lstm validation loss not decreasing

@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. hidden units). 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. 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. ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. Designing a better optimizer is very much an active area of research. But the validation loss starts with very small . Validation loss is not decreasing - Data Science Stack Exchange Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This will avoid gradient issues for saturated sigmoids, at the output. The funny thing is that they're half right: coding, It is really nice answer. See: In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. For example, suppose we are building a classifier to classify 6 and 9, and we use random rotation augmentation Why can't scikit-learn SVM solve two concentric circles? It also hedges against mistakenly repeating the same dead-end experiment. Where $a$ is your learning rate, $t$ is your iteration number and $m$ is a coefficient that identifies learning rate decreasing speed. 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 Any time you're writing code, you need to verify that it works as intended. 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. Accuracy on training dataset was always okay. Please help me. We've added a "Necessary cookies only" option to the cookie consent popup. LSTM training loss does not decrease - nlp - PyTorch Forums . To make sure the existing knowledge is not lost, reduce the set learning rate. Since either on its own is very useful, understanding how to use both is an active area of research. pixel values are in [0,1] instead of [0, 255]). 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). What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? 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. You need to test all of the steps that produce or transform data and feed into the network. 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. No change in accuracy using Adam Optimizer when SGD works fine. Should I put my dog down to help the homeless? 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. See this Meta thread for a discussion: What's the best way to answer "my neural network doesn't work, please fix" questions? To set the gradient threshold, use the 'GradientThreshold' option in trainingOptions. I regret that I left it out of my answer. If you haven't done so, you may consider to work with some benchmark dataset like SQuAD Try to adjust the parameters $\mathbf W$ and $\mathbf b$ to minimize this loss function. The reason is that for DNNs, we usually deal with gigantic data sets, several orders of magnitude larger than what we're used to, when we fit more standard nonlinear parametric statistical models (NNs belong to this family, in theory). The cross-validation loss tracks the training loss. Why this happening and how can I fix it? 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. Your learning could be to big after the 25th epoch. One way for implementing curriculum learning is to rank the training examples by difficulty. I worked on this in my free time, between grad school and my job. Making statements based on opinion; back them up with references or personal experience. Large non-decreasing LSTM training loss - PyTorch Forums These bugs might even be the insidious kind for which the network will train, but get stuck at a sub-optimal solution, or the resulting network does not have the desired architecture. Then you can take a look at your hidden-state outputs after every step and make sure they are actually different. How to tell which packages are held back due to phased updates. Is it correct to use "the" before "materials used in making buildings are"? How can I fix this? 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. I am getting different values for the loss function per epoch. Does not being able to overfit a single training sample mean that the neural network architecure or implementation is wrong? 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). 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. 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. Problem is I do not understand what's going on here. Just at the end adjust the training and the validation size to get the best result in the test set. Replacing broken pins/legs on a DIP IC package. 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 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. Or the other way around? My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? 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. And these elements may completely destroy the data. Shuffling the labels independently from the samples (for instance, creating train/test splits for the labels and samples separately); Accidentally assigning the training data as the testing data; When using a train/test split, the model references the original, non-split data instead of the training partition or the testing partition. My model look like this: And here is the function for each training sample. This is easily the worse part of NN training, but these are gigantic, non-identifiable models whose parameters are fit by solving a non-convex optimization, so these iterations often can't be avoided. 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. 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. The best answers are voted up and rise to the top, Not the answer you're looking for? There's a saying among writers that "All writing is re-writing" -- that is, the greater part of writing is revising. Do they first resize and then normalize the image? The suggestions for randomization tests are really great ways to get at bugged networks. Other people insist that scheduling is essential. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? MathJax reference. It only takes a minute to sign up. You just need to set up a smaller value for your learning rate. Specifically for triplet-loss models, there are a number of tricks which can improve training time and generalization. Basically, the idea is to calculate the derivative by defining two points with a $\epsilon$ interval. Towards a Theoretical Understanding of Batch Normalization, How Does Batch Normalization Help Optimization? The first step when dealing with overfitting is to decrease the complexity of the model. If decreasing the learning rate does not help, then try using gradient clipping. Is it possible to rotate a window 90 degrees if it has the same length and width? Do I need a thermal expansion tank if I already have a pressure tank? Just want to add on one technique haven't been discussed yet. This is an easier task, so the model learns a good initialization before training on the real task. As the OP was using Keras, another option to make slightly more sophisticated learning rate updates would be to use a callback like. "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks" by Jinghui Chen, Quanquan Gu. Hence validation accuracy also stays at same level but training accuracy goes up. 