lstm validation loss not decreasing lstm validation loss not decreasing

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"? What am I doing wrong here in the PlotLegends specification? Making sure that your model can overfit is an excellent idea. My dataset contains about 1000+ examples. Any advice on what to do, or what is wrong? Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? Psychologically, it also lets you look back and observe "Well, the project might not be where I want it to be today, but I am making progress compared to where I was $k$ weeks ago. (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. The validation loss is similar to the training loss and is calculated from a sum of the errors for each example in the validation set. For programmers (or at least data scientists) the expression could be re-phrased as "All coding is debugging.". Connect and share knowledge within a single location that is structured and easy to search. 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'll let you decide. The experiments show that significant improvements in generalization can be achieved. Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? ncdu: What's going on with this second size column? It only takes a minute to sign up. visualize the distribution of weights and biases for each layer. 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. Large non-decreasing LSTM training loss - PyTorch Forums Ive seen a number of NN posts where OP left a comment like oh I found a bug now it works.. Making statements based on opinion; back them up with references or personal experience. 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. Additionally, the validation loss is measured after each epoch. 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). But these networks didn't spring fully-formed into existence; their designers built up to them from smaller units. See: In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. 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). Check the accuracy on the test set, and make some diagnostic plots/tables. A place where magic is studied and practiced? This is called unit testing. The best answers are voted up and rise to the top, Not the answer you're looking for? What should I do when my neural network doesn't generalize well? I checked and found while I was using LSTM: Thanks for contributing an answer to Data Science Stack Exchange! What should I do when my neural network doesn't learn? 1 2 . Even for simple, feed-forward networks, the onus is largely on the user to make numerous decisions about how the network is configured, connected, initialized and optimized. Why is Newton's method not widely used in machine learning? How do you ensure that a red herring doesn't violate Chekhov's gun? The posted answers are great, and I wanted to add a few "Sanity Checks" which have greatly helped me in the past. Use MathJax to format equations. 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 . 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. 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. The validation loss slightly increase such as from 0.016 to 0.018. Thanks for contributing an answer to Data Science Stack Exchange! If you preorder a special airline meal (e.g. (No, It Is Not About Internal Covariate Shift). I just learned this lesson recently and I think it is interesting to share. if you're getting some error at training time, update your CV and start looking for a different job :-). How to react to a students panic attack in an oral exam? LSTM training loss does not decrease - nlp - PyTorch Forums So this does not explain why you do not see overfit. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. What's the difference between a power rail and a signal line? 6 Answers Sorted by: 36 The model is overfitting right from epoch 10, the validation loss is increasing while the training loss is decreasing. 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. loss/val_loss are decreasing but accuracies are the same in LSTM! That probably did fix wrong activation method. 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. ncdu: What's going on with this second size column? In one example, I use 2 answers, one correct answer and one wrong answer. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. (+1) Checking the initial loss is a great suggestion. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Connect and share knowledge within a single location that is structured and easy to search. keras - Understanding LSTM behaviour: Validation loss smaller than Now I'm working on it. 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. Before combining $f(\mathbf x)$ with several other layers, generate a random target vector $\mathbf y \in \mathbb R^k$. To learn more, see our tips on writing great answers. 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 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). Too few neurons in a layer can restrict the representation that the network learns, causing under-fitting. If this doesn't happen, there's a bug in your code. 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. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? Also, real-world datasets are dirty: for classification, there could be a high level of label noise (samples having the wrong class label) or for multivariate time series forecast, some of the time series components may have a lot of missing data (I've seen numbers as high as 94% for some of the inputs). What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? What to do if training loss decreases but validation loss does not decrease? and i used keras framework to build the network, but it seems the NN can't be build up easily. We've added a "Necessary cookies only" option to the cookie consent popup. To learn more, see our tips on writing great answers. Deep Learning Tips and Tricks - MATLAB & Simulink - MathWorks Why does Mister Mxyzptlk need to have a weakness in the comics? It can also catch buggy activations. 