lstm validation loss not decreasing

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. I just attributed that to a poor choice for the accuracy-metric and haven't given it much thought. For example you could try dropout of 0.5 and so on. Asking for help, clarification, or responding to other answers. Is it possible to create a concave light? Do new devs get fired if they can't solve a certain bug? Additionally, neural networks have a very large number of parameters, which restricts us to solely first-order methods (see: Why is Newton's method not widely used in machine learning?). How does the Adam method of stochastic gradient descent work? 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. The difference between the phonemes /p/ and /b/ in Japanese, Short story taking place on a toroidal planet or moon involving flying. The problem I find is that the models, for various hyperparameters I try (e.g. Loss is still decreasing at the end of training. It also hedges against mistakenly repeating the same dead-end experiment. When resizing an image, what interpolation do they use? In all other cases, the optimization problem is non-convex, and non-convex optimization is hard. How to react to a students panic attack in an oral exam? The scale of the data can make an enormous difference on training. I try to maximize the difference between the cosine similarities for the correct and wrong answers, correct answer representation should have a high similarity with the question/explanation representation while wrong answer should have a low similarity, and minimize this loss. My recent lesson is trying to detect if an image contains some hidden information, by stenography tools. Redoing the align environment with a specific formatting. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Can archive.org's Wayback Machine ignore some query terms? "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks" by Jinghui Chen, Quanquan Gu. What degree of difference does validation and training loss need to have to be called good fit? As an example, imagine you're using an LSTM to make predictions from time-series data. The suggestions for randomization tests are really great ways to get at bugged networks. Recurrent neural networks can do well on sequential data types, such as natural language or time series data. 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. 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. I borrowed this example of buggy code from the article: Do you see the error? Double check your input data. Your learning rate could be to big after the 25th epoch. If the training algorithm is not suitable you should have the same problems even without the validation or dropout. We've added a "Necessary cookies only" option to the cookie consent popup. padding them with data to make them equal length), the LSTM is correctly ignoring your masked data. As a simple example, suppose that we are classifying images, and that we expect the output to be the $k$-dimensional vector $\mathbf y = \begin{bmatrix}1 & 0 & 0 & \cdots & 0\end{bmatrix}$. First, it quickly shows you that your model is able to learn by checking if your model can overfit your data. I think what you said must be on the right track. Thanks a bunch for your insight! To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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. How to interpret intermitent decrease of loss? rev2023.3.3.43278. 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 . The 'validation loss' metrics from the test data has been oscillating a lot after epochs but not really decreasing. 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. But why is it better? 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). Connect and share knowledge within a single location that is structured and easy to search. To verify my implementation of the model and understand keras, I'm using a toyproblem to make sure I understand what's going on. (+1) Checking the initial loss is a great suggestion. if you're getting some error at training time, update your CV and start looking for a different job :-). Residual connections can improve deep feed-forward networks. ncdu: What's going on with this second size column? I'm training a neural network but the training loss doesn't decrease. The main point is that the error rate will be lower in some point in time. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. How to match a specific column position till the end of line? Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? It only takes a minute to sign up. The essential idea of curriculum learning is best described in the abstract of the previously linked paper by Bengio et al. So this does not explain why you do not see overfit. 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. Try to adjust the parameters $\mathbf W$ and $\mathbf b$ to minimize this loss function. 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. @Alex R. I'm still unsure what to do if you do pass the overfitting test. Try to set up it smaller and check your loss again. Learning . The validation loss slightly increase such as from 0.016 to 0.018. All of these topics are active areas of research. The best answers are voted up and rise to the top, Not the answer you're looking for? Convolutional neural networks can achieve impressive results on "structured" data sources, image or audio data. Use MathJax to format equations. This usually happens when your neural network weights aren't properly balanced, especially closer to the softmax/sigmoid. oytungunes Asks: Validation Loss does not decrease in LSTM? 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. If your model is unable to overfit a few data points, then either it's too small (which is unlikely in today's age),or something is wrong in its structure or the learning algorithm. ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. Neural Network - Estimating Non-linear function, Poor recurrent neural network performance on sequential data. Connect and share knowledge within a single location that is structured and easy to search. 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. And struggled for a long time that the model does not learn. Training loss goes down and up again. Then training proceed with online hard negative mining, and the model is better for it as a result. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks. Why does $[0,1]$ scaling dramatically increase training time for feed forward ANN (1 hidden layer)? Why do many companies reject expired SSL certificates as bugs in bug bounties? Check the data pre-processing and augmentation. 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"? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Find centralized, trusted content and collaborate around the technologies you use most. Connect and share knowledge within a single location that is structured and easy to search. This is a non-exhaustive list of the configuration options which are not also regularization options or numerical optimization options. 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. 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. My training loss goes down and then up again. (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. How to handle a hobby that makes income in US. Where $a$ is your learning rate, $t$ is your iteration number and $m$ is a coefficient that identifies learning rate decreasing speed. @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. (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). If nothing helped, it's now the time to start fiddling with hyperparameters. Do not train a neural network to start with! 