lstm validation loss not decreasing lstm validation loss not decreasing

Thanks @Roni. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? What image preprocessing routines do they use? remove regularization gradually (maybe switch batch norm for a few layers). This is because your model should start out close to randomly guessing. hidden units). If we do not trust that $\delta(\cdot)$ is working as expected, then since we know that it is monotonically increasing in the inputs, then we can work backwards and deduce that the input must have been a $k$-dimensional vector where the maximum element occurs at the first element. Loss was constant 4.000 and accuracy 0.142 on 7 target values dataset. If I make any parameter modification, I make a new configuration file. Data normalization and standardization in neural networks. visualize the distribution of weights and biases for each layer. Especially if you plan on shipping the model to production, it'll make things a lot easier. 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. Why do many companies reject expired SSL certificates as bugs in bug bounties? Why does momentum escape from a saddle point in this famous image? Otherwise, you might as well be re-arranging deck chairs on the RMS Titanic. Decrease the initial learning rate using the 'InitialLearnRate' option of trainingOptions. 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." 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. Is it correct to use "the" before "materials used in making buildings are"? How to interpret intermitent decrease of loss? 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. Choosing a clever network wiring can do a lot of the work for you. Use MathJax to format equations. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? "The Marginal Value of Adaptive Gradient Methods in Machine Learning" by Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, Benjamin Recht, But on the other hand, this very recent paper proposes a new adaptive learning-rate optimizer which supposedly closes the gap between adaptive-rate methods and SGD with momentum. I don't know why that is. 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. Aren't my iterations needed to train NN for XOR with MSE < 0.001 too high? This leaves how to close the generalization gap of adaptive gradient methods an open problem. Even when a neural network code executes without raising an exception, the network can still have bugs! In my case it's not a problem with the architecture (I'm implementing a Resnet from another paper). 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. I just attributed that to a poor choice for the accuracy-metric and haven't given it much thought. The suggestions for randomization tests are really great ways to get at bugged networks. I worked on this in my free time, between grad school and my job. I checked and found while I was using LSTM: I simplified the model - instead of 20 layers, I opted for 8 layers. Training loss decreasing while Validation loss is not decreasing See if you inverted the training set and test set labels, for example (happened to me once -___-), or if you imported the wrong file. Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? I have two stacked LSTMS as follows (on Keras): Train on 127803 samples, validate on 31951 samples. keras - Understanding LSTM behaviour: Validation loss smaller than Deep Learning Tips and Tricks - MATLAB & Simulink - MathWorks You need to test all of the steps that produce or transform data and feed into the network. Okay, so this explains why the validation score is not worse. Why does the loss/accuracy fluctuate during the training? (Keras, LSTM) Thanks. But why is it better? How to tell which packages are held back due to phased updates. So this does not explain why you do not see overfit. 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. How can I fix this? Can I add data, that my neural network classified, to the training set, in order to improve it? There are 252 buckets. Can I tell police to wait and call a lawyer when served with a search warrant? Choosing the number of hidden layers lets the network learn an abstraction from the raw data. I understand that it might not be feasible, but very often data size is the key to success. Replacing broken pins/legs on a DIP IC package. However I don't get any sensible values for accuracy. 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. 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). How can change in cost function be positive? To learn more, see our tips on writing great answers. Recurrent neural networks can do well on sequential data types, such as natural language or time series data. Curriculum learning is a formalization of @h22's answer. 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. (This is an example of the difference between a syntactic and semantic error.). This Medium post, "How to unit test machine learning code," by Chase Roberts discusses unit-testing for machine learning models in more detail. As an example, imagine you're using an LSTM to make predictions from time-series data. Go back to point 1 because the results aren't good. 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. See: In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. 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. Calculating probabilities from d6 dice pool (Degenesis rules for botches and triggers), Minimising the environmental effects of my dyson brain. Problem is I do not understand what's going on here. Thanks for contributing an answer to Stack Overflow! Connect and share knowledge within a single location that is structured and easy to search. 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. Setting this too small will prevent you from making any real progress, and possibly allow the noise inherent in SGD to overwhelm your gradient estimates. If the loss decreases consistently, then this check has passed. We hypothesize that 3) Generalize your model outputs to debug. as a particular form of continuation method (a general strategy for global optimization of non-convex functions). +1 for "All coding is debugging". Dropout is used during testing, instead of only being used for training. 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. The second one is to decrease your learning rate monotonically. And the loss in the training looks like this: Is there anything wrong with these codes? Does Counterspell prevent from any further spells being cast on a given turn? Connect and share knowledge within a single location that is structured and easy to search. 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. I agree with your analysis. It takes 10 minutes just for your GPU to initialize your model. 