(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.). 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%. pixel values are in [0,1] instead of [0, 255]). 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). Other people insist that scheduling is essential. See this Meta thread for a discussion: What's the best way to answer "my neural network doesn't work, please fix" questions? So this does not explain why you do not see overfit. First one is a simplest one. Neural networks in particular are extremely sensitive to small changes in your data. What is a word for the arcane equivalent of a monastery? Residual connections can improve deep feed-forward networks. This can be a source of issues. My model architecture is as follows (if not relevant please ignore): I pass the explanation (encoded) and question each through the same lstm to get a vector representation of the explanation/question and add these representations together to get a combined representation for the explanation and question. 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. Has 90% of ice around Antarctica disappeared in less than a decade? 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? Is your data source amenable to specialized network architectures? What's the best way to answer "my neural network doesn't work, please fix" questions? My dataset contains about 1000+ examples. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Thanks for contributing an answer to Stack Overflow! 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. Sometimes, networks simply won't reduce the loss if the data isn't scaled. Comprehensive list of activation functions in neural networks with pros/cons, "Deep Residual Learning for Image Recognition", Identity Mappings in Deep Residual Networks. learning rate) is more or less important than another (e.g. Wide and deep neural networks, and neural networks with exotic wiring, are the Hot Thing right now in machine learning. Tensorboard provides a useful way of visualizing your layer outputs. 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. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? What are "volatile" learning curves indicative of? What is going on? Two parts of regularization are in conflict. 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. 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 added more features, which I thought intuitively would add some new intelligent information to the X->y pair. Even when a neural network code executes without raising an exception, the network can still have bugs! 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 . One way for implementing curriculum learning is to rank the training examples by difficulty. As an example, imagine you're using an LSTM to make predictions from time-series data. Of course, this can be cumbersome. Problem is I do not understand what's going on here. Many of the different operations are not actually used because previous results are over-written with new variables. Pytorch. What is happening? $\begingroup$ As the OP was using Keras, another option to make slightly more sophisticated learning rate updates would be to use a callback like ReduceLROnPlateau, which reduces the learning rate once the validation loss hasn't improved for a given number of epochs. Psychologically, it also lets you look back and observe "Well, the project might not be where I want it to be today, but I am making progress compared to where I was $k$ weeks ago. Why is this the case? This means writing code, and writing code means debugging. Instead, make a batch of fake data (same shape), and break your model down into components. 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. Why do many companies reject expired SSL certificates as bugs in bug bounties? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. :). What should I do when my neural network doesn't generalize well? Likely a problem with the data? In theory then, using Docker along with the same GPU as on your training system should then produce the same results. Initialization over too-large an interval can set initial weights too large, meaning that single neurons have an outsize influence over the network behavior. But some recent research has found that SGD with momentum can out-perform adaptive gradient methods for neural networks. The network initialization is often overlooked as a source of neural network bugs. 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). How to handle a hobby that makes income in US. Nowadays, many frameworks have built in data pre-processing pipeline and augmentation. and "How do I choose a good schedule?"). This is a good addition. 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. Model compelxity: Check if the model is too complex. To learn more, see our tips on writing great answers. remove regularization gradually (maybe switch batch norm for a few layers). 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. rev2023.3.3.43278. 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. If you haven't done so, you may consider to work with some benchmark dataset like SQuAD What should I do? Check the accuracy on the test set, and make some diagnostic plots/tables. 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. 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. How to handle hidden-cell output of 2-layer LSTM in PyTorch? Any advice on what to do, or what is wrong? visualize the distribution of weights and biases for each layer. Why is this the case? I am training an LSTM to give counts of the number of items in buckets. I had this issue - while training loss was decreasing, the validation loss was not decreasing. 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. Aren't my iterations needed to train NN for XOR with MSE < 0.001 too high? 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). 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. Do I need a thermal expansion tank if I already have a pressure tank? What image loaders do they use? as a particular form of continuation method (a general strategy for global optimization of non-convex functions). If the problem related to your learning rate than NN should reach a lower error despite that it will go up again after a while. 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. Should I put my dog down to help the homeless? Choosing the number of hidden layers lets the network learn an abstraction from the raw data. Are there tables of wastage rates for different fruit and veg? 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. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. 'Jupyter notebook' and 'unit testing' are anti-correlated. Before I was knowing that this is wrong, I did add Batch Normalisation layer after every learnable layer, and that helps. 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. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? I worked on this in my free time, between grad school and my job. If nothing helped, it's now the time to start fiddling with hyperparameters. 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. 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. This leaves how to close the generalization gap of adaptive gradient methods an open problem. Some examples: When it first came out, the Adam optimizer generated a lot of interest. This verifies a few things. Have a look at a few input samples, and the associated labels, and make sure they make sense. 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 . See if the norm of the weights is increasing abnormally with epochs. You just need to set up a smaller value for your learning rate. Otherwise, you might as well be re-arranging deck chairs on the RMS Titanic. The differences are usually really small, but you'll occasionally see drops in model performance due to this kind of stuff. and i used keras framework to build the network, but it seems the NN can't be build up easily. It only takes a minute to sign up. Neural networks are not "off-the-shelf" algorithms in the way that random forest or logistic regression are. I don't know why that is. any suggestions would be appreciated. with two problems ("How do I get learning to continue after a certain epoch?" My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? The validation loss slightly increase such as from 0.016 to 0.018. 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?. You need to test all of the steps that produce or transform data and feed into the network. Minimising the environmental effects of my dyson brain. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks. When resizing an image, what interpolation do they use? Thank you for informing me regarding your experiment. Convolutional neural networks can achieve impressive results on "structured" data sources, image or audio data. I agree with this answer. Learn more about Stack Overflow the company, and our products. How do you ensure that a red herring doesn't violate Chekhov's gun? Your learning could be to big after the 25th 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. Where $a$ is your learning rate, $t$ is your iteration number and $m$ is a coefficient that identifies learning rate decreasing speed. The experiments show that significant improvements in generalization can be achieved. Make sure you're minimizing the loss function, Make sure your loss is computed correctly. 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. And when the training rounds are after 30 times validation loss and test loss tend to be stable after 30 training . 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. There's a saying among writers that "All writing is re-writing" -- that is, the greater part of writing is revising. Go back to point 1 because the results aren't good. 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. And struggled for a long time that the model does not learn. 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. So I suspect, there's something going on with the model that I don't understand.
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