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. rev2023.3.3.43278. Redoing the align environment with a specific formatting. The scale of the data can make an enormous difference on training. Loss was constant 4.000 and accuracy 0.142 on 7 target values dataset. 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. This means writing code, and writing code means debugging. But how could extra training make the training data loss bigger? Scaling the testing data using the statistics of the test partition instead of the train partition; Forgetting to un-scale the predictions (e.g. As an example, imagine you're using an LSTM to make predictions from time-series data. What could cause this? I borrowed this example of buggy code from the article: Do you see the error? 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. Although it can easily overfit to a single image, it can't fit to a large dataset, despite good normalization and shuffling. Why do we use ReLU in neural networks and how do we use it? Large non-decreasing LSTM training loss. How to Diagnose Overfitting and Underfitting of LSTM Models; Overfitting and Underfitting With Machine Learning Algorithms; Articles. learning rate) is more or less important than another (e.g. 1) Train your model on a single data point. The best method I've ever found for verifying correctness is to break your code into small segments, and verify that each segment works. It is very weird. How can change in cost function be positive? Thanks. ), have a look at a few samples (to make sure the import has gone well) and perform data cleaning if/when needed. Should I put my dog down to help the homeless? 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. 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 What is the essential difference between neural network and linear regression. any suggestions would be appreciated. ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. How to Diagnose Overfitting and Underfitting of LSTM Models By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? 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. 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%. (See: Why do we use ReLU in neural networks and how do we use it?) (which could be considered as some kind of testing). The asker was looking for "neural network doesn't learn" so I majored there. My model look like this: And here is the function for each training sample. I think what you said must be on the right track. Suppose that the softmax operation was not applied to obtain $\mathbf y$ (as is normally done), and suppose instead that some other operation, called $\delta(\cdot)$, that is also monotonically increasing in the inputs, was applied instead. Find centralized, trusted content and collaborate around the technologies you use most. 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). self.rnn = nn.RNNinput_size = input_sizehidden_ size = hidden_ sizebatch_first = TrueNameError'input_size'. So I suspect, there's something going on with the model that I don't understand. If you don't see any difference between the training loss before and after shuffling labels, this means that your code is buggy (remember that we have already checked the labels of the training set in the step before). anonymous2 (Parker) May 9, 2022, 5:30am #1. Do new devs get fired if they can't solve a certain bug? The problem I find is that the models, for various hyperparameters I try (e.g. Hey there, I'm just curious as to why this is so common with RNNs. Why do many companies reject expired SSL certificates as bugs in bug bounties? If you re-train your RNN on this fake dataset and achieve similar performance as on the real dataset, then we can say that your RNN is memorizing. To learn more, see our tips on writing great answers. Is your data source amenable to specialized network architectures? Tensorboard provides a useful way of visualizing your layer outputs. vegan) just to try it, does this inconvenience the caterers and staff? Neural networks are not "off-the-shelf" algorithms in the way that random forest or logistic regression are. I edited my original post to accomodate your input and some information about my loss/acc values. I am training an LSTM to give counts of the number of items in buckets. You have to check that your code is free of bugs before you can tune network performance! Fighting the good fight. Can I tell police to wait and call a lawyer when served with a search warrant? To learn more, see our tips on writing great answers. : Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones. 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. . This Medium post, "How to unit test machine learning code," by Chase Roberts discusses unit-testing for machine learning models in more detail. In the context of recent research studying the difficulty of training in the presence of non-convex training criteria Why this happening and how can I fix it? What should I do? What could cause this? normalize or standardize the data in some way. 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. Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary. It also hedges against mistakenly repeating the same dead-end experiment. Then incrementally add additional model complexity, and verify that each of those works as well. 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.). Or the other way around? Asking for help, clarification, or responding to other answers. I just copied the code above (fixed the scaler bug) and reran it on CPU. Neural networks in particular are extremely sensitive to small changes in your data. Some common mistakes here are. rev2023.3.3.43278. How do you ensure that a red herring doesn't violate Chekhov's gun? Also, when it comes to explaining your model, someone will come along and ask "what's the effect of $x_k$ on the result?" For me, the validation loss also never decreases. Is it correct to use "the" before "materials used in making buildings are"? You just need to set up a smaller value for your learning rate. This will avoid gradient issues for saturated sigmoids, at the output. I'm asking about how to solve the problem where my network's performance doesn't improve on the training set. 