keras - Understanding LSTM behaviour: Validation loss smaller than I understand that it might not be feasible, but very often data size is the key to success. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Since either on its own is very useful, understanding how to use both is an active area of research. If this works, train it on two inputs with different outputs. self.rnn = nn.RNNinput_size = input_sizehidden_ size = hidden_ sizebatch_first = TrueNameError'input_size'. Why do we use ReLU in neural networks and how do we use it? I edited my original post to accomodate your input and some information about my loss/acc values. Conceptually this means that your output is heavily saturated, for example toward 0. 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. Towards a Theoretical Understanding of Batch Normalization, How Does Batch Normalization Help Optimization? +1 for "All coding is debugging". Please help me. What can be the actions to decrease? 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. 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. I regret that I left it out of my answer. This is achieved by including in the training phase simultaneously (i) physical dependencies between. Is this drop in training accuracy due to a statistical or programming error? Minimising the environmental effects of my dyson brain. My recent lesson is trying to detect if an image contains some hidden information, by stenography tools. A typical trick to verify that is to manually mutate some labels. Thanks for contributing an answer to Data Science Stack Exchange! Problem is I do not understand what's going on here. In all other cases, the optimization problem is non-convex, and non-convex optimization is hard. For an example of such an approach you can have a look at my experiment. A place where magic is studied and practiced? 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 just attributed that to a poor choice for the accuracy-metric and haven't given it much thought. Then I add each regularization piece back, and verify that each of those works along the way. 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. I am training a LSTM model to do question answering, i.e. 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. I don't know why that is. It might also be possible that you will see overfit if you invest more epochs into the training. This is called unit testing. While this is highly dependent on the availability of data. Check that the normalized data are really normalized (have a look at their range). 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? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. When resizing an image, what interpolation do they use? However training as well as validation loss pretty much converge to zero, so I guess we can conclude that the problem is to easy because training and validation data are generated in exactly the same way. Why this happening and how can I fix it? This will avoid gradient issues for saturated sigmoids, at the output. I am getting different values for the loss function per epoch. Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary. Where $a$ is your learning rate, $t$ is your iteration number and $m$ is a coefficient that identifies learning rate decreasing speed. For programmers (or at least data scientists) the expression could be re-phrased as "All coding is debugging.". I get NaN values for train/val loss and therefore 0.0% accuracy. Why are physically impossible and logically impossible concepts considered separate in terms of probability? But some recent research has found that SGD with momentum can out-perform adaptive gradient methods for neural networks. 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. The training loss should now decrease, but the test loss may increase. Does Counterspell prevent from any further spells being cast on a given turn? Check the data pre-processing and augmentation. Writing good unit tests is a key piece of becoming a good statistician/data scientist/machine learning expert/neural network practitioner. The second one is to decrease your learning rate monotonically. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? What's the difference between a power rail and a signal line? 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. 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. Is it possible to rotate a window 90 degrees if it has the same length and width? The experiments show that significant improvements in generalization can be achieved. and all you will be able to do is shrug your shoulders. pixel values are in [0,1] instead of [0, 255]). Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? Continuing the binary example, if your data is 30% 0's and 70% 1's, then your intial expected loss around $L=-0.3\ln(0.5)-0.7\ln(0.5)\approx 0.7$. learning rate) is more or less important than another (e.g. How to match a specific column position till the end of line? 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. LSTM training loss does not decrease - nlp - PyTorch Forums 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. How do you ensure that a red herring doesn't violate Chekhov's gun? The best answers are voted up and rise to the top, Not the answer you're looking for? To learn more, see our tips on writing great answers. Is there a solution if you can't find more data, or is an RNN just the wrong model? +1, but "bloody Jupyter Notebook"? thanks, I will try increasing my training set size, I was actually trying to reduce the number of hidden units but to no avail, thanks for pointing out! I am so used to thinking about overfitting as a weakness that I never explicitly thought (until you mentioned it) that the. 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 The main point is that the error rate will be lower in some point in time. Too few neurons in a layer can restrict the representation that the network learns, causing under-fitting. Has 90% of ice around Antarctica disappeared in less than a decade? A standard neural network is composed of layers. This tactic can pinpoint where some regularization might be poorly set. 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. Find centralized, trusted content and collaborate around the technologies you use most. One way for implementing curriculum learning is to rank the training examples by difficulty. I used to think that this was a set-and-forget parameter, typically at 1.0, but I found that I could make an LSTM language model dramatically better by setting it to 0.25. LSTM Training loss decreases and increases, Sequence lengths in LSTM / BiLSTMs and overfitting, Why does the loss/accuracy fluctuate during the training? If it is indeed memorizing, the best practice is to collect a larger dataset. Is it suspicious or odd to stand by the gate of a GA airport watching the planes? Dropout is used during testing, instead of only being used for training. It took about a year, and I iterated over about 150 different models before getting to a model that did what I wanted: generate new English-language text that (sort of) makes sense. Connect and share knowledge within a single location that is structured and easy to search. Increase the size of your model (either number of layers or the raw number of neurons per layer) . Thanks for contributing an answer to Stack Overflow! Many of the different operations are not actually used because previous results are over-written with new variables. 