lstm validation loss not decreasing

I had this issue - while training loss was decreasing, the validation loss was not decreasing. See, There are a number of other options. How do you ensure that a red herring doesn't violate Chekhov's gun? read data from some source (the Internet, a database, a set of local files, etc. @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. Since either on its own is very useful, understanding how to use both is an active area of research. You just need to set up a smaller value for your learning rate. 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. thank you n1k31t4 for your replies, you're right about the scaler/targetScaler issue, however it doesn't significantly change the outcome of the experiment. 3) Generalize your model outputs to debug. For example a Naive Bayes classifier for classification (or even just classifying always the most common class), or an ARIMA model for time series forecasting. But how could extra training make the training data loss bigger? Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. However, training become somehow erratic so accuracy during training could easily drop from 40% down to 9% on validation set. @Alex R. I'm still unsure what to do if you do pass the overfitting test. Your learning rate could be to big after the 25th epoch. Training loss goes down and up again. What is happening? 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. I then pass the answers through an LSTM to get a representation (50 units) of the same length for answers. However I don't get any sensible values for accuracy. 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. Loss was constant 4.000 and accuracy 0.142 on 7 target values dataset. 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). As an example, if you expect your output to be heavily skewed toward 0, it might be a good idea to transform your expected outputs (your training data) by taking the square roots of the expected output. I agree with this answer. 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. tensorflow - Why the LSTM can't reduce the loss - Stack Overflow To subscribe to this RSS feed, copy and paste this URL into your RSS reader. How to use Learning Curves to Diagnose Machine Learning Model How do you ensure that a red herring doesn't violate Chekhov's gun? 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. 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. When resizing an image, what interpolation do they use? 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 If your neural network does not generalize well, see: What should I do when my neural network doesn't generalize well? 6) Standardize your Preprocessing and Package Versions. 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. Should I put my dog down to help the homeless? Multi-layer perceptron vs deep neural network, My neural network can't even learn Euclidean distance. How to handle a hobby that makes income in US. Selecting a label smoothing factor for seq2seq NMT with a massive imbalanced vocabulary. Accuracy on training dataset was always okay. 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 . Why is Newton's method not widely used in machine learning? anonymous2 (Parker) May 9, 2022, 5:30am #1. 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. I keep all of these configuration files. Can archive.org's Wayback Machine ignore some query terms? Validation loss is neither increasing or decreasing nlp - Pytorch LSTM model's loss not decreasing - Stack Overflow A lot of times you'll see an initial loss of something ridiculous, like 6.5. 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. When my network doesn't learn, I turn off all regularization and verify that the non-regularized network works correctly. Is it possible to create a concave light? ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. Sometimes, networks simply won't reduce the loss if the data isn't scaled. This is a very active area of research. $f(\mathbf x) = \alpha(\mathbf W \mathbf x + \mathbf b)$, $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$, $\mathbf y = \begin{bmatrix}1 & 0 & 0 & \cdots & 0\end{bmatrix}$. LSTM training loss does not decrease - nlp - PyTorch Forums Why this happening and how can I fix it? 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. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? And struggled for a long time that the model does not learn. 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. Why is this the case? 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). To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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. What's the best way to answer "my neural network doesn't work, please fix" questions? 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? Replacing broken pins/legs on a DIP IC package. Dealing with such a Model: Data Preprocessing: Standardizing and Normalizing the data. Or the other way around? rev2023.3.3.43278. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Go back to point 1 because the results aren't good. Initialization over too-large an interval can set initial weights too large, meaning that single neurons have an outsize influence over the network behavior. As you commented, this in not the case here, you generate the data only once. neural-network - PytorchRNN - 12 that validation loss and test loss keep decreasing when the training rounds are before 30 times. I get NaN values for train/val loss and therefore 0.0% accuracy. I think what you said must be on the right track. 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. Connect and share knowledge within a single location that is structured and easy to search. My dataset contains about 1000+ examples. 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. 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. ncdu: What's going on with this second size column? In particular, you should reach the random chance loss on the test set. Do new devs get fired if they can't solve a certain bug? $$. 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. Lol. To learn more, see our tips on writing great answers. Don't Overfit! How to prevent Overfitting in your Deep Learning It just stucks at random chance of particular result with no loss improvement during training. Learn more about Stack Overflow the company, and our products. 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. What is the best question generation state of art with nlp? LSTM Training loss decreases and increases, Sequence lengths in LSTM / BiLSTMs and overfitting, Why does the loss/accuracy fluctuate during the training? My recent lesson is trying to detect if an image contains some hidden information, by stenography tools. 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. 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. 