lstm validation loss not decreasing

Problem is I do not understand what's going on here. 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. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Writing good unit tests is a key piece of becoming a good statistician/data scientist/machine learning expert/neural network practitioner. Lol. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? train.py model.py python. 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. If decreasing the learning rate does not help, then try using gradient clipping. Calculating probabilities from d6 dice pool (Degenesis rules for botches and triggers), Minimising the environmental effects of my dyson brain. 6) Standardize your Preprocessing and Package Versions. Without generalizing your model you will never find this issue. Minimising the environmental effects of my dyson brain. 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). Can archive.org's Wayback Machine ignore some query terms? visualize the distribution of weights and biases for each layer. Aren't my iterations needed to train NN for XOR with MSE < 0.001 too high? I reduced the batch size from 500 to 50 (just trial and error). You need to test all of the steps that produce or transform data and feed into the network. 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. Making statements based on opinion; back them up with references or personal experience. 3) Generalize your model outputs to debug. rev2023.3.3.43278. 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. As a simple example, suppose that we are classifying images, and that we expect the output to be the $k$-dimensional vector $\mathbf y = \begin{bmatrix}1 & 0 & 0 & \cdots & 0\end{bmatrix}$. What am I doing wrong here in the PlotLegends specification? But these networks didn't spring fully-formed into existence; their designers built up to them from smaller units. 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. See this Meta thread for a discussion: What's the best way to answer "my neural network doesn't work, please fix" questions? padding them with data to make them equal length), the LSTM is correctly ignoring your masked data. This means that if you have 1000 classes, you should reach an accuracy of 0.1%. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Training loss goes down and up again. Thus, if the machine is constantly improving and does not overfit, the gap between the network's average performance in an epoch and its performance at the end of an epoch is translated into the gap between training and validation scores - in favor of the validation scores. Wide and deep neural networks, and neural networks with exotic wiring, are the Hot Thing right now in machine learning. (which could be considered as some kind of testing). The best answers are voted up and rise to the top, Not the answer you're looking for? 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. Just want to add on one technique haven't been discussed yet. Scaling the testing data using the statistics of the test partition instead of the train partition; Forgetting to un-scale the predictions (e.g. 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. Why are physically impossible and logically impossible concepts considered separate in terms of probability? 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. If so, how close was it? MathJax reference. As I am fitting the model, training loss is constantly larger than validation loss, even for a balanced train/validation set (5000 samples each): In my understanding the two curves should be exactly the other way around such that training loss would be an upper bound for validation loss. 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. The suggestions for randomization tests are really great ways to get at bugged networks. The second one is to decrease your learning rate monotonically. Any advice on what to do, or what is wrong? Thanks for contributing an answer to Data Science Stack Exchange! Two parts of regularization are in conflict. Even when a neural network code executes without raising an exception, the network can still have bugs! Nowadays, many frameworks have built in data pre-processing pipeline and augmentation. +1 for "All coding is debugging". Is it possible to share more info and possibly some code? You want the mini-batch to be large enough to be informative about the direction of the gradient, but small enough that SGD can regularize your network. 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. 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. You have to check that your code is free of bugs before you can tune network performance! What is happening? But the validation loss starts with very small . Any time you're writing code, you need to verify that it works as intended. 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." 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). If I make any parameter modification, I make a new configuration file. 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. But how could extra training make the training data loss bigger? Here is a simple formula: $$ The main point is that the error rate will be lower in some point in time. Why do we use ReLU in neural networks and how do we use it? Minimising the environmental effects of my dyson brain. Using Kolmogorov complexity to measure difficulty of problems? Neural Network - Estimating Non-linear function, Poor recurrent neural network performance on sequential data. 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 $\mathbf W \in \mathbb R^{k \times d}$. Is it correct to use "the" before "materials used in making buildings are"? Of course, this can be cumbersome. What image preprocessing routines do they use? Okay, so this explains why the validation score is not worse. Thanks. Neural networks are not "off-the-shelf" algorithms in the way that random forest or logistic regression are. Might be an interesting experiment. Do new devs get fired if they can't solve a certain bug? 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? Check the data pre-processing and augmentation. What is going on? 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. What to do if training loss decreases but validation loss does not decrease? Is it possible to create a concave light? Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary. If it is indeed memorizing, the best practice is to collect a larger dataset. I simplified the model - instead of 20 layers, I opted for 8 layers. Are there tables of wastage rates for different fruit and veg? Conceptually this means that your output is heavily saturated, for example toward 0. Is it correct to use "the" before "materials used in making buildings are"? The scale of the data can make an enormous difference on training. 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. I just learned this lesson recently and I think it is interesting to share. Other people insist that scheduling is essential. Decrease the initial learning rate using the 'InitialLearnRate' option of trainingOptions. Care to comment on that? I checked and found while I was using LSTM: I simplified the model - instead of 20 layers, I opted for 8 layers. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Convolutional neural networks can achieve impressive results on "structured" data sources, image or audio data. 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. Short story taking place on a toroidal planet or moon involving flying. rev2023.3.3.43278. The problem turns out to be the misunderstanding of the batch size and other features that defining an nn.LSTM. I'm not asking about overfitting or regularization. ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. If your neural network does not generalize well, see: What should I do when my neural network doesn't generalize well? I think what you said must be on the right track. Double check your input data. Then training proceed with online hard negative mining, and the model is better for it as a result. Finally, I append as comments all of the per-epoch losses for training and validation. 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. When I set up a neural network, I don't hard-code any parameter settings. LSTM neural network is a kind of temporal recurrent neural network (RNN), whose core is the gating unit. or bAbI. Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. . Deep learning is all the rage these days, and networks with a large number of layers have shown impressive results. