+1 for "All coding is debugging". As you commented, this in not the case here, you generate the data only once. The best answers are voted up and rise to the top, Not the answer you're looking for? What is the purpose of this D-shaped ring at the base of the tongue on my hiking boots? "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. Try a random shuffle of the training set (without breaking the association between inputs and outputs) and see if the training loss goes down. The challenges of training neural networks are well-known (see: Why is it hard to train deep neural networks?). as a particular form of continuation method (a general strategy for global optimization of non-convex functions). I am runnning LSTM for classification task, and my validation loss does not decrease. Set up a very small step and train it. Ok, rereading your code I can obviously see that you are correct; I will edit my answer. We can then generate a similar target to aim for, rather than a random one. Why does momentum escape from a saddle point in this famous image? I think what you said must be on the right track. keras lstm loss-function accuracy Share Improve this question (For example, the code may seem to work when it's not correctly implemented. 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. In theory then, using Docker along with the same GPU as on your training system should then produce the same results. oytungunes Asks: Validation Loss does not decrease in LSTM? 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? LSTM neural network is a kind of temporal recurrent neural network (RNN), whose core is the gating unit. How can change in cost function be positive? We've added a "Necessary cookies only" option to the cookie consent popup. (No, It Is Not About Internal Covariate Shift). Comprehensive list of activation functions in neural networks with pros/cons, "Deep Residual Learning for Image Recognition", Identity Mappings in Deep Residual Networks. Learning . What could cause this? rev2023.3.3.43278. In cases in which training as well as validation examples are generated de novo, the network is not presented with the same examples over and over. history = model.fit(X, Y, epochs=100, validation_split=0.33) 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. 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. Learn more about Stack Overflow the company, and our products. What should I do? The network picked this simplified case well. But for my case, training loss still goes down but validation loss stays at same level. Double check your input data. Please help me. 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! All of these topics are active areas of research. Often the simpler forms of regression get overlooked. 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 . And when the training rounds are after 30 times validation loss and test loss tend to be stable after 30 training . Why this happening and how can I fix it? Might be an interesting experiment. If you haven't done so, you may consider to work with some benchmark dataset like SQuAD 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. 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. 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). 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. Pytorch. 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 . It means that your step will minimise by a factor of two when $t$ is equal to $m$. In one example, I use 2 answers, one correct answer and one wrong answer. Find centralized, trusted content and collaborate around the technologies you use most. First, build a small network with a single hidden layer and verify that it works correctly. Towards a Theoretical Understanding of Batch Normalization, How Does Batch Normalization Help Optimization? The safest way of standardizing packages is to use a requirements.txt file that outlines all your packages just like on your training system setup, down to the keras==2.1.5 version numbers. Connect and share knowledge within a single location that is structured and easy to search. Can I add data, that my neural network classified, to the training set, in order to improve it? This is achieved by including in the training phase simultaneously (i) physical dependencies between. Can I tell police to wait and call a lawyer when served with a search warrant? I'm not asking about overfitting or regularization. Replacing broken pins/legs on a DIP IC package. How to Diagnose Overfitting and Underfitting of LSTM Models Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? 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. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. How to react to a students panic attack in an oral exam? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. To learn more, see our tips on writing great answers. Do new devs get fired if they can't solve a certain bug? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. ", 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. To learn more, see our tips on writing great answers. What's the difference between a power rail and a signal line? What am I doing wrong here in the PlotLegends specification? Dropout is used during testing, instead of only being used for training. ), @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. Reiterate ad nauseam. 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). I then pass the answers through an LSTM to get a representation (50 units) of the same length for answers. This usually happens when your neural network weights aren't properly balanced, especially closer to the softmax/sigmoid. Aren't my iterations needed to train NN for XOR with MSE < 0.001 too high? The key difference between a neural network and a regression model is that a neural network is a composition of many nonlinear functions, called activation functions. Thank you for informing me regarding your experiment. 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. (+1) This is a good write-up. But how could extra training make the training data loss bigger? Training accuracy is ~97% but validation accuracy is stuck at ~40%. This means that if you have 1000 classes, you should reach an accuracy of 0.1%. 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. 2 Usually when a model overfits, validation loss goes up and training loss goes down from the point of overfitting. Solutions to this are to decrease your network size, or to increase dropout. Curriculum learning is a formalization of @h22's answer. Connect and share knowledge within a single location that is structured and easy to search. But why is it better? 