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! 'Jupyter notebook' and 'unit testing' are anti-correlated. (No, It Is Not About Internal Covariate Shift). 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. Why does $[0,1]$ scaling dramatically increase training time for feed forward ANN (1 hidden layer)? "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. If the model isn't learning, there is a decent chance that your backpropagation is not working. The Marginal Value of Adaptive Gradient Methods in Machine Learning, Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. remove regularization gradually (maybe switch batch norm for a few layers). There are two tests which I call Golden Tests, which are very useful to find issues in a NN which doesn't train: reduce the training set to 1 or 2 samples, and train on this. ncdu: What's going on with this second size column? Can I tell police to wait and call a lawyer when served with a search warrant? Thanks for contributing an answer to Stack Overflow! The best answers are voted up and rise to the top, Not the answer you're looking for? . Even for simple, feed-forward networks, the onus is largely on the user to make numerous decisions about how the network is configured, connected, initialized and optimized. Why is this the case? Thank you itdxer. Learn more about Stack Overflow the company, and our products. Here's an example of a question where the problem appears to be one of model configuration or hyperparameter choice, but actually the problem was a subtle bug in how gradients were computed. Training loss goes up and down regularly. We hypothesize that Calculating probabilities from d6 dice pool (Degenesis rules for botches and triggers), Minimising the environmental effects of my dyson brain. Finally, I append as comments all of the per-epoch losses for training and validation. Can archive.org's Wayback Machine ignore some query terms? Then, if you achieve a decent performance on these models (better than random guessing), you can start tuning a neural network (and @Sycorax 's answer will solve most issues). What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? 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 your neural network does not generalize well, see: What should I do when my neural network doesn't generalize well? padding them with data to make them equal length), the LSTM is correctly ignoring your masked data. The comparison between the training loss and validation loss curve guides you, of course, but don't underestimate the die hard attitude of NNs (and especially DNNs): they often show a (maybe slowly) decreasing training/validation loss even when you have crippling bugs in your code. Is there a solution if you can't find more data, or is an RNN just the wrong model? Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary. Then, let $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$ be a loss function. train the neural network, while at the same time controlling the loss on the validation set. Is it possible to create a concave light? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. 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. Instead, I do that in a configuration file (e.g., JSON) that is read and used to populate network configuration details at runtime. Additionally, the validation loss is measured after each epoch. This looks like a typical of scenario of overfitting: in this case your RNN is memorizing the correct answers, instead of understanding the semantics and the logic to choose the correct answers. So this would tell you if your initialization is bad. (LSTM) models you are looking at data that is adjusted according to the data . +1, but "bloody Jupyter Notebook"? Does Counterspell prevent from any further spells being cast on a given turn? For example a Naive Bayes classifier for classification (or even just classifying always the most common class), or an ARIMA model for time series forecasting. How can change in cost function be positive? To learn more, see our tips on writing great answers. How do I reduce my validation loss? | ResearchGate How do you ensure that a red herring doesn't violate Chekhov's gun? \alpha(t + 1) = \frac{\alpha(0)}{1 + \frac{t}{m}} An application of this is to make sure that when you're masking your sequences (i.e. Is it possible to create a concave light? Convolutional neural networks can achieve impressive results on "structured" data sources, image or audio data. This problem is easy to identify. Switch the LSTM to return predictions at each step (in keras, this is return_sequences=True). The main point is that the error rate will be lower in some point in time. split data in training/validation/test set, or in multiple folds if using cross-validation. If you want to write a full answer I shall accept it. Why is it hard to train deep neural networks? Even when a neural network code executes without raising an exception, the network can still have bugs! For example, let $\alpha(\cdot)$ represent an arbitrary activation function, such that $f(\mathbf x) = \alpha(\mathbf W \mathbf x + \mathbf b)$ represents a classic fully-connected layer, where $\mathbf x \in \mathbb R^d$ and $\mathbf W \in \mathbb R^{k \times d}$. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. I couldn't obtained a good validation loss as my training loss was decreasing. This means that if you have 1000 classes, you should reach an accuracy of 0.1%. Wide and deep neural networks, and neural networks with exotic wiring, are the Hot Thing right now in machine learning. Thanks @Roni. How to match a specific column position till the end of line? Thanks a bunch for your insight! Other explanations might be that this is because your network does not have enough trainable parameters to overfit, coupled with a relatively large number of training examples (and of course, generating the training and the validation examples with the same process). If you preorder a special airline meal (e.g. I think I might have misunderstood something here, what do you mean exactly by "the network is not presented with the same examples over and over"? The order in which the training set is fed to the net during training may have an effect. 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. $\begingroup$ 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. For an example of such an approach you can have a look at my experiment. Training and Validation Loss in Deep Learning - Baeldung Comprehensive list of activation functions in neural networks with pros/cons, "Deep Residual Learning for Image Recognition", Identity Mappings in Deep Residual Networks. If it can't learn a single point, then your network structure probably can't represent the input -> output function and needs to be redesigned. My dataset contains about 1000+ examples. This is because your model should start out close to randomly guessing. Neural networks in particular are extremely sensitive to small changes in your data. Build unit tests. Dropout is used during testing, instead of only being used for training. 