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". $f(\mathbf x) = \alpha(\mathbf W \mathbf x + \mathbf b)$, $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$, $\mathbf y = \begin{bmatrix}1 & 0 & 0 & \cdots & 0\end{bmatrix}$. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. To achieve state of the art, or even merely good, results, you have to set up all of the parts configured to work well together. 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, The model of LSTM with more than one unit. 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 an example of the difference between a syntactic and semantic error.). Using indicator constraint with two variables. What could cause this? Or the other way around? Make sure you're minimizing the loss function, Make sure your loss is computed correctly. 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. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Is it suspicious or odd to stand by the gate of a GA airport watching the planes? How do you ensure that a red herring doesn't violate Chekhov's gun? Many of the different operations are not actually used because previous results are over-written with new variables. Asking for help, clarification, or responding to other answers. If your training/validation loss are about equal then your model is underfitting. This means writing code, and writing code means debugging. You might want to simplify your architecture to include just a single LSTM layer (like I did) just until you convince yourself that the model is actually learning something. And the loss in the training looks like this: Is there anything wrong with these codes? 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). To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Go back to point 1 because the results aren't good. so given an explanation/context and a question, it is supposed to predict the correct answer out of 4 options. Likely a problem with the data? The funny thing is that they're half right: coding, It is really nice answer. Thanks for contributing an answer to Stack Overflow! anonymous2 (Parker) May 9, 2022, 5:30am #1. My dataset contains about 1000+ examples. Towards a Theoretical Understanding of Batch Normalization, How Does Batch Normalization Help Optimization? I worked on this in my free time, between grad school and my job. I borrowed this example of buggy code from the article: Do you see the error? Weight changes but performance remains the same. Does not being able to overfit a single training sample mean that the neural network architecure or implementation is wrong? 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 validation loss slightly increase such as from 0.016 to 0.018. I am amazed how many posters on SO seem to think that coding is a simple exercise requiring little effort; who expect their code to work correctly the first time they run it; and who seem to be unable to proceed when it doesn't. Thanks @Roni. 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. Do new devs get fired if they can't solve a certain bug? pixel values are in [0,1] instead of [0, 255]). 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 . Connect and share knowledge within a single location that is structured and easy to search. To learn more, see our tips on writing great answers. A similar phenomenon also arises in another context, with a different solution. 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. 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. @Alex R. I'm still unsure what to do if you do pass the overfitting test. Accuracy (0-1 loss) is a crappy metric if you have strong class imbalance. :). 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. $\begingroup$ 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. Tuning configuration choices is not really as simple as saying that one kind of configuration choice (e.g. The essential idea of curriculum learning is best described in the abstract of the previously linked paper by Bengio et al. 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. Connect and share knowledge within a single location that is structured and easy to search. This usually happens when your neural network weights aren't properly balanced, especially closer to the softmax/sigmoid. 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. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. How do you ensure that a red herring doesn't violate Chekhov's gun? Has 90% of ice around Antarctica disappeared in less than a decade? You can also query layer outputs in keras on a batch of predictions, and then look for layers which have suspiciously skewed activations (either all 0, or all nonzero). I regret that I left it out of my answer. 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. Tensorboard provides a useful way of visualizing your layer outputs. 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. Fighting the good fight. I understand that it might not be feasible, but very often data size is the key to success. Why is this the case? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. To make sure the existing knowledge is not lost, reduce the set learning rate. What can be the actions to decrease? What is the essential difference between neural network and linear regression. (+1) Checking the initial loss is a great suggestion. Some common mistakes here are. it is shown in Fig. The problem I find is that the models, for various hyperparameters I try (e.g. Curriculum learning is a formalization of @h22's answer. Hey there, I'm just curious as to why this is so common with RNNs. Making statements based on opinion; back them up with references or personal experience. How to handle a hobby that makes income in US. Have a look at a few input samples, and the associated labels, and make sure they make sense. . Note that it is not uncommon that when training a RNN, reducing model complexity (by hidden_size, number of layers or word embedding dimension) does not improve overfitting. If you want to write a full answer I shall accept it. Thanks a bunch for your insight! What should I do when my neural network doesn't generalize well? It become true that I was doing regression with ReLU last activation layer, which is obviously wrong. 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. Why is it hard to train deep neural networks? I keep all of these configuration files. I am writing a program that make use of the build in LSTM in the Pytorch, however the loss is always around some numbers and does not decrease significantly. Why do many companies reject expired SSL certificates as bugs in bug bounties? What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? Build unit tests. . I never had to get here, but if you're using BatchNorm, you would expect approximately standard normal distributions. Did you need to set anything else? Set up a very small step and train it. If I run your code (unchanged - on a GPU), then the model doesn't seem to train. (The author is also inconsistent about using single- or double-quotes but that's purely stylistic. (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). This informs us as to whether the model needs further tuning or adjustments or not. If nothing helped, it's now the time to start fiddling with hyperparameters. There are 252 buckets. What image loaders do they use? What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? My model look like this: And here is the function for each training sample. Use MathJax to format equations. Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? Comprehensive list of activation functions in neural networks with pros/cons, "Deep Residual Learning for Image Recognition", Identity Mappings in Deep Residual Networks. Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. Residual connections can improve deep feed-forward networks. The order in which the training set is fed to the net during training may have an effect. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? Training loss goes up and down regularly. 1 2 . This is called unit testing. Ive seen a number of NN posts where OP left a comment like oh I found a bug now it works.. I don't know why that is. 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 . Switch the LSTM to return predictions at each step (in keras, this is return_sequences=True). Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? One caution about ReLUs is the "dead neuron" phenomenon, which can stymie learning; leaky relus and similar variants avoid this problem. "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.

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