How to handle a hobby that makes income in US. keras lstm loss-function accuracy Share Improve this question Might be an interesting experiment. Set up a very small step and train it. There's a saying among writers that "All writing is re-writing" -- that is, the greater part of writing is revising. I checked and found while I was using LSTM: I simplified the model - instead of 20 layers, I opted for 8 layers. This will help you make sure that your model structure is correct and that there are no extraneous issues. How to react to a students panic attack in an oral exam? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. A standard neural network is composed of layers. Making statements based on opinion; back them up with references or personal experience. I worked on this in my free time, between grad school and my job. 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. . 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. 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. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks. In theory then, using Docker along with the same GPU as on your training system should then produce the same results. 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. rev2023.3.3.43278. $\begingroup$ 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. Why is it hard to train deep neural networks? How to handle hidden-cell output of 2-layer LSTM in PyTorch? if you're getting some error at training time, update your CV and start looking for a different job :-). This can help make sure that inputs/outputs are properly normalized in each layer. Deep learning is all the rage these days, and networks with a large number of layers have shown impressive results. :). This means writing code, and writing code means debugging. You can easily (and quickly) query internal model layers and see if you've setup your graph correctly. (+1) Checking the initial loss is a great suggestion. But the validation loss starts with very small . 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 I regret that I left it out of my answer. 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. 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. If you haven't done so, you may consider to work with some benchmark dataset like SQuAD 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. 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. 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. In one example, I use 2 answers, one correct answer and one wrong answer. Sometimes, networks simply won't reduce the loss if the data isn't scaled. Making statements based on opinion; back them up with references or personal experience. See, There are a number of other options. What am I doing wrong here in the PlotLegends specification? In my case the initial training set was probably too difficult for the network, so it was not making any progress. It also hedges against mistakenly repeating the same dead-end experiment. Dropout is used during testing, instead of only being used for training. How to tell which packages are held back due to phased updates, How do you get out of a corner when plotting yourself into a corner. If it is indeed memorizing, the best practice is to collect a larger dataset. (+1) This is a good write-up. Increase the size of your model (either number of layers or the raw number of neurons per layer) . 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. What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? What is the purpose of this D-shaped ring at the base of the tongue on my hiking boots? 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. This is a good addition. Loss is still decreasing at the end of training. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Curriculum learning is a formalization of @h22's answer. Other explanations might be that this is because your network does not have enough trainable parameters to overfit, coupled with a relatively large number of training examples (and of course, generating the training and the validation examples with the same process). The second one is to decrease your learning rate monotonically. Just at the end adjust the training and the validation size to get the best result in the test set. 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). here is my code and my outputs: This can be done by setting the validation_split argument on fit () to use a portion of the training data as a validation dataset. What degree of difference does validation and training loss need to have to be called good fit? Does not being able to overfit a single training sample mean that the neural network architecure or implementation is wrong? 12 that validation loss and test loss keep decreasing when the training rounds are before 30 times. Accuracy on training dataset was always okay. Maybe in your example, you only care about the latest prediction, so your LSTM outputs a single value and not a sequence. What is happening? See if you inverted the training set and test set labels, for example (happened to me once -___-), or if you imported the wrong file. Residual connections are a neat development that can make it easier to train neural networks. Please help me. 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. Prior to presenting data to a neural network. 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?. First, build a small network with a single hidden layer and verify that it works correctly. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. One caution about ReLUs is the "dead neuron" phenomenon, which can stymie learning; leaky relus and similar variants avoid this problem. the opposite test: you keep the full training set, but you shuffle the labels. (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. It just stucks at random chance of particular result with no loss improvement during training. Data normalization and standardization in neural networks. This Medium post, "How to unit test machine learning code," by Chase Roberts discusses unit-testing for machine learning models in more detail. The problem turns out to be the misunderstanding of the batch size and other features that defining an nn.LSTM. Why are physically impossible and logically impossible concepts considered separate in terms of probability? Connect and share knowledge within a single location that is structured and easy to search. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Now I'm working on it. A typical trick to verify that is to manually mutate some labels. To set the gradient threshold, use the 'GradientThreshold' option in trainingOptions. This is especially useful for checking that your data is correctly normalized. Large non-decreasing LSTM training loss. Basically, the idea is to calculate the derivative by defining two points with a $\epsilon$ interval. Although it can easily overfit to a single image, it can't fit to a large dataset, despite good normalization and shuffling. 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. 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). The first step when dealing with overfitting is to decrease the complexity of the model. Why does Mister Mxyzptlk need to have a weakness in the comics? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. 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 . Use MathJax to format equations. remove regularization gradually (maybe switch batch norm for a few layers). What am I doing wrong here in the PlotLegends specification? It means that your step will minimise by a factor of two when $t$ is equal to $m$. MathJax reference. I keep all of these configuration files. Tensorboard provides a useful way of visualizing your layer outputs. But adding too many hidden layers can make risk overfitting or make it very hard to optimize the network. ), @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. So I suspect, there's something going on with the model that I don't understand. Are there tables of wastage rates for different fruit and veg? Asking for help, clarification, or responding to other answers. visualize the distribution of weights and biases for each layer. Lol. Try to adjust the parameters $\mathbf W$ and $\mathbf b$ to minimize this loss function. 