lstm validation loss not decreasing

Please help me. Ok, rereading your code I can obviously see that you are correct; I will edit my answer. In the context of recent research studying the difficulty of training in the presence of non-convex training criteria The reason that I'm so obsessive about retaining old results is that this makes it very easy to go back and review previous experiments. I had this issue - while training loss was decreasing, the validation loss was not decreasing. history = model.fit(X, Y, epochs=100, validation_split=0.33) I am so used to thinking about overfitting as a weakness that I never explicitly thought (until you mentioned it) that the. Learn more about Stack Overflow the company, and our products. . if you're getting some error at training time, update your CV and start looking for a different job :-). 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. pixel values are in [0,1] instead of [0, 255]). 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). A lot of times you'll see an initial loss of something ridiculous, like 6.5. Try to set up it smaller and check your loss again. Thanks a bunch for your insight! 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. This will avoid gradient issues for saturated sigmoids, at the output. remove regularization gradually (maybe switch batch norm for a few layers). Prior to presenting data to a neural network. This will help you make sure that your model structure is correct and that there are no extraneous issues. What's the difference between a power rail and a signal line? Why is this the case? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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. For an example of such an approach you can have a look at my experiment. rev2023.3.3.43278. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? 12 that validation loss and test loss keep decreasing when the training rounds are before 30 times. 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. As an example, two popular image loading packages are cv2 and PIL. Asking for help, clarification, or responding to other answers. What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? The problem turns out to be the misunderstanding of the batch size and other features that defining an nn.LSTM. 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. (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). 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. 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." Connect and share knowledge within a single location that is structured and easy to search. . Fighting the good fight. Too many neurons can cause over-fitting because the network will "memorize" the training data. Sometimes, networks simply won't reduce the loss if the data isn't scaled. If your neural network does not generalize well, see: What should I do when my neural network doesn't generalize well? 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. padding them with data to make them equal length), the LSTM is correctly ignoring your masked data. That probably did fix wrong activation method. 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. 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. It can also catch buggy activations. What am I doing wrong here in the PlotLegends specification? (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.). What are "volatile" learning curves indicative of? 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. What's the difference between a power rail and a signal line? Comprehensive list of activation functions in neural networks with pros/cons, "Deep Residual Learning for Image Recognition", Identity Mappings in Deep Residual Networks. However I don't get any sensible values for accuracy. 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. 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%. Here's an example of a question where the problem appears to be one of model configuration or hyperparameter choice, but actually the problem was a subtle bug in how gradients were computed. (See: Why do we use ReLU in neural networks and how do we use it?) The best answers are voted up and rise to the top, Not the answer you're looking for? Then I add each regularization piece back, and verify that each of those works along the way. In one example, I use 2 answers, one correct answer and one wrong answer. Some common mistakes here are. While this is highly dependent on the availability of data. I couldn't obtained a good validation loss as my training loss was decreasing. 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. Connect and share knowledge within a single location that is structured and easy to search. Before I was knowing that this is wrong, I did add Batch Normalisation layer after every learnable layer, and that helps. Is it possible to rotate a window 90 degrees if it has the same length and width? 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. There are two features of neural networks that make verification even more important than for other types of machine learning or statistical models. If I make any parameter modification, I make a new configuration file. If the label you are trying to predict is independent from your features, then it is likely that the training loss will have a hard time reducing. The best answers are voted up and rise to the top, Not the answer you're looking for? This means writing code, and writing code means debugging. (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 slightly increase such as from 0.016 to 0.018. However, at the time that your network is struggling to decrease the loss on the training data -- when the network is not learning -- regularization can obscure what the problem is. I simplified the model - instead of 20 layers, I opted for 8 layers. Even when a neural network code executes without raising an exception, the network can still have bugs! For example $-0.3\ln(0.99)-0.7\ln(0.01) = 3.2$, so if you're seeing a loss that's bigger than 1, it's likely your model is very skewed. 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? Towards a Theoretical Understanding of Batch Normalization, How Does Batch Normalization Help Optimization? I added more features, which I thought intuitively would add some new intelligent information to the X->y pair. We can then generate a similar target to aim for, rather than a random one. 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. LSTM Training loss decreases and increases, Sequence lengths in LSTM / BiLSTMs and overfitting, Why does the loss/accuracy fluctuate during the training? Multi-layer perceptron vs deep neural network, My neural network can't even learn Euclidean distance. 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. Replacing broken pins/legs on a DIP IC package. 