lstm validation loss not decreasing

(But I don't think anyone fully understands why this is the case.) The funny thing is that they're half right: coding, It is really nice answer. neural-network - PytorchRNN - If you want to write a full answer I shall accept it. Why does Mister Mxyzptlk need to have a weakness in the comics? A place where magic is studied and practiced? 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. oytungunes Asks: Validation Loss does not decrease in LSTM? 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. The best answers are voted up and rise to the top, Not the answer you're looking for? Replacing broken pins/legs on a DIP IC package. I am training a LSTM model to do question answering, i.e. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Try to adjust the parameters $\mathbf W$ and $\mathbf b$ to minimize this loss function. 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. Nowadays, many frameworks have built in data pre-processing pipeline and augmentation. We can then generate a similar target to aim for, rather than a random one. Not the answer you're looking for? 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. 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 $f(\mathbf x) = \alpha(\mathbf W \mathbf x + \mathbf b)$, $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$, $\mathbf y = \begin{bmatrix}1 & 0 & 0 & \cdots & 0\end{bmatrix}$. 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 step is not as trivial as people usually assume it to be. My model look like this: And here is the function for each training sample. How to handle a hobby that makes income in US. 1) Train your model on a single data point. 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). So this does not explain why you do not see overfit. In the context of recent research studying the difficulty of training in the presence of non-convex training criteria This tactic can pinpoint where some regularization might be poorly set. Lol. If decreasing the learning rate does not help, then try using gradient clipping. (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. How to match a specific column position till the end of line? What degree of difference does validation and training loss need to have to be called good fit? 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). Connect and share knowledge within a single location that is structured and easy to search. Why do many companies reject expired SSL certificates as bugs in bug bounties? Making statements based on opinion; back them up with references or personal experience. visualize the distribution of weights and biases for each layer. Styling contours by colour and by line thickness in QGIS. Convolutional neural networks can achieve impressive results on "structured" data sources, image or audio data. If nothing helped, it's now the time to start fiddling with hyperparameters. It might also be possible that you will see overfit if you invest more epochs into the training. I edited my original post to accomodate your input and some information about my loss/acc values. 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. import imblearn import mat73 import keras from keras.utils import np_utils import os. Thank you for informing me regarding your experiment. You have to check that your code is free of bugs before you can tune network performance! I have prepared the easier set, selecting cases where differences between categories were seen by my own perception as more obvious. Or the other way around? train.py model.py python. Neural networks and other forms of ML are "so hot right now". Why does momentum escape from a saddle point in this famous image? If you can't find a simple, tested architecture which works in your case, think of a simple baseline. Choosing a clever network wiring can