lstm validation loss not decreasing

Data normalization and standardization in neural networks. But adding too many hidden layers can make risk overfitting or make it very hard to optimize the network. 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. "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks" by Jinghui Chen, Quanquan Gu. 1) Train your model on a single data point. I then pass the answers through an LSTM to get a representation (50 units) of the same length for answers. So if you're downloading someone's model from github, pay close attention to their preprocessing. What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. rev2023.3.3.43278. +1 for "All coding is debugging". How can change in cost function be positive? It become true that I was doing regression with ReLU last activation layer, which is obviously wrong. Learn more about Stack Overflow the company, and our products. MathJax reference. Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? The posted answers are great, and I wanted to add a few "Sanity Checks" which have greatly helped me in the past. That probably did fix wrong activation method. if you're getting some error at training time, update your CV and start looking for a different job :-). (For example, the code may seem to work when it's not correctly implemented. It could be that the preprocessing steps (the padding) are creating input sequences that cannot be separated (perhaps you are getting a lot of zeros or something of that sort). Lol. How to react to a students panic attack in an oral exam? 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. Hey there, I'm just curious as to why this is so common with RNNs. Set up a very small step and train it. Styling contours by colour and by line thickness in QGIS. 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. 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. 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 . ncdu: What's going on with this second size column? The order in which the training set is fed to the net during training may have an effect. Predictions are more or less ok here. Otherwise, you might as well be re-arranging deck chairs on the RMS Titanic. 3) Generalize your model outputs to debug. 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. This is a very active area of research. This can be done by setting the validation_split argument on fit () to use a portion of the training data as a validation dataset. I think Sycorax and Alex both provide very good comprehensive answers. Connect and share knowledge within a single location that is structured and easy to search. (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. The training loss should now decrease, but the test loss may increase. What is happening? The best method I've ever found for verifying correctness is to break your code into small segments, and verify that each segment works. Replacing broken pins/legs on a DIP IC package. Just by virtue of opening a JPEG, both these packages will produce slightly different images. Is there a solution if you can't find more data, or is an RNN just the wrong model? (See: Why do we use ReLU in neural networks and how do we use it?) I regret that I left it out of my answer. 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. Why is this sentence from The Great Gatsby grammatical? Initialization over too-large an interval can set initial weights too large, meaning that single neurons have an outsize influence over the network behavior. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. I am runnning LSTM for classification task, and my validation loss does not decrease. What video game is Charlie playing in Poker Face S01E07? If I make any parameter modification, I make a new configuration file. Why do we use ReLU in neural networks and how do we use it? When my network doesn't learn, I turn off all regularization and verify that the non-regularized network works correctly. 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. Do I need a thermal expansion tank if I already have a pressure tank? 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." Why is Newton's method not widely used in machine learning? I knew a good part of this stuff, what stood out for me is. There are 252 buckets. Even when a neural network code executes without raising an exception, the network can still have bugs! : 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. In theory then, using Docker along with the same GPU as on your training system should then produce the same results. 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. Suppose that the softmax operation was not applied to obtain $\mathbf y$ (as is normally done), and suppose instead that some other operation, called $\delta(\cdot)$, that is also monotonically increasing in the inputs, was applied instead. (But I don't think anyone fully understands why this is the case.) There are a number of variants on stochastic gradient descent which use momentum, adaptive learning rates, Nesterov updates and so on to improve upon vanilla SGD. 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. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural 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. Making statements based on opinion; back them up with references or personal experience. As an example, imagine you're using an LSTM to make predictions from time-series data. 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. 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). 'Jupyter notebook' and 'unit testing' are anti-correlated. 