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. "FaceNet: A Unified Embedding for Face Recognition and Clustering" Florian Schroff, Dmitry Kalenichenko, James Philbin. +1 Learning like children, starting with simple examples, not being given everything at once! Short story taking place on a toroidal planet or moon involving flying. How do you ensure that a red herring doesn't violate Chekhov's gun? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Then I add each regularization piece back, and verify that each of those works along the way. So I suspect, there's something going on with the model that I don't understand. For example you could try dropout of 0.5 and so on. Why are physically impossible and logically impossible concepts considered separate in terms of probability? You can study this further by making your model predict on a few thousand examples, and then histogramming the outputs. try different optimizers: SGD trains slower, but it leads to a lower generalization error, while Adam trains faster, but the test loss stalls to a higher value, increase the learning rate initially, and then decay it, or use. number of hidden units, LSTM or GRU) the training loss decreases, but the validation loss stays quite high (I use dropout, the rate I use is 0.5), e.g. Lots of good advice there. Minimising the environmental effects of my dyson brain. See: Comprehensive list of activation functions in neural networks with pros/cons. How to Diagnose Overfitting and Underfitting of LSTM Models You can easily (and quickly) query internal model layers and see if you've setup your graph correctly. Validation loss is neither increasing or decreasing Sometimes, networks simply won't reduce the loss if the data isn't scaled. This is achieved by including in the training phase simultaneously (i) physical dependencies between. Some examples are. What to do if training loss decreases but validation loss does not Some common mistakes here are. Learn more about Stack Overflow the company, and our products. Linear Algebra - Linear transformation question. Ok, rereading your code I can obviously see that you are correct; I will edit my answer. I'm asking about how to solve the problem where my network's performance doesn't improve on the training set. Did you need to set anything else? keras lstm loss-function accuracy Share Improve this question What is the purpose of this D-shaped ring at the base of the tongue on my hiking boots? 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.). (The author is also inconsistent about using single- or double-quotes but that's purely stylistic. (+1) Checking the initial loss is a great suggestion. 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. I knew a good part of this stuff, what stood out for me is. I'll let you decide. How do you ensure that a red herring doesn't violate Chekhov's gun? : Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones. What degree of difference does validation and training loss need to have to be called good fit? Connect and share knowledge within a single location that is structured and easy to search. What image preprocessing routines do they use? Is it correct to use "the" before "materials used in making buildings are"? Connect and share knowledge within a single location that is structured and easy to search. Dealing with such a Model: Data Preprocessing: Standardizing and Normalizing the data. Making statements based on opinion; back them up with references or personal experience. What's the best way to answer "my neural network doesn't work, please fix" questions? The best answers are voted up and rise to the top, Not the answer you're looking for? here is my lstm NN source code of python: def lstm_rls (num_in,num_out=1, batch_size=128, step=1,dim=1): model = Sequential () model.add (LSTM ( 1024, input_shape= (step, num_in), return_sequences=True)) model.add (Dropout (0.2)) model.add (LSTM . Check the accuracy on the test set, and make some diagnostic plots/tables. Decrease the initial learning rate using the 'InitialLearnRate' option of trainingOptions. This is a good addition. Making statements based on opinion; back them up with references or personal experience. I'm not asking about overfitting or regularization. The difference between the phonemes /p/ and /b/ in Japanese, Short story taking place on a toroidal planet or moon involving flying. 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. What am I doing wrong here in the PlotLegends specification? 12 that validation loss and test loss keep decreasing when the training rounds are before 30 times. Also, when it comes to explaining your model, someone will come along and ask "what's the effect of $x_k$ on the result?" Loss is still decreasing at the end of training. (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. Thanks. 6 Answers Sorted by: 36 The model is overfitting right from epoch 10, the validation loss is increasing while the training loss is decreasing. If so, how close was it? 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. The posted answers are great, and I wanted to add a few "Sanity Checks" which have greatly helped me in the past. This will help you make sure that your model structure is correct and that there are no extraneous issues. This can be done by setting the validation_split argument on fit () to use a portion of the training data as a validation dataset. and all you will be able to do is shrug your shoulders. read data from some source (the Internet, a database, a set of local files, etc. Scaling the testing data using the statistics of the test partition instead of the train partition; Forgetting to un-scale the predictions (e.g. ncdu: What's going on with this second size column? These data sets are well-tested: if your training loss goes down here but not on your original data set, you may have issues in the data set. 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 performance per each epoch. What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? What is the essential difference between neural network and linear regression. What should I do when my neural network doesn't generalize well? 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. 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. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Pytorch. Have a look at a few input samples, and the associated labels, and make sure they make sense. Although it can easily overfit to a single image, it can't fit to a large dataset, despite good normalization and shuffling. I agree with this answer. 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. Why do many companies reject expired SSL certificates as bugs in bug bounties? Using indicator constraint with two variables. How to react to a students panic attack in an oral exam? The objective function of a neural network is only convex when there are no hidden units, all activations are linear, and the design matrix is full-rank -- because this configuration is identically an ordinary regression problem. 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.