lstm validation loss not decreasing

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. This will avoid gradient issues for saturated sigmoids, at the output. One way for implementing curriculum learning is to rank the training examples by difficulty. 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%. 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. 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. No change in accuracy using Adam Optimizer when SGD works fine. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. 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. Tensorboard provides a useful way of visualizing your layer outputs. However, when I did replace ReLU with Linear activation (for regression), no Batch Normalisation was needed any more and model started to train significantly better. 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. This is called unit testing. I checked and found while I was using LSTM: I simplified the model - instead of 20 layers, I opted for 8 layers. What could cause this? In my case it's not a problem with the architecture (I'm implementing a Resnet from another paper). Nowadays, many frameworks have built in data pre-processing pipeline and augmentation. Alternatively, rather than generating a random target as we did above with $\mathbf y$, we could work backwards from the actual loss function to be used in training the entire neural network to determine a more realistic target. You need to test all of the steps that produce or transform data and feed into the network. +1, but "bloody Jupyter Notebook"? How can change in cost function be positive? Making statements based on opinion; back them up with references or personal experience. This is achieved by including in the training phase simultaneously (i) physical dependencies between. To make sure the existing knowledge is not lost, reduce the set learning rate. Learn more about Stack Overflow the company, and our products. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. This is a good addition. I understand that it might not be feasible, but very often data size is the key to success. I agree with your analysis. Curriculum learning is a formalization of @h22's answer. Loss is still decreasing at the end of training. my immediate suspect would be the learning rate, try reducing it by several orders of magnitude, you may want to try the default value 1e-3 a few more tweaks that may help you debug your code: - you don't have to initialize the hidden state, it's optional and LSTM will do it internally - calling optimizer.zero_grad () right before loss.backward . Since NNs are nonlinear models, normalizing the data can affect not only the numerical stability, but also the training time, and the NN outputs (a linear function such as normalization doesn't commute with a nonlinear hierarchical function). Some examples are. If nothing helped, it's now the time to start fiddling with hyperparameters. (One key sticking point, and part of the reason that it took so many attempts, is that it was not sufficient to simply get a low out-of-sample loss, since early low-loss models had managed to memorize the training data, so it was just reproducing germane blocks of text verbatim in reply to prompts -- it took some tweaking to make the model more spontaneous and still have low loss.). Might be an interesting experiment. The best answers are voted up and rise to the top, Not the answer you're looking for? Learning rate scheduling can decrease the learning rate over the course of training. The lstm_size can be adjusted . rev2023.3.3.43278. Hence validation accuracy also stays at same level but training accuracy goes up. For example you could try dropout of 0.5 and so on. This is because your model should start out close to randomly guessing. 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 . 3) Generalize your model outputs to debug. In all other cases, the optimization problem is non-convex, and non-convex optimization is hard. You can study this further by making your model predict on a few thousand examples, and then histogramming the outputs. Go back to point 1 because the results aren't good. 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 . However training as well as validation loss pretty much converge to zero, so I guess we can conclude that the problem is to easy because training and validation data are generated in exactly the same way. Many of the different operations are not actually used because previous results are over-written with new variables. Reiterate ad nauseam. The asker was looking for "neural network doesn't learn" so I majored there. The network picked this simplified case well. 2 Usually when a model overfits, validation loss goes up and training loss goes down from the point of overfitting. The main point is that the error rate will be lower in some point in time. Does Counterspell prevent from any further spells being cast on a given turn? Asking for help, clarification, or responding to other answers. 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. 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. The only way the NN can learn now is by memorising the training set, which means that the training loss will decrease very slowly, while the test loss will increase very quickly. Neural Network - Estimating Non-linear function, Poor recurrent neural network performance on sequential data. If so, how close was it? ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. here is my code and my outputs: Specifically for triplet-loss models, there are a number of tricks which can improve training time and generalization. Setting the learning rate too large will cause the optimization to diverge, because you will leap from one side of the "canyon" to the other. 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. 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. In my case, I constantly make silly mistakes of doing Dense(1,activation='softmax') vs Dense(1,activation='sigmoid') for binary predictions, and the first one gives garbage results. The funny thing is that they're half right: coding, It is really nice answer. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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. $$. Check that the normalized data are really normalized (have a look at their range). 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. Make sure you're minimizing the loss function, Make sure your loss is computed correctly. Otherwise, you might as well be re-arranging deck chairs on the RMS Titanic. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks. A similar phenomenon also arises in another context, with a different solution. 