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 $\mathbf W \in \mathbb R^{k \times d}$. Then, let $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$ be a loss function. The scale of the data can make an enormous difference on training. Then I add each regularization piece back, and verify that each of those works along the way. Styling contours by colour and by line thickness in QGIS. This is because your model should start out close to randomly guessing. I have two stacked LSTMS as follows (on Keras): Train on 127803 samples, validate on 31951 samples. Please help me. In particular, you should reach the random chance loss on the test set. and i used keras framework to build the network, but it seems the NN can't be build up easily. Replacing broken pins/legs on a DIP IC package. Before I was knowing that this is wrong, I did add Batch Normalisation layer after every learnable layer, and that helps. This verifies a few things. Testing on a single data point is a really great idea. In all other cases, the optimization problem is non-convex, and non-convex optimization is hard. The best answers are voted up and rise to the top, Not the answer you're looking for? 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. 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 tactic can pinpoint where some regularization might be poorly set. 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. Two parts of regularization are in conflict. While this is highly dependent on the availability of data. For an example of such an approach you can have a look at my experiment. I checked and found while I was using LSTM: Thanks for contributing an answer to Data Science Stack Exchange! You can study this further by making your model predict on a few thousand examples, and then histogramming the outputs. Does a summoned creature play immediately after being summoned by a ready action? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. You have to check that your code is free of bugs before you can tune network performance! or bAbI. Switch the LSTM to return predictions at each step (in keras, this is return_sequences=True). Decrease the initial learning rate using the 'InitialLearnRate' option of trainingOptions. To learn more, see our tips on writing great answers. . Ok, rereading your code I can obviously see that you are correct; I will edit my answer. 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. As a simple example, suppose that we are classifying images, and that we expect the output to be the $k$-dimensional vector $\mathbf y = \begin{bmatrix}1 & 0 & 0 & \cdots & 0\end{bmatrix}$. Just at the end adjust the training and the validation size to get the best result in the test set. If the loss decreases consistently, then this check has passed. 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. Can I tell police to wait and call a lawyer when served with a search warrant? Deep learning is all the rage these days, and networks with a large number of layers have shown impressive results. See: In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. with two problems ("How do I get learning to continue after a certain epoch?" The differences are usually really small, but you'll occasionally see drops in model performance due to this kind of stuff. Here is a simple formula: $$ It's interesting how many of your comments are similar to comments I have made (or have seen others make) in relation to debugging estimation of parameters or predictions for complex models with MCMC sampling schemes. 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. Now I'm working on it. Making sure that your model can overfit is an excellent idea. I'm possibly being too negative, but frankly I've had enough with people cloning Jupyter Notebooks from GitHub, thinking it would be a matter of minutes to adapt the code to their use case and then coming to me complaining that nothing works. (This is an example of the difference between a syntactic and semantic error.). How does the Adam method of stochastic gradient descent work? 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. vegan) just to try it, does this inconvenience the caterers and staff? One way for implementing curriculum learning is to rank the training examples by difficulty. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? 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. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Problem is I do not understand what's going on here. Or the other way around? Any time you're writing code, you need to verify that it works as intended. If the model isn't learning, there is a decent chance that your backpropagation is not working. Thanks for contributing an answer to Data Science Stack Exchange! The problem turns out to be the misunderstanding of the batch size and other features that defining an nn.LSTM. The suggestions for randomization tests are really great ways to get at bugged networks. 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. For example, it's widely observed that layer normalization and dropout are difficult to use together. Choosing the number of hidden layers lets the network learn an abstraction from the raw data. 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. If you haven't done so, you may consider to work with some benchmark dataset like SQuAD I'll let you decide. I simplified the model - instead of 20 layers, I opted for 8 layers. And the loss in the training looks like this: Is there anything wrong with these codes? Is it suspicious or odd to stand by the gate of a GA airport watching the planes? Instead, several authors have proposed easier methods, such as Curriculum by Smoothing, where the output of each convolutional layer in a convolutional neural network (CNN) is smoothed using a Gaussian kernel. Setting up a neural network configuration that actually learns is a lot like picking a lock: all of the pieces have to be lined up just right. The essential idea of curriculum learning is best described in the abstract of the previously linked paper by Bengio et al. Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by the magnitude of one order Try something more meaningful such as cross-entropy loss: you don't just want to classify correctly, but you'd like to classify with high accuracy. Short story taking place on a toroidal planet or moon involving flying. Convolutional neural networks can achieve impressive results on "structured" data sources, image or audio data. This problem is easy to identify. 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.

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