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 $\mathbf W \in \mathbb R^{k \times d}$. What image preprocessing routines do they use? and all you will be able to do is shrug your shoulders. Validation loss is not decreasing - Data Science Stack Exchange A place where magic is studied and practiced? How to handle hidden-cell output of 2-layer LSTM in PyTorch? . Why does Mister Mxyzptlk need to have a weakness in the comics? In the given base model, there are 2 hidden Layers, one with 128 and one with 64 neurons. How to interpret intermitent decrease of loss? This leaves how to close the generalization gap of adaptive gradient methods an open problem. I couldn't obtained a good validation loss as my training loss was decreasing. LSTM training loss does not decrease nlp sbhatt (Shreyansh Bhatt) October 7, 2019, 5:17pm #1 Hello, I have implemented a one layer LSTM network followed by a linear layer. Especially if you plan on shipping the model to production, it'll make things a lot easier. Tuning configuration choices is not really as simple as saying that one kind of configuration choice (e.g. Model compelxity: Check if the model is too complex. Choosing the number of hidden layers lets the network learn an abstraction from the raw data. I don't know why that is. Connect and share knowledge within a single location that is structured and easy to search. 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). Any advice on what to do, or what is wrong? keras - Understanding LSTM behaviour: Validation loss smaller than train.py model.py python. The funny thing is that they're half right: coding, It is really nice answer. 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. (The author is also inconsistent about using single- or double-quotes but that's purely stylistic. What am I doing wrong here in the PlotLegends specification? This is because your model should start out close to randomly guessing. The network initialization is often overlooked as a source of neural network bugs. Connect and share knowledge within a single location that is structured and easy to search. Make sure you're minimizing the loss function, Make sure your loss is computed correctly. What are "volatile" learning curves indicative of? 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). This usually happens when your neural network weights aren't properly balanced, especially closer to the softmax/sigmoid. My model look like this: And here is the function for each training sample. Are there tables of wastage rates for different fruit and veg? Connect and share knowledge within a single location that is structured and easy to search. Why is this sentence from The Great Gatsby grammatical? However, I am running into an issue with very large MSELoss that does not decrease in training (meaning essentially my network is not training). 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. Likely a problem with the data? $\begingroup$ 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. If you can't find a simple, tested architecture which works in your case, think of a simple baseline. When I set up a neural network, I don't hard-code any parameter settings. What am I doing wrong here in the PlotLegends specification? Styling contours by colour and by line thickness in QGIS. $L^2$ regularization (aka weight decay) or $L^1$ regularization is set too large, so the weights can't move. ncdu: What's going on with this second size column? How to handle a hobby that makes income in US. It become true that I was doing regression with ReLU last activation layer, which is obviously wrong. Training loss goes up and down regularly. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Maybe in your example, you only care about the latest prediction, so your LSTM outputs a single value and not a sequence. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? So this would tell you if your initialization is bad. oytungunes Asks: Validation Loss does not decrease in LSTM? What video game is Charlie playing in Poker Face S01E07? In the context of recent research studying the difficulty of training in the presence of non-convex training criteria You can easily (and quickly) query internal model layers and see if you've setup your graph correctly. It is very weird. I have prepared the easier set, selecting cases where differences between categories were seen by my own perception as more obvious. So if you're downloading someone's model from github, pay close attention to their preprocessing. . Why do many companies reject expired SSL certificates as bugs in bug bounties? Thank you for informing me regarding your experiment. This informs us as to whether the model needs further tuning or adjustments or not. 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. A similar phenomenon also arises in another context, with a different solution. So I suspect, there's something going on with the model that I don't understand. Connect and share knowledge within a single location that is structured and easy to search. Do not train a neural network to start with! Why do many companies reject expired SSL certificates as bugs in bug bounties? Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? Thanks for contributing an answer to Cross Validated! Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? Before combining $f(\mathbf x)$ with several other layers, generate a random target vector $\mathbf y \in \mathbb R^k$. Instead, I do that in a configuration file (e.g., JSON) that is read and used to populate network configuration details at runtime. What image loaders do they use? 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. 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. Build unit tests. This is especially useful for checking that your data is correctly normalized. Why does momentum escape from a saddle point in this famous image? Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary. MathJax reference. I knew a good part of this stuff, what stood out for me is. 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. 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. As the OP was using Keras, another option to make slightly more sophisticated learning rate updates would be to use a callback like. Training and Validation Loss in Deep Learning - Baeldung If you observed this behaviour you could use two simple solutions. First, it quickly shows you that your model is able to learn by checking if your model can overfit your data. If it is indeed memorizing, the best practice is to collect a larger dataset. One caution about ReLUs is the "dead neuron" phenomenon, which can stymie learning; leaky relus and similar variants avoid this problem. To set the gradient threshold, use the 'GradientThreshold' option in trainingOptions. 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. 6 Answers Sorted by: 36 The model is overfitting right from epoch 10, the validation loss is increasing while the training loss is decreasing. The scale of the data can make an enormous difference on training. 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"? Otherwise, you might as well be re-arranging deck chairs on the RMS Titanic. 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. Does Counterspell prevent from any further spells being cast on a given turn?

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