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. Why do many companies reject expired SSL certificates as bugs in bug bounties? This Medium post, "How to unit test machine learning code," by Chase Roberts discusses unit-testing for machine learning models in more detail. ), have a look at a few samples (to make sure the import has gone well) and perform data cleaning if/when needed. 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?. What could cause this? model.py . In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. This can be done by comparing the segment output to what you know to be the correct answer. The second one is to decrease your learning rate monotonically. 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. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Use MathJax to format equations. I edited my original post to accomodate your input and some information about my loss/acc values. Designing a better optimizer is very much an active area of research. As the most upvoted answer has already covered unit tests, I'll just add that there exists a library which supports unit tests development for NN (only in Tensorflow, unfortunately). . To learn more, see our tips on writing great answers. I have two stacked LSTMS as follows (on Keras): Train on 127803 samples, validate on 31951 samples. However I don't get any sensible values for accuracy. If a law is new but its interpretation is vague, can the courts directly ask the drafters the intent and official interpretation of their law? For instance, you can generate a fake dataset by using the same documents (or explanations you your word) and questions, but for half of the questions, label a wrong answer as correct. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. All of these topics are active areas of research. Edit: I added some output of an experiment: Training scores can be expected to be better than those of the validation when the machine you train can "adapt" to the specifics of the training examples while not successfully generalizing; the greater the adaption to the specifics of the training examples and the worse generalization, the bigger the gap between training and validation scores (in favor of the training scores). 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 is this the case? 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). 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. If you want to write a full answer I shall accept it. (For example, the code may seem to work when it's not correctly implemented. It is very weird. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? 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. 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. I added more features, which I thought intuitively would add some new intelligent information to the X->y pair. And struggled for a long time that the model does not learn. This tactic can pinpoint where some regularization might be poorly set. I never had to get here, but if you're using BatchNorm, you would expect approximately standard normal distributions. Here is a simple formula: $$ On the same dataset a simple averaged sentence embedding gets f1 of .75, while an LSTM is a flip of a coin. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? Choosing a good minibatch size can influence the learning process indirectly, since a larger mini-batch will tend to have a smaller variance (law-of-large-numbers) than a smaller mini-batch. . Do not train a neural network to start with! It become true that I was doing regression with ReLU last activation layer, which is obviously wrong. Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? 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. Wide and deep neural networks, and neural networks with exotic wiring, are the Hot Thing right now in machine learning. Fighting the good fight. Replacing broken pins/legs on a DIP IC package. Making statements based on opinion; back them up with references or personal experience. 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! import imblearn import mat73 import keras from keras.utils import np_utils import os. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. split data in training/validation/test set, or in multiple folds if using cross-validation. 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.). keras lstm loss-function accuracy Share Improve this question In particular, you should reach the random chance loss on the test set. 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. What should I do? 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). If so, how close was it? 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). 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 Please help me. Dropout is used during testing, instead of only being used for training. Linear Algebra - Linear transformation question. 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. What's the best way to answer "my neural network doesn't work, please fix" questions? 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. 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 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. rev2023.3.3.43278. What's the channel order for RGB images? LSTM Training loss decreases and increases, Sequence lengths in LSTM / BiLSTMs and overfitting, Why does the loss/accuracy fluctuate during the training? This question is intentionally general so that other questions about how to train a neural network can be closed as a duplicate of this one, with the attitude that "if you give a man a fish you feed him for a day, but if you teach a man to fish, you can feed him for the rest of his life." Is it possible to share more info and possibly some code? But how could extra training make the training data loss bigger? Asking for help, clarification, or responding to other answers. The training loss should now decrease, but the test loss may increase. $\endgroup$ First, it quickly shows you that your model is able to learn by checking if your model can overfit your data. That probably did fix wrong activation method. If you can't find a simple, tested architecture which works in your case, think of a simple baseline. I worked on this in my free time, between grad school and my job. Why do we use ReLU in neural networks and how do we use it? Using Kolmogorov complexity to measure difficulty of problems? Is it possible to rotate a window 90 degrees if it has the same length and width? 