lstm validation loss not decreasing

if you're getting some error at training time, update your CV and start looking for a different job :-). Prior to presenting data to a neural network. There is simply no substitute. $L^2$ regularization (aka weight decay) or $L^1$ regularization is set too large, so the weights can't move. If I run your code (unchanged - on a GPU), then the model doesn't seem to train. I edited my original post to accomodate your input and some information about my loss/acc values. Why does the loss/accuracy fluctuate during the training? (Keras, LSTM) 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. The 'validation loss' metrics from the test data has been oscillating a lot after epochs but not really decreasing. 1 2 . Also, when it comes to explaining your model, someone will come along and ask "what's the effect of $x_k$ on the result?" Thanks @Roni. Ive seen a number of NN posts where OP left a comment like oh I found a bug now it works.. But why is it better? Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. Tensorboard provides a useful way of visualizing your layer outputs. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Validation loss is not decreasing - Data Science Stack Exchange 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. Then you can take a look at your hidden-state outputs after every step and make sure they are actually different. 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. Decrease the initial learning rate using the 'InitialLearnRate' option of trainingOptions. If so, how close was it? 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. 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 . 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. What should I do when my neural network doesn't generalize well? If you're doing image classification, instead than the images you collected, use a standard dataset such CIFAR10 or CIFAR100 (or ImageNet, if you can afford to train on that). 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. Validation loss is neither increasing or decreasing This will avoid gradient issues for saturated sigmoids, at the output. If this doesn't happen, there's a bug in your code. 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). I had a model that did not train at all. Basically, the idea is to calculate the derivative by defining two points with a $\epsilon$ interval. 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. ncdu: What's going on with this second size column? 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). This paper introduces a physics-informed machine learning approach for pathloss prediction. I teach a programming for data science course in python, and we actually do functions and unit testing on the first day, as primary concepts. I agree with this 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 for "All coding is debugging". 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. Thank you itdxer. (LSTM) models you are looking at data that is adjusted according to the data . 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. Scaling the inputs (and certain times, the targets) can dramatically improve the network's training. Other people insist that scheduling is essential. What are "volatile" learning curves indicative of? Choosing a clever network wiring can do a lot of the work for you. The order in which the training set is fed to the net during training may have an effect. 2 Usually when a model overfits, validation loss goes up and training loss goes down from the point of overfitting. 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. history = model.fit(X, Y, epochs=100, validation_split=0.33) This looks like a typical of scenario of overfitting: in this case your RNN is memorizing the correct answers, instead of understanding the semantics and the logic to choose the correct answers. The network picked this simplified case well. train the neural network, while at the same time controlling the loss on the validation set. The asker was looking for "neural network doesn't learn" so I majored there. If the training algorithm is not suitable you should have the same problems even without the validation or dropout. 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. If you preorder a special airline meal (e.g. Deep learning is all the rage these days, and networks with a large number of layers have shown impressive results. Dropout is used during testing, instead of only being used for training. visualize the distribution of weights and biases for each layer. (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). To learn more, see our tips on writing great answers. Training accuracy is ~97% but validation accuracy is stuck at ~40%. Making statements based on opinion; back them up with references or personal experience. A similar phenomenon also arises in another context, with a different solution. This is a very active area of research. Writing good unit tests is a key piece of becoming a good statistician/data scientist/machine learning expert/neural network practitioner. 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. Neural networks are not "off-the-shelf" algorithms in the way that random forest or logistic regression are. I checked and found while I was using LSTM: I simplified the model - instead of 20 layers, I opted for 8 layers. Not the answer you're looking for? Maybe in your example, you only care about the latest prediction, so your LSTM outputs a single value and not a sequence. Switch the LSTM to return predictions at each step (in keras, this is return_sequences=True). anonymous2 (Parker) May 9, 2022, 5:30am #1. So I suspect, there's something going on with the model that I don't understand. Is your data source amenable to specialized network architectures? What should I do? 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. I just learned this lesson recently and I think it is interesting to share. It takes 10 minutes just for your GPU to initialize your model. No change in accuracy using Adam Optimizer when SGD works fine. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? Dropout is used during testing, instead of only being used for training. 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. 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. This is because your model should start out close to randomly guessing. Then I add each regularization piece back, and verify that each of those works along the way. My dataset contains about 1000+ examples. Recurrent neural networks can do well on sequential data types, such as natural language or time series data. It only takes a minute to sign up. . Do not train a neural network to start with! What could cause this? If the model isn't learning, there is a decent chance that your backpropagation is not working. In theory then, using Docker along with the same GPU as on your training system should then produce the same results. The training loss should now decrease, but the test loss may increase. Double check your input data. Ok, rereading your code I can obviously see that you are correct; I will edit my answer. Neural networks in particular are extremely sensitive to small changes in your data. The essential idea of curriculum learning is best described in the abstract of the previously linked paper by Bengio et al. \alpha(t + 1) = \frac{\alpha(0)}{1 + \frac{t}{m}} 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). First, it quickly shows you that your model is able to learn by checking if your model can overfit your data. Using Kolmogorov complexity to measure difficulty of problems? 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. In my case the initial training set was probably too difficult for the network, so it was not making any progress. What's the difference between a power rail and a signal line? Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? 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. keras - Understanding LSTM behaviour: Validation loss smaller than You need to test all of the steps that produce or transform data and feed into the network. I'm training a neural network but the training loss doesn't decrease. Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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. (No, It Is Not About Internal Covariate Shift). Make sure you're minimizing the loss function, Make sure your loss is computed correctly. 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. 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 channel order for RGB images? Staging Ground Beta 1 Recap, and Reviewers needed for Beta 2, multi-variable linear regression with pytorch, PyTorch path generation with RNN - confusion with input, output, hidden and batch sizes, Pytorch GRU error RuntimeError : size mismatch, m1: [1600 x 3], m2: [50 x 20], CNN -> LSTM cascaded models to PyTorch Lightning. oytungunes Asks: Validation Loss does not decrease in LSTM? Is it possible to share more info and possibly some code? 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. Making sure that your model can overfit is an excellent idea. Before combining $f(\mathbf x)$ with several other layers, generate a random target vector $\mathbf y \in \mathbb R^k$. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. How can this new ban on drag possibly be considered constitutional? In the context of recent research studying the difficulty of training in the presence of non-convex training criteria 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. Go back to point 1 because the results aren't good. Data normalization and standardization in neural networks. Instead, I do that in a configuration file (e.g., JSON) that is read and used to populate network configuration details at runtime. The suggestions for randomization tests are really great ways to get at bugged networks. There are two features of neural networks that make verification even more important than for other types of machine learning or statistical models. How can change in cost function be positive? Curriculum learning is a formalization of @h22's answer. Is this drop in training accuracy due to a statistical or programming error? Styling contours by colour and by line thickness in QGIS. neural-network - PytorchRNN - normalize or standardize the data in some way. Some common mistakes here are. For example you could try dropout of 0.5 and so on. 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). The problem I find is that the models, for various hyperparameters I try (e.g. 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 validation loss < training loss and validation accuracy < training accuracy, Keras stateful LSTM returns NaN for validation loss, Validation loss keeps fluctuating about training loss, Validation loss is lower than the training loss, Understanding output of LSTM for regression, Understanding Training and Test Loss Plots, Understanding LSTM Training and Validation Graph and their metrics (LSTM Keras), Validation loss much higher than training loss, LSTM RNN regression: validation loss erratic during training. I am so used to thinking about overfitting as a weakness that I never explicitly thought (until you mentioned it) that the. 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. 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. Likely a problem with the data? First, build a small network with a single hidden layer and verify that it works correctly. Be advised that validation, as it is calculated at the end of each epoch, uses the "best" machine trained in that epoch (that is, the last one, but if constant improvement is the case then the last weights should yield the best results - at least for training loss, if not for validation), while the train loss is calculated as an average of the performance per each epoch. I had this issue - while training loss was decreasing, the validation loss was not decreasing. 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. 