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. I am training a LSTM model to do question answering, i.e. (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). 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. or bAbI. 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. This will help you make sure that your model structure is correct and that there are no extraneous issues. 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. Minimising the environmental effects of my dyson brain. Choosing a clever network wiring can do a lot of the work for you. 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). I tried using "adam" instead of "adadelta" and this solved the problem, though I'm guessing that reducing the learning rate of "adadelta" would probably have worked also. If your training/validation loss are about equal then your model is underfitting. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. I had this issue - while training loss was decreasing, the validation loss was not decreasing. The Marginal Value of Adaptive Gradient Methods in Machine Learning, Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. Can archive.org's Wayback Machine ignore some query terms? Using this block of code in a network will still train and the weights will update and the loss might even decrease -- but the code definitely isn't doing what was intended. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. The lstm_size can be adjusted . 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). What is happening? Care to comment on that? What are "volatile" learning curves indicative of? I am getting different values for the loss function per epoch. Set up a very small step and train it. I like to start with exploratory data analysis to get a sense of "what the data wants to tell me" before getting into the models. "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks" by Jinghui Chen, Quanquan Gu. Connect and share knowledge within a single location that is structured and easy to search. 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). The first step when dealing with overfitting is to decrease the complexity of the model. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Making sure the derivative is approximately matching your result from backpropagation should help in locating where is the problem. 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. (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. For example, suppose we are building a classifier to classify 6 and 9, and we use random rotation augmentation Why can't scikit-learn SVM solve two concentric circles? 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. How to handle hidden-cell output of 2-layer LSTM in PyTorch? See this Meta thread for a discussion: What's the best way to answer "my neural network doesn't work, please fix" questions? Basically, the idea is to calculate the derivative by defining two points with a $\epsilon$ interval. If this works, train it on two inputs with different outputs. hidden units). Residual connections can improve deep feed-forward networks. Try to set up it smaller and check your loss again. For example, it's widely observed that layer normalization and dropout are difficult to use together. What is a word for the arcane equivalent of a monastery? There is simply no substitute. and all you will be able to do is shrug your shoulders. Usually I make these preliminary checks: look for a simple architecture which works well on your problem (for example, MobileNetV2 in the case of image classification) and apply a suitable initialization (at this level, random will usually do). My recent lesson is trying to detect if an image contains some hidden information, by stenography tools. This can be a source of issues. Does Counterspell prevent from any further spells being cast on a given turn? Training loss decreasing while Validation loss is not decreasing Try a random shuffle of the training set (without breaking the association between inputs and outputs) and see if the training loss goes down. There's a saying among writers that "All writing is re-writing" -- that is, the greater part of writing is revising. Training loss goes up and down regularly. Why is this the case? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. How Intuit democratizes AI development across teams through reusability. And struggled for a long time that the model does not learn. If you observed this behaviour you could use two simple solutions. This is an easier task, so the model learns a good initialization before training on the real task. While this is highly dependent on the availability of data. Finally, the best way to check if you have training set issues is to use another training set. "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'm building a lstm model for regression on timeseries. Recurrent neural networks can do well on sequential data types, such as natural language or time series data. I have prepared the easier set, selecting cases where differences between categories were seen by my own perception as more obvious. Connect and share knowledge within a single location that is structured and easy to search. This is a very active area of research. nlp - Pytorch LSTM model's loss not decreasing - Stack Overflow Any time you're writing code, you need to verify that it works as intended. First, build a small network with a single hidden layer and verify that it works correctly. If the training algorithm is not suitable you should have the same problems even without the validation or dropout.