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. Instead, several authors have proposed easier methods, such as Curriculum by Smoothing, where the output of each convolutional layer in a convolutional neural network (CNN) is smoothed using a Gaussian kernel. Using indicator constraint with two variables. Finally, I append as comments all of the per-epoch losses for training and validation. Using Kolmogorov complexity to measure difficulty of problems? MathJax reference. import imblearn import mat73 import keras from keras.utils import np_utils import os. If it is indeed memorizing, the best practice is to collect a larger dataset. 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 . number of units), since all of these choices interact with all of the other choices, so one choice can do well in combination with another choice made elsewhere. It takes 10 minutes just for your GPU to initialize your model. How do you ensure that a red herring doesn't violate Chekhov's gun? I agree with your analysis. 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? 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 differences are usually really small, but you'll occasionally see drops in model performance due to this kind of stuff. Replacing broken pins/legs on a DIP IC package. Then I add each regularization piece back, and verify that each of those works along the way. This tactic can pinpoint where some regularization might be poorly set. To learn more, see our tips on writing great answers. I am getting different values for the loss function per epoch. Choosing the number of hidden layers lets the network learn an abstraction from the raw data. Now I'm working on it. 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! Designing a better optimizer is very much an active area of research. So this would tell you if your initialization is bad. Any advice on what to do, or what is wrong? 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. Has 90% of ice around Antarctica disappeared in less than a decade? Learn more about Stack Overflow the company, and our products. 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. 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. See: In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. It only takes a minute to sign up. 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%. Nowadays, many frameworks have built in data pre-processing pipeline and augmentation. 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. What could cause my neural network model's loss increases dramatically? Specifically for triplet-loss models, there are a number of tricks which can improve training time and generalization. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. But for my case, training loss still goes down but validation loss stays at same level. Styling contours by colour and by line thickness in QGIS. 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). Dropout is used during testing, instead of only being used for training. Neural networks in particular are extremely sensitive to small changes in your data. Go back to point 1 because the results aren't good. 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. 3) Generalize your model outputs to debug. Instead, make a batch of fake data (same shape), and break your model down into components. 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. We hypothesize that 2 Usually when a model overfits, validation loss goes up and training loss goes down from the point of overfitting. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? What am I doing wrong here in the PlotLegends specification? My model look like this: And here is the function for each training sample. I have two stacked LSTMS as follows (on Keras): Train on 127803 samples, validate on 31951 samples. (This is an example of the difference between a syntactic and semantic error.). A lot of times you'll see an initial loss of something ridiculous, like 6.5. What to do if training loss decreases but validation loss does not decrease? A typical trick to verify that is to manually mutate some labels. Some common mistakes here are. (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. 1) Train your model on a single data point. +1 for "All coding is debugging". You have to check that your code is free of bugs before you can tune network performance! My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? There are a number of variants on stochastic gradient descent which use momentum, adaptive learning rates, Nesterov updates and so on to improve upon vanilla SGD. Please help me. If your training/validation loss are about equal then your model is underfitting. It only takes a minute to sign up. To learn more, see our tips on writing great answers. If so, how close was it? Thank you for informing me regarding your experiment. Making statements based on opinion; back them up with references or personal experience. 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. Prior to presenting data to a neural network. Then incrementally add additional model complexity, and verify that each of those works as well. What's the best way to answer "my neural network doesn't work, please fix" questions? This means that if you have 1000 classes, you should reach an accuracy of 0.1%. Is it suspicious or odd to stand by the gate of a GA airport watching the planes? I keep all of these configuration files. Of course, this can be cumbersome. normalize or standardize the data in some way. 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. If you observed this behaviour you could use two simple solutions. 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. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. model.py . The challenges of training neural networks are well-known (see: Why is it hard to train deep neural networks?). I knew a good part of this stuff, what stood out for me is. 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). That probably did fix wrong activation method. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? 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. 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.). any suggestions would be appreciated. This is especially useful for checking that your data is correctly normalized. The cross-validation loss tracks the training loss. One way for implementing curriculum learning is to rank the training examples by difficulty. First one is a simplest one. ), @Glen_b I dont think coding best practices receive enough emphasis in most stats/machine learning curricula which is why I emphasized that point so heavily. Increase the size of your model (either number of layers or the raw number of neurons per layer) . I just learned this lesson recently and I think it is interesting to share. There's a saying among writers that "All writing is re-writing" -- that is, the greater part of writing is revising. Making statements based on opinion; back them up with references or personal experience. split data in training/validation/test set, or in multiple folds if using cross-validation. Did you need to set anything else? What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? Too few neurons in a layer can restrict the representation that the network learns, causing under-fitting. Here, we formalize such training strategies in the context of machine learning, and call them curriculum learning. Scaling the testing data using the statistics of the test partition instead of the train partition; Forgetting to un-scale the predictions (e.g.

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