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. This is a good addition. Thanks for contributing an answer to Data Science Stack Exchange! 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. My model look like this: And here is the function for each training sample. Asking for help, clarification, or responding to other answers. Minimising the environmental effects of my dyson brain. I had this issue - while training loss was decreasing, the validation loss was not decreasing. 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. Residual connections can improve deep feed-forward networks. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. . and i used keras framework to build the network, but it seems the NN can't be build up easily. Sometimes, networks simply won't reduce the loss if the data isn't scaled. This is especially useful for checking that your data is correctly normalized. What video game is Charlie playing in Poker Face S01E07? rev2023.3.3.43278. On the same dataset a simple averaged sentence embedding gets f1 of .75, while an LSTM is a flip of a coin. This is achieved by including in the training phase simultaneously (i) physical dependencies between. $f(\mathbf x) = \alpha(\mathbf W \mathbf x + \mathbf b)$, $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$, $\mathbf y = \begin{bmatrix}1 & 0 & 0 & \cdots & 0\end{bmatrix}$. Model compelxity: Check if the model is too complex. If this works, train it on two inputs with different outputs. What could cause this? 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. This step is not as trivial as people usually assume it to be. 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? 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. It only takes a minute to sign up. 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." Linear Algebra - Linear transformation question, ERROR: CREATE MATERIALIZED VIEW WITH DATA cannot be executed from a function. For me, the validation loss also never decreases. Why does momentum escape from a saddle point in this famous image? How to tell which packages are held back due to phased updates. The best answers are voted up and rise to the top, Not the answer you're looking for? It just stucks at random chance of particular result with no loss improvement during training. Just want to add on one technique haven't been discussed yet. You can easily (and quickly) query internal model layers and see if you've setup your graph correctly. Tuning configuration choices is not really as simple as saying that one kind of configuration choice (e.g. Aren't my iterations needed to train NN for XOR with MSE < 0.001 too high? To make sure the existing knowledge is not lost, reduce the set learning rate. Just at the end adjust the training and the validation size to get the best result in the test set. Styling contours by colour and by line thickness in QGIS. Neural networks and other forms of ML are "so hot right now". 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. Find centralized, trusted content and collaborate around the technologies you use most. 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. Solutions to this are to decrease your network size, or to increase dropout. So this would tell you if your initialization is bad. 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. Why do many companies reject expired SSL certificates as bugs in bug bounties? What is the essential difference between neural network and linear regression. How to handle hidden-cell output of 2-layer LSTM in PyTorch? How can I fix this? 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). One caution about ReLUs is the "dead neuron" phenomenon, which can stymie learning; leaky relus and similar variants avoid this problem. All of these topics are active areas of research. How to interpret intermitent decrease of loss? keras lstm loss-function accuracy Share Improve this question It also hedges against mistakenly repeating the same dead-end experiment. That probably did fix wrong activation method. ), @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. These bugs might even be the insidious kind for which the network will train, but get stuck at a sub-optimal solution, or the resulting network does not have the desired architecture. Too few neurons in a layer can restrict the representation that the network learns, causing under-fitting. But some recent research has found that SGD with momentum can out-perform adaptive gradient methods for neural networks. If I make any parameter modification, I make a new configuration file. Might be an interesting experiment. Some examples: When it first came out, the Adam optimizer generated a lot of interest. train.py model.py python. 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. I knew a good part of this stuff, what stood out for me is. 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. This tactic can pinpoint where some regularization might be poorly set. 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. 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? Don't Overfit! How to prevent Overfitting in your Deep Learning I never had to get here, but if you're using BatchNorm, you would expect approximately standard normal distributions. 6) Standardize your Preprocessing and Package Versions. ncdu: What's going on with this second size column? Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Accuracy on training dataset was always okay. How to handle a hobby that makes income in US. 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. 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. What am I doing wrong here in the PlotLegends specification? 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. The funny thing is that they're half right: coding, It is really nice answer. Or the other way around? For example, it's widely observed that layer normalization and dropout are difficult to use together. 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. Dealing with such a Model: Data Preprocessing: Standardizing and Normalizing the data.