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. In the given base model, there are 2 hidden Layers, one with 128 and one with 64 neurons. Finally, I append as comments all of the per-epoch losses for training and validation. Training loss goes down and up again. I'm not asking about overfitting or regularization. Sometimes, networks simply won't reduce the loss if the data isn't scaled. 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. with two problems ("How do I get learning to continue after a certain epoch?" By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Try to adjust the parameters $\mathbf W$ and $\mathbf b$ to minimize this loss function. How Intuit democratizes AI development across teams through reusability. The problem I find is that the models, for various hyperparameters I try (e.g. curriculum learning has both an effect on the speed of convergence of the training process to a minimum and, in the case of non-convex criteria, on the quality of the local minima obtained: curriculum learning can be seen Thank you for informing me regarding your experiment. If the loss decreases consistently, then this check has passed. In one example, I use 2 answers, one correct answer and one wrong answer. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. See if you inverted the training set and test set labels, for example (happened to me once -___-), or if you imported the wrong file. I reduced the batch size from 500 to 50 (just trial and error). and all you will be able to do is shrug your shoulders. In my experience, trying to use scheduling is a lot like regex: it replaces one problem ("How do I get learning to continue after a certain epoch?") To learn more, see our tips on writing great answers. Have a look at a few input samples, and the associated labels, and make sure they make sense. If decreasing the learning rate does not help, then try using gradient clipping. Finally, the best way to check if you have training set issues is to use another training set. How can I fix this? The problem turns out to be the misunderstanding of the batch size and other features that defining an nn.LSTM. Give or take minor variations that result from the random process of sample generation (even if data is generated only once, but especially if it is generated anew for each epoch). First one is a simplest one. Making statements based on opinion; back them up with references or personal experience. 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. 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. Try something more meaningful such as cross-entropy loss: you don't just want to classify correctly, but you'd like to classify with high accuracy. Loss was constant 4.000 and accuracy 0.142 on 7 target values dataset. Comprehensive list of activation functions in neural networks with pros/cons, "Deep Residual Learning for Image Recognition", Identity Mappings in Deep Residual Networks. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? What is a word for the arcane equivalent of a monastery? Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? Care to comment on that? When my network doesn't learn, I turn off all regularization and verify that the non-regularized network works correctly. Just want to add on one technique haven't been discussed yet. As an example, two popular image loading packages are cv2 and PIL. How to match a specific column position till the end of line? 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. The best answers are voted up and rise to the top, Not the answer you're looking for? I'm training a neural network but the training loss doesn't decrease. How to tell which packages are held back due to phased updates. Thank you itdxer. so given an explanation/context and a question, it is supposed to predict the correct answer out of 4 options. However, training become somehow erratic so accuracy during training could easily drop from 40% down to 9% on validation set. ncdu: What's going on with this second size column? A recent result has found that ReLU (or similar) units tend to work better because the have steeper gradients, so updates can be applied quickly. What is happening? (which could be considered as some kind of testing). If this works, train it on two inputs with different outputs. Can I add data, that my neural network classified, to the training set, in order to improve it? See, There are a number of other options. read data from some source (the Internet, a database, a set of local files, etc. @Alex R. I'm still unsure what to do if you do pass the overfitting test. 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. 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). And these elements may completely destroy the data. 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. (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. See if the norm of the weights is increasing abnormally with epochs. And when the training rounds are after 30 times validation loss and test loss tend to be stable after 30 training . Your learning could be to big after the 25th epoch. 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. For programmers (or at least data scientists) the expression could be re-phrased as "All coding is debugging.". 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." 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. 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). 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. See: Comprehensive list of activation functions in neural networks with pros/cons. I'm possibly being too negative, but frankly I've had enough with people cloning Jupyter Notebooks from GitHub, thinking it would be a matter of minutes to adapt the code to their use case and then coming to me complaining that nothing works. Lol. 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). An application of this is to make sure that when you're masking your sequences (i.e. Decrease the initial learning rate using the 'InitialLearnRate' option of trainingOptions. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? Not the answer you're looking for? Continuing the binary example, if your data is 30% 0's and 70% 1's, then your intial expected loss around $L=-0.3\ln(0.5)-0.7\ln(0.5)\approx 0.7$. Is it correct to use "the" before "materials used in making buildings are"? Then training proceed with online hard negative mining, and the model is better for it as a result. Is it possible to create a concave light? Asking for help, clarification, or responding to other answers. How to handle hidden-cell output of 2-layer LSTM in PyTorch? 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.

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