are held back due to phased updates. Weight changes but performance remains the same. $L^2$ regularization (aka weight decay) or $L^1$ regularization is set too large, so the weights can't move. I don't know why that is. In all other cases, the optimization problem is non-convex, and non-convex optimization is hard. A place where magic is studied and practiced? Specifically for triplet-loss models, there are a number of tricks which can improve training time and generalization. +1 for "All coding is debugging". To set the gradient threshold, use the 'GradientThreshold' option in trainingOptions. As an example, imagine you're using an LSTM to make predictions from time-series data. I am runnning LSTM for classification task, and my validation loss does not decrease. The best answers are voted up and rise to the top, Not the answer you're looking for? If you can't find a simple, tested architecture which works in your case, think of a simple baseline. Before combining $f(\mathbf x)$ with several other layers, generate a random target vector $\mathbf y \in \mathbb R^k$. 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. Prior to presenting data to a neural network. Experiments on standard benchmarks show that Padam can maintain fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. I am writing a program that make use of the build in LSTM in the Pytorch, however the loss is always around some numbers and does not decrease significantly. And struggled for a long time that the model does not learn. What could cause this? When training triplet networks, training with online hard negative mining immediately risks model collapse, so people train with semi-hard negative mining first as a kind of "pre training." This is a non-exhaustive list of the configuration options which are not also regularization options or numerical optimization options. Now I'm working on it. Thus, if the machine is constantly improving and does not overfit, the gap between the network's average performance in an epoch and its performance at the end of an epoch is translated into the gap between training and validation scores - in favor of the validation scores. Thanks @Roni. MathJax reference. My model look like this: And here is the function for each training sample. Just at the end adjust the training and the validation size to get the best result in the test set. Scaling the testing data using the statistics of the test partition instead of the train partition; Forgetting to un-scale the predictions (e.g. Basically, the idea is to calculate the derivative by defining two points with a $\epsilon$ interval. Is it possible to create a concave light? learning rate) is more or less important than another (e.g. Then training proceed with online hard negative mining, and the model is better for it as a result. AFAIK, this triplet network strategy is first suggested in the FaceNet paper. the opposite test: you keep the full training set, but you shuffle the labels. I am training a LSTM model to do question answering, i.e. Do not train a neural network to start with! 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. Double check your input data. Styling contours by colour and by line thickness in QGIS. Connect and share knowledge within a single location that is structured and easy to search. In my case it's not a problem with the architecture (I'm implementing a Resnet from another paper). Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. How does the Adam method of stochastic gradient descent work? But there are so many things can go wrong with a black box model like Neural Network, there are many things you need to check. Training accuracy is ~97% but validation accuracy is stuck at ~40%. (This is an example of the difference between a syntactic and semantic error.). Especially if you plan on shipping the model to production, it'll make things a lot easier. Setting up a neural network configuration that actually learns is a lot like picking a lock: all of the pieces have to be lined up just right. Connect and share knowledge within a single location that is structured and easy to search. Before I was knowing that this is wrong, I did add Batch Normalisation layer after every learnable layer, and that helps. I checked and found while I was using LSTM: Thanks for contributing an answer to Data Science Stack Exchange! See: Comprehensive list of activation functions in neural networks with pros/cons. This can help make sure that inputs/outputs are properly normalized in each layer. as a particular form of continuation method (a general strategy for global optimization of non-convex functions). (For example, the code may seem to work when it's not correctly implemented. Testing on a single data point is a really great idea. We hypothesize that The best answers are voted up and rise to the top, Not the answer you're looking for? If the problem related to your learning rate than NN should reach a lower error despite that it will go up again after a while. Is it correct to use "the" before "materials used in making buildings are"? To achieve state of the art, or even merely good, results, you have to set up all of the parts configured to work well together. For cripes' sake, get a real IDE such as PyCharm or VisualStudio Code and create a well-structured code, rather than cooking up a Notebook! Why is this the case? Do new devs get fired if they can't solve a certain bug? I agree with your analysis. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. What are "volatile" learning curves indicative of? How to handle a hobby that makes income in US. . Have a look at a few input samples, and the associated labels, and make sure they make sense. The Marginal Value of Adaptive Gradient Methods in Machine Learning, Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. It might also be possible that you will see overfit if you invest more epochs into the training. 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.