lstm validation loss not decreasing

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. 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. What is happening? If the training algorithm is not suitable you should have the same problems even without the validation or dropout. Short story taking place on a toroidal planet or moon involving flying. 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. How to interpret the neural network model when validation accuracy Deep learning is all the rage these days, and networks with a large number of layers have shown impressive results. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? rev2023.3.3.43278. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? Why does $[0,1]$ scaling dramatically increase training time for feed forward ANN (1 hidden layer)? And the loss in the training looks like this: Is there anything wrong with these codes? How do you ensure that a red herring doesn't violate Chekhov's gun? 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. And when the training rounds are after 30 times validation loss and test loss tend to be stable after 30 training . Additionally, the validation loss is measured after each epoch. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? If I make any parameter modification, I make a new configuration file. It might also be possible that you will see overfit if you invest more epochs into the training. To learn more, see our tips on writing great answers. Learn more about Stack Overflow the company, and our products. A recent result has found that ReLU (or similar) units tend to work better because the have steeper gradients, so updates can be applied quickly. Large non-decreasing LSTM training loss - PyTorch Forums I'll let you decide. Problem is I do not understand what's going on here. I have two stacked LSTMS as follows (on Keras): Train on 127803 samples, validate on 31951 samples. You need to test all of the steps that produce or transform data and feed into the network. In the Machine Learning Course by Andrew Ng, he suggests running Gradient Checking in the first few iterations to make sure the backpropagation is doing the right thing. If the model isn't learning, there is a decent chance that your backpropagation is not working. What is the best question generation state of art with nlp? The best answers are voted up and rise to the top, Not the answer you're looking for? Redoing the align environment with a specific formatting. 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. I'm training a neural network but the training loss doesn't decrease. How does the Adam method of stochastic gradient descent work? In all other cases, the optimization problem is non-convex, and non-convex optimization is hard. I struggled for a while with such a model, and when I tried a simpler version, I found out that one of the layers wasn't being masked properly due to a keras bug. @Alex R. I'm still unsure what to do if you do pass the overfitting test. For example you could try dropout of 0.5 and so on. Training loss goes up and down regularly. neural-network - PytorchRNN - 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. Your learning rate could be to big after the 25th epoch. To learn more, see our tips on writing great answers. I never had to get here, but if you're using BatchNorm, you would expect approximately standard normal distributions. As an example, two popular image loading packages are cv2 and PIL. The experiments show that significant improvements in generalization can be achieved. "FaceNet: A Unified Embedding for Face Recognition and Clustering" Florian Schroff, Dmitry Kalenichenko, James Philbin. Initialization over too-large an interval can set initial weights too large, meaning that single neurons have an outsize influence over the network behavior. It just stucks at random chance of particular result with no loss improvement during training. ncdu: What's going on with this second size column? For example, it's widely observed that layer normalization and dropout are difficult to use together. Thanks a bunch for your insight! My training loss goes down and then up again. 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. Nowadays, many frameworks have built in data pre-processing pipeline and augmentation. 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! ", As an example, I wanted to learn about LSTM language models, so I decided to make a Twitter bot that writes new tweets in response to other Twitter users. Lol. You have to check that your code is free of bugs before you can tune network performance! We can then generate a similar target to aim for, rather than a random one. train the neural network, while at the same time controlling the loss on the validation set. This is an easier task, so the model learns a good initialization before training on the real task. The funny thing is that they're half right: coding, It is really nice answer. Is there a proper earth ground point in this switch box? Dropout is used during testing, instead of only being used for training. In particular, you should reach the random chance loss on the test set. To learn more, see our tips on writing great answers. Training loss goes down and up again. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Now I'm working on it. There are 252 buckets. What's the best way to answer "my neural network doesn't work, please fix" questions? 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). Is it possible to create a concave light? Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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 . On the same dataset a simple averaged sentence embedding gets f1 of .75, while an LSTM is a flip of a coin. What could cause this? 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. any suggestions would be appreciated. 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. So this does not explain why you do not see overfit. If this trains correctly on your data, at least you know that there are no glaring issues in the data set. Then incrementally add additional model complexity, and verify that each of those works as well. (See: Why do we use ReLU in neural networks and how do we use it?) I simplified the model - instead of 20 layers, I opted for 8 layers. Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? 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. 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. Thanks @Roni. Or the other way around? This Medium post, "How to unit test machine learning code," by Chase Roberts discusses unit-testing for machine learning models in more detail. Styling contours by colour and by line thickness in QGIS. Accuracy (0-1 loss) is a crappy metric if you have strong class imbalance. 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. I regret that I left it out of my 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. 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. (The author is also inconsistent about using single- or double-quotes but that's purely stylistic. 