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 Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? 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. This is an easier task, so the model learns a good initialization before training on the real task. 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). See if you inverted the training set and test set labels, for example (happened to me once -___-), or if you imported the wrong file. However, I am running into an issue with very large MSELoss that does not decrease in training (meaning essentially my network is not training). In particular, you should reach the random chance loss on the test set. Short story taking place on a toroidal planet or moon involving flying. Minimising the environmental effects of my dyson brain. How to match a specific column position till the end of line? 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"? A similar phenomenon also arises in another context, with a different solution. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. $L^2$ regularization (aka weight decay) or $L^1$ regularization is set too large, so the weights can't move. I edited my original post to accomodate your input and some information about my loss/acc values. Why do many companies reject expired SSL certificates as bugs in bug bounties? On the same dataset a simple averaged sentence embedding gets f1 of .75, while an LSTM is a flip of a coin. 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. So if you're downloading someone's model from github, pay close attention to their preprocessing. Maybe in your example, you only care about the latest prediction, so your LSTM outputs a single value and not a sequence. Does a summoned creature play immediately after being summoned by a ready action? Is it possible to create a concave light? What could cause this? 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). Check that the normalized data are really normalized (have a look at their range). What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? I am training an LSTM to give counts of the number of items in buckets. This problem is easy to identify. Is your data source amenable to specialized network architectures? What's the channel order for RGB images? 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. The cross-validation loss tracks the training loss. Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. I keep all of these configuration files. 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). One way for implementing curriculum learning is to rank the training examples by difficulty. Can I tell police to wait and call a lawyer when served with a search warrant? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. number of hidden units, LSTM or GRU) the training loss decreases, but the validation loss stays quite high (I use dropout, the rate I use is 0.5), e.g. The essential idea of curriculum learning is best described in the abstract of the previously linked paper by Bengio et al. 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. This can help make sure that inputs/outputs are properly normalized in each layer. This is called unit testing. ncdu: What's going on with this second size column? Okay, so this explains why the validation score is not worse. But some recent research has found that SGD with momentum can out-perform adaptive gradient methods for neural networks. What is the purpose of this D-shaped ring at the base of the tongue on my hiking boots? However I'd still like to understand what's going on, as I see similar behavior of the loss in my real problem but there the predictions are rubbish. Why is this sentence from The Great Gatsby grammatical? train the neural network, while at the same time controlling the loss on the validation set. One caution about ReLUs is the "dead neuron" phenomenon, which can stymie learning; leaky relus and similar variants avoid this problem. How do you ensure that a red herring doesn't violate Chekhov's gun? Is this drop in training accuracy due to a statistical or programming error? It might also be possible that you will see overfit if you invest more epochs into the training. What am I doing wrong here in the PlotLegends specification? What's the best way to answer "my neural network doesn't work, please fix" questions? This informs us as to whether the model needs further tuning or adjustments or not. "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. The difference between the phonemes /p/ and /b/ in Japanese, Short story taking place on a toroidal planet or moon involving flying. 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. I'm training a neural network but the training loss doesn't decrease. and all you will be able to do is shrug your shoulders. Just by virtue of opening a JPEG, both these packages will produce slightly different images. Predictions are more or less ok here. Do new devs get fired if they can't solve a certain bug? LSTM neural network is a kind of temporal recurrent neural network (RNN), whose core is the gating unit. You need to test all of the steps that produce or transform data and feed into the network. See: Comprehensive list of activation functions in neural networks with pros/cons. Training loss goes up and down regularly. Build unit tests. 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. Recurrent neural networks can do well on sequential data types, such as natural language or time series data. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Check the accuracy on the test set, and make some diagnostic plots/tables. How to match a specific column position till the end of line? 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. with two problems ("How do I get learning to continue after a certain epoch?" 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. MathJax reference. And these elements may completely destroy the data. What could cause my neural network model's loss increases dramatically? To make sure the existing knowledge is not lost, reduce the set learning rate. 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. I'm not asking about overfitting or regularization. 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. self.rnn = nn.RNNinput_size = input_sizehidden_ size = hidden_ sizebatch_first = TrueNameError'input_size'. 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. (which could be considered as some kind of testing). Some examples are. What is happening? Neural networks and other forms of ML are "so hot right now". Thanks for contributing an answer to Cross Validated! What is a word for the arcane equivalent of a monastery? 2 Usually when a model overfits, validation loss goes up and training loss goes down from the point of overfitting. 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.).

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