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. See if the norm of the weights is increasing abnormally with epochs. So I suspect, there's something going on with the model that I don't understand. The posted answers are great, and I wanted to add a few "Sanity Checks" which have greatly helped me in the past. LSTM neural network is a kind of temporal recurrent neural network (RNN), whose core is the gating unit. 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. Lots of good advice there. What is the best question generation state of art with nlp? What is a word for the arcane equivalent of a monastery? The validation loss slightly increase such as from 0.016 to 0.018. It become true that I was doing regression with ReLU last activation layer, which is obviously wrong. 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! In the context of recent research studying the difficulty of training in the presence of non-convex training criteria Where $a$ is your learning rate, $t$ is your iteration number and $m$ is a coefficient that identifies learning rate decreasing speed. :). What could cause my neural network model's loss increases dramatically? 1) Train your model on a single data point. So this would tell you if your initialization is bad. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. 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). The difference between the phonemes /p/ and /b/ in Japanese, Short story taking place on a toroidal planet or moon involving flying. How to use Learning Curves to Diagnose Machine Learning Model 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. I'm training a neural network but the training loss doesn't decrease. with two problems ("How do I get learning to continue after a certain epoch?" Do I need a thermal expansion tank if I already have a pressure tank? Thanks for contributing an answer to Data Science Stack Exchange! Why is this the case? We can then generate a similar target to aim for, rather than a random one. For example you could try dropout of 0.5 and so on. $\endgroup$ 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. Hence validation accuracy also stays at same level but training accuracy goes up. How to handle a hobby that makes income in US. It can also catch buggy activations. 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. nlp - Pytorch LSTM model's loss not decreasing - Stack Overflow and "How do I choose a good schedule?"). Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. This paper introduces a physics-informed machine learning approach for pathloss prediction. What is going on? Replacing broken pins/legs on a DIP IC package. If so, how close was it? 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). Why is this the case? (which could be considered as some kind of testing). Do new devs get fired if they can't solve a certain bug? The reason is many packages are rescaling images to certain size and this operation completely destroys the hidden information inside. Conceptually this means that your output is heavily saturated, for example toward 0. In the given base model, there are 2 hidden Layers, one with 128 and one with 64 neurons. If your training/validation loss are about equal then your model is underfitting. How do you ensure that a red herring doesn't violate Chekhov's gun? Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. import imblearn import mat73 import keras from keras.utils import np_utils import os. (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.). What is the essential difference between neural network and linear regression. Why do we use ReLU in neural networks and how do we use it? 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). Please help me. 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. Dropout is used during testing, instead of only being used for training. 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. (LSTM) models you are looking at data that is adjusted according to the data . pixel values are in [0,1] instead of [0, 255]). tensorflow - Why the LSTM can't reduce the loss - Stack Overflow 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). Why is Newton's method not widely used in machine learning? The cross-validation loss tracks the training loss. 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. rev2023.3.3.43278. Accuracy on training dataset was always okay. Thank you for informing me regarding your experiment. The best answers are voted up and rise to the top, Not the answer you're looking for? 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. 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. I edited my original post to accomodate your input and some information about my loss/acc values. (No, It Is Not About Internal Covariate Shift). To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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). 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"? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. I just copied the code above (fixed the scaler bug) and reran it on CPU. Do not train a neural network to start with! How do I reduce my validation loss? | ResearchGate [Solved] Validation Loss does not decrease in LSTM? If it is indeed memorizing, the best practice is to collect a larger dataset. Learn more about Stack Overflow the company, and our products. Training loss goes down and up again. Thanks a bunch for your insight! If you observed this behaviour you could use two simple solutions. @Alex R. I'm still unsure what to do if you do pass the overfitting test. In cases in which training as well as validation examples are generated de novo, the network is not presented with the same examples over and over. Why is this sentence from The Great Gatsby grammatical? The 'validation loss' metrics from the test data has been oscillating a lot after epochs but not really decreasing. I then pass the answers through an LSTM to get a representation (50 units) of the same length for answers. Does not being able to overfit a single training sample mean that the neural network architecure or implementation is wrong? There's a saying among writers that "All writing is re-writing" -- that is, the greater part of writing is revising. train the neural network, while at the same time controlling the loss on the validation set. 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. 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. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, 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() may prevent some unexpected consequences, How Intuit democratizes AI development across teams through reusability. This problem is easy to identify. Set up a very small step and train it. Then, let $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$ be a loss function. But for my case, training loss still goes down but validation loss stays at same level. In one example, I use 2 answers, one correct answer and one wrong answer.