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. For example, it's widely observed that layer normalization and dropout are difficult to use together. Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by the magnitude of one order If this doesn't happen, there's a bug in your code. See: In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. $$. Are there tables of wastage rates for different fruit and veg? Predictions are more or less ok here. hidden units). (which could be considered as some kind of testing). 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). padding them with data to make them equal length), the LSTM is correctly ignoring your masked data. Initialization over too-large an interval can set initial weights too large, meaning that single neurons have an outsize influence over the network behavior. 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. Making statements based on opinion; back them up with references or personal experience. 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. Neural networks in particular are extremely sensitive to small changes in your data. 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. Why is it hard to train deep neural networks? Why is this sentence from The Great Gatsby grammatical? I keep all of these configuration files. 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. Thank you for informing me regarding your experiment. Of course, this can be cumbersome. What video game is Charlie playing in Poker Face S01E07? This usually happens when your neural network weights aren't properly balanced, especially closer to the softmax/sigmoid. The network initialization is often overlooked as a source of neural network bugs. But adding too many hidden layers can make risk overfitting or make it very hard to optimize the network. ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. It only takes a minute to sign up. MathJax reference. Testing on a single data point is a really great idea. 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). This can help make sure that inputs/outputs are properly normalized in each layer. 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. What could cause this? This is a good addition. The problem turns out to be the misunderstanding of the batch size and other features that defining an nn.LSTM. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? 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). How to match a specific column position till the end of line? So this does not explain why you do not see overfit. \alpha(t + 1) = \frac{\alpha(0)}{1 + \frac{t}{m}} Do I need a thermal expansion tank if I already have a pressure tank? Is it possible to create a concave light? First one is a simplest one. Additionally, the validation loss is measured after each epoch. And these elements may completely destroy the data. (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. I provide an example of this in the context of the XOR problem here: Aren't my iterations needed to train NN for XOR with MSE < 0.001 too high?. This is a non-exhaustive list of the configuration options which are not also regularization options or numerical optimization options. 3) Generalize your model outputs to debug. But why is it better? Wide and deep neural networks, and neural networks with exotic wiring, are the Hot Thing right now in machine learning. However I don't get any sensible values for accuracy. And struggled for a long time that the model does not learn. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. ncdu: What's going on with this second size column? Training loss goes down and up again. Linear Algebra - Linear transformation question, ERROR: CREATE MATERIALIZED VIEW WITH DATA cannot be executed from a function. I'm possibly being too negative, but frankly I've had enough with people cloning Jupyter Notebooks from GitHub, thinking it would be a matter of minutes to adapt the code to their use case and then coming to me complaining that nothing works. 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. 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. (For example, the code may seem to work when it's not correctly implemented. 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. If you haven't done so, you may consider to work with some benchmark dataset like SQuAD A similar phenomenon also arises in another context, with a different solution. The differences are usually really small, but you'll occasionally see drops in model performance due to this kind of stuff. Designing a better optimizer is very much an active area of research. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Asking for help, clarification, or responding to other answers. (+1) This is a good write-up. I have prepared the easier set, selecting cases where differences between categories were seen by my own perception as more obvious. For example you could try dropout of 0.5 and so on. (One key sticking point, and part of the reason that it took so many attempts, is that it was not sufficient to simply get a low out-of-sample loss, since early low-loss models had managed to memorize the training data, so it was just reproducing germane blocks of text verbatim in reply to prompts -- it took some tweaking to make the model more spontaneous and still have low loss.). Keras also allows you to specify a separate validation dataset while fitting your model that can also be evaluated using the same loss and metrics. What is happening? Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Neural networks are not "off-the-shelf" algorithms in the way that random forest or logistic regression are. 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. You just need to set up a smaller value for your learning rate. Asking for help, clarification, or responding to other answers. 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. Try to set up it smaller and check your loss again. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Just at the end adjust the training and the validation size to get the best result in the test set. It become true that I was doing regression with ReLU last activation layer, which is obviously wrong. Should I put my dog down to help the homeless? Maybe in your example, you only care about the latest prediction, so your LSTM outputs a single value and not a sequence. Double check your input data. This informs us as to whether the model needs further tuning or adjustments or not.