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. 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.). $L^2$ regularization (aka weight decay) or $L^1$ regularization is set too large, so the weights can't move. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? The reason is many packages are rescaling images to certain size and this operation completely destroys the hidden information inside. What is the best question generation state of art with nlp? If so, how close was it? I reduced the batch size from 500 to 50 (just trial and error). Do I need a thermal expansion tank if I already have a pressure tank? When training triplet networks, training with online hard negative mining immediately risks model collapse, so people train with semi-hard negative mining first as a kind of "pre training." What should I do? What can be the actions to decrease? The best answers are voted up and rise to the top, Not the answer you're looking for? 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. 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. What should I do when my neural network doesn't generalize well? Why is Newton's method not widely used in machine learning? Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. If we do not trust that $\delta(\cdot)$ is working as expected, then since we know that it is monotonically increasing in the inputs, then we can work backwards and deduce that the input must have been a $k$-dimensional vector where the maximum element occurs at the first element. ncdu: What's going on with this second size column? Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary. Replacing broken pins/legs on a DIP IC package. The 'validation loss' metrics from the test data has been oscillating a lot after epochs but not really decreasing. Is there a solution if you can't find more data, or is an RNN just the wrong model? What to do if training loss decreases but validation loss does not decrease? In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". Replacing broken pins/legs on a DIP IC package. Then you can take a look at your hidden-state outputs after every step and make sure they are actually different. Why do we use ReLU in neural networks and how do we use it? @Alex R. I'm still unsure what to do if you do pass the overfitting test. 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. I had a model that did not train at all. 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. Can archive.org's Wayback Machine ignore some query terms? I just learned this lesson recently and I think it is interesting to share. Reiterate ad nauseam. To learn more, see our tips on writing great answers. Comprehensive list of activation functions in neural networks with pros/cons, "Deep Residual Learning for Image Recognition", Identity Mappings in Deep Residual Networks. 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. When I set up a neural network, I don't hard-code any parameter settings. See: Gradient clipping re-scales the norm of the gradient if it's above some threshold. anonymous2 (Parker) May 9, 2022, 5:30am #1. Instead of training for a fixed number of epochs, you stop as soon as the validation loss rises because, after that, your model will generally only get worse . This tactic can pinpoint where some regularization might be poorly set. There are 252 buckets. 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. Then training proceed with online hard negative mining, and the model is better for it as a result. If the loss decreases consistently, then this check has passed. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Even if you can prove that there is, mathematically, only a small number of neurons necessary to model a problem, it is often the case that having "a few more" neurons makes it easier for the optimizer to find a "good" configuration. 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. What am I doing wrong here in the PlotLegends specification? 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. The problem I find is that the models, for various hyperparameters I try (e.g. 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"? This can be done by comparing the segment output to what you know to be the correct answer. The network picked this simplified case well. Do they first resize and then normalize the image? I understand that it might not be feasible, but very often data size is the key to success. For an example of such an approach you can have a look at my experiment. Hence validation accuracy also stays at same level but training accuracy goes up. Using indicator constraint with two variables. 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. +1 for "All coding is debugging". 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%. Any time you're writing code, you need to verify that it works as intended. rev2023.3.3.43278. I get NaN values for train/val loss and therefore 0.0% accuracy. 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. Or the other way around? Especially if you plan on shipping the model to production, it'll make things a lot easier. 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. If the training algorithm is not suitable you should have the same problems even without the validation or dropout. The asker was looking for "neural network doesn't learn" so I majored there. Writing good unit tests is a key piece of becoming a good statistician/data scientist/machine learning expert/neural network practitioner. If your training/validation loss are about equal then your model is underfitting. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? Is it possible to rotate a window 90 degrees if it has the same length and width? What image loaders do they use? The validation loss slightly increase such as from 0.016 to 0.018. Towards a Theoretical Understanding of Batch Normalization, How Does Batch Normalization Help Optimization? How to match a specific column position till the end of line? Of course, this can be cumbersome. This is a non-exhaustive list of the configuration options which are not also regularization options or numerical optimization options. Minimising the environmental effects of my dyson brain. Not the answer you're looking for? 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. 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. 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. 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. Why do many companies reject expired SSL certificates as bugs in bug bounties? Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? 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. 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. 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. Double check your input data. Continuing the binary example, if your data is 30% 0's and 70% 1's, then your intial expected loss around $L=-0.3\ln(0.5)-0.7\ln(0.5)\approx 0.7$. Thanks a bunch for your insight! : Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones. I simplified the model - instead of 20 layers, I opted for 8 layers. Just want to add on one technique haven't been discussed yet. Asking for help, clarification, or responding to other answers. When resizing an image, what interpolation do they use? In the given base model, there are 2 hidden Layers, one with 128 and one with 64 neurons. What could cause this? 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 .