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. How to use Learning Curves to Diagnose Machine Learning Model The first step when dealing with overfitting is to decrease the complexity of the model. Thanks for contributing an answer to Stack Overflow! These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks. as a particular form of continuation method (a general strategy for global optimization of non-convex functions). 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. +1, but "bloody Jupyter Notebook"? Sometimes, networks simply won't reduce the loss if the data isn't scaled. Find centralized, trusted content and collaborate around the technologies you use most. 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. 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. Asking for help, clarification, or responding to other answers. We design a new algorithm, called Partially adaptive momentum estimation method (Padam), which unifies the Adam/Amsgrad with SGD to achieve the best from both worlds. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? 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. Replacing broken pins/legs on a DIP IC package. vegan) just to try it, does this inconvenience the caterers and staff? I am training an LSTM to give counts of the number of items in buckets. 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. However I don't get any sensible values for accuracy. This step is not as trivial as people usually assume it to be. What is the purpose of this D-shaped ring at the base of the tongue on my hiking boots? 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. One caution about ReLUs is the "dead neuron" phenomenon, which can stymie learning; leaky relus and similar variants avoid this problem. $L^2$ regularization (aka weight decay) or $L^1$ regularization is set too large, so the weights can't move. This is a very active area of research. 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. I'm not asking about overfitting or regularization. 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). Before combining $f(\mathbf x)$ with several other layers, generate a random target vector $\mathbf y \in \mathbb R^k$. A place where magic is studied and practiced? Basically, the idea is to calculate the derivative by defining two points with a $\epsilon$ interval. Go back to point 1 because the results aren't good. First one is a simplest one. 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. Other networks will decrease the loss, but only very slowly. Why do many companies reject expired SSL certificates as bugs in bug bounties? How to handle a hobby that makes income in US. What is a word for the arcane equivalent of a monastery? However, I am running into an issue with very large MSELoss that does not decrease in training (meaning essentially my network is not training). Although it can easily overfit to a single image, it can't fit to a large dataset, despite good normalization and shuffling. 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. 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. And these elements may completely destroy the data. But adding too many hidden layers can make risk overfitting or make it very hard to optimize the network. Here, we formalize such training strategies in the context of machine learning, and call them curriculum learning. try different optimizers: SGD trains slower, but it leads to a lower generalization error, while Adam trains faster, but the test loss stalls to a higher value, increase the learning rate initially, and then decay it, or use. Switch the LSTM to return predictions at each step (in keras, this is return_sequences=True). 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. 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. Dealing with such a Model: Data Preprocessing: Standardizing and Normalizing the data. If this doesn't happen, there's a bug in your code. Is your data source amenable to specialized network architectures? So this would tell you if your initialization is bad. Use MathJax to format equations. :). 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.). I'm asking about how to solve the problem where my network's performance doesn't improve on the training set. I just learned this lesson recently and I think it is interesting to share. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". For cripes' sake, get a real IDE such as PyCharm or VisualStudio Code and create a well-structured code, rather than cooking up a Notebook! 6) Standardize your Preprocessing and Package Versions. But how could extra training make the training data loss bigger? 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. Training loss decreasing while Validation loss is not decreasing (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. The network initialization is often overlooked as a source of neural network bugs. Neural networks and other forms of ML are "so hot right now". . Thanks. Is it possible to rotate a window 90 degrees if it has the same length and width? 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. In my case the initial training set was probably too difficult for the network, so it was not making any progress. Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. 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. What should I do? 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. Accuracy on training dataset was always okay. Two parts of regularization are in conflict. The differences are usually really small, but you'll occasionally see drops in model performance due to this kind of stuff. The validation loss slightly increase such as from 0.016 to 0.018. the opposite test: you keep the full training set, but you shuffle the labels. The suggestions for randomization tests are really great ways to get at bugged networks. Hence validation accuracy also stays at same level but training accuracy goes up. It only takes a minute to sign up. Specifically for triplet-loss models, there are a number of tricks which can improve training time and generalization. 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). Reiterate ad nauseam. Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? Learn more about Stack Overflow the company, and our products. Thanks for contributing an answer to Cross Validated! Curriculum learning is a formalization of @h22's answer. 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. 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 can help make sure that inputs/outputs are properly normalized in each layer. First, it quickly shows you that your model is able to learn by checking if your model can overfit your data. Thanks for contributing an answer to Data Science Stack Exchange! Instead, make a batch of fake data (same shape), and break your model down into components. The scale of the data can make an enormous difference on training. The order in which the training set is fed to the net during training may have an effect. It also hedges against mistakenly repeating the same dead-end experiment. 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.