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. +1 Learning like children, starting with simple examples, not being given everything at once! If the loss decreases consistently, then this check has passed. The differences are usually really small, but you'll occasionally see drops in model performance due to this kind of stuff. I knew a good part of this stuff, what stood out for me is. Continuing the binary example, if your data is 30% 0's and 70% 1's, then your intial expected loss around $L=-0.3\ln(0.5)-0.7\ln(0.5)\approx 0.7$. Linear Algebra - Linear transformation question, ERROR: CREATE MATERIALIZED VIEW WITH DATA cannot be executed from a function. Learn more about Stack Overflow the company, and our products. The best answers are voted up and rise to the top, Not the answer you're looking for? Some common mistakes here are. This will avoid gradient issues for saturated sigmoids, at the output. Also, when it comes to explaining your model, someone will come along and ask "what's the effect of $x_k$ on the result?" It only takes a minute to sign up. Weight changes but performance remains the same. Redoing the align environment with a specific formatting. Decrease the initial learning rate using the 'InitialLearnRate' option of trainingOptions. Why do we use ReLU in neural networks and how do we use it? 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. Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? 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. Loss is still decreasing at the end of training. any suggestions would be appreciated. Instead, I do that in a configuration file (e.g., JSON) that is read and used to populate network configuration details at runtime. But these networks didn't spring fully-formed into existence; their designers built up to them from smaller units. if you're getting some error at training time, update your CV and start looking for a different job :-). If this trains correctly on your data, at least you know that there are no glaring issues in the data set. Choosing the number of hidden layers lets the network learn an abstraction from the raw data. or bAbI. To make sure the existing knowledge is not lost, reduce the set learning rate. (+1) Checking the initial loss is a great suggestion. Ive seen a number of NN posts where OP left a comment like oh I found a bug now it works.. The reason is many packages are rescaling images to certain size and this operation completely destroys the hidden information inside. Welcome to DataScience. 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. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. The first step when dealing with overfitting is to decrease the complexity of the model. How to tell which packages are held back due to phased updates. How Intuit democratizes AI development across teams through reusability. 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. To learn more, see our tips on writing great answers. (The author is also inconsistent about using single- or double-quotes but that's purely stylistic. To set the gradient threshold, use the 'GradientThreshold' option in trainingOptions. loss/val_loss are decreasing but accuracies are the same in LSTM! Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Nowadays, many frameworks have built in data pre-processing pipeline and augmentation. remove regularization gradually (maybe switch batch norm for a few layers). Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. If I make any parameter modification, I make a new configuration file. Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by the magnitude of one order What could cause this? A standard neural network is composed of layers. Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? In all other cases, the optimization problem is non-convex, and non-convex optimization is hard. The only way the NN can learn now is by memorising the training set, which means that the training loss will decrease very slowly, while the test loss will increase very quickly. Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. Thanks @Roni. This tactic can pinpoint where some regularization might be poorly set. 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. @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. (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. (This is an example of the difference between a syntactic and semantic error.). Validation loss is neither increasing or decreasing I agree with this answer. Then, let $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$ be a loss function. visualize the distribution of weights and biases for each layer. keras - Understanding LSTM behaviour: Validation loss smaller than Making sure that your model can overfit is an excellent idea. number of units), since all of these choices interact with all of the other choices, so one choice can do well in combination with another choice made elsewhere. Wide and deep neural networks, and neural networks with exotic wiring, are the Hot Thing right now in machine learning. with two problems ("How do I get learning to continue after a certain epoch?" I am getting different values for the loss function per epoch. On the same dataset a simple averaged sentence embedding gets f1 of .75, while an LSTM is a flip of a coin. Two parts of regularization are in conflict. Selecting a label smoothing factor for seq2seq NMT with a massive imbalanced vocabulary. How to react to a students panic attack in an oral exam? This is a very active area of research. 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. Here, we formalize such training strategies in the context of machine learning, and call them curriculum learning. Making sure the derivative is approximately matching your result from backpropagation should help in locating where is the problem. How to interpret intermitent decrease of loss? Learn more about Stack Overflow the company, and our products. There are two tests which I call Golden Tests, which are very useful to find issues in a NN which doesn't train: reduce the training set to 1 or 2 samples, and train on this. As I am fitting the model, training loss is constantly larger than validation loss, even for a balanced train/validation set (5000 samples each): In my understanding the two curves should be exactly the other way around such that training loss would be an upper bound for validation loss. Initialization over too-large an interval can set initial weights too large, meaning that single neurons have an outsize influence over the network behavior. Is this drop in training accuracy due to a statistical or programming error? Then, if you achieve a decent performance on these models (better than random guessing), you can start tuning a neural network (and @Sycorax 's answer will solve most issues). 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. If your neural network does not generalize well, see: What should I do when my neural network doesn't generalize well? This is a non-exhaustive list of the configuration options which are not also regularization options or numerical optimization options. 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." It is very weird. What should I do when my neural network doesn't learn? here is my code and my outputs: Training loss goes down and up again. What is happening? Here is a simple formula: $$ Dealing with such a Model: Data Preprocessing: Standardizing and Normalizing the data. tensorflow - Why the LSTM can't reduce the loss - Stack Overflow A lot of times you'll see an initial loss of something ridiculous, like 6.5. The training loss should now decrease, but the test loss may increase. This Medium post, "How to unit test machine learning code," by Chase Roberts discusses unit-testing for machine learning models in more detail. Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. 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. The asker was looking for "neural network doesn't learn" so I majored there. $\endgroup$ train the neural network, while at the same time controlling the loss on the validation set. Making statements based on opinion; back them up with references or personal experience. I keep all of these configuration files. Then you can take a look at your hidden-state outputs after every step and make sure they are actually different. 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. I agree with your analysis. Is it possible to share more info and possibly some code? Model compelxity: Check if the model is too complex. I just copied the code above (fixed the scaler bug) and reran it on CPU. 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. Check that the normalized data are really normalized (have a look at their range). @Alex R. I'm still unsure what to do if you do pass the overfitting test. 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. 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. Prior to presenting data to a neural network. Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? Asking for help, clarification, or responding to other answers. Do new devs get fired if they can't solve a certain bug? One way for implementing curriculum learning is to rank the training examples by difficulty. Problem is I do not understand what's going on here. 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. What is happening? I just attributed that to a poor choice for the accuracy-metric and haven't given it much thought. Minimising the environmental effects of my dyson brain. Fighting the good fight. Designing a better optimizer is very much an active area of research. . Then incrementally add additional model complexity, and verify that each of those works as well. 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. What video game is Charlie playing in Poker Face S01E07? What is going on? The difference between the phonemes /p/ and /b/ in Japanese, Short story taking place on a toroidal planet or moon involving flying. 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. 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. As an example, two popular image loading packages are cv2 and PIL. Why is this sentence from The Great Gatsby grammatical? See, There are a number of other options. Build unit tests. What's the best way to answer "my neural network doesn't work, please fix" questions? In my experience, trying to use scheduling is a lot like regex: it replaces one problem ("How do I get learning to continue after a certain epoch?") 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. The posted answers are great, and I wanted to add a few "Sanity Checks" which have greatly helped me in the past. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. What image loaders 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. This can be a source of issues. Making statements based on opinion; back them up with references or personal experience. model.py . 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). If so, how close was it? 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. Just by virtue of opening a JPEG, both these packages will produce slightly different images. I regret that I left it out of my answer. Conceptually this means that your output is heavily saturated, for example toward 0. Why do many companies reject expired SSL certificates as bugs in bug bounties? I simplified the model - instead of 20 layers, I opted for 8 layers. Using Kolmogorov complexity to measure difficulty of problems? 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. 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. Making statements based on opinion; back them up with references or personal experience. Your learning could be to big after the 25th epoch. This is a good addition. 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. So this does not explain why you do not see overfit. Where $a$ is your learning rate, $t$ is your iteration number and $m$ is a coefficient that identifies learning rate decreasing speed. How does the Adam method of stochastic gradient descent work? Even when a neural network code executes without raising an exception, the network can still have bugs! Deep learning is all the rage these days, and networks with a large number of layers have shown impressive results. What degree of difference does validation and training loss need to have to be called good fit? But some recent research has found that SGD with momentum can out-perform adaptive gradient methods for neural networks. Choosing and tuning network regularization is a key part of building a model that generalizes well (that is, a model that is not overfit to the training data). 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. nlp - Pytorch LSTM model's loss not decreasing - Stack Overflow Why does the loss/accuracy fluctuate during the training? (Keras, LSTM) 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). What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? Or the other way around? My model look like this: And here is the function for each training sample. In my case it's not a problem with the architecture (I'm implementing a Resnet from another paper). Making statements based on opinion; back them up with references or personal experience. 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. 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. 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). 'Jupyter notebook' and 'unit testing' are anti-correlated. (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. 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. 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. How do you ensure that a red herring doesn't violate Chekhov's gun? This informs us as to whether the model needs further tuning or adjustments or not. . Instead, make a batch of fake data (same shape), and break your model down into components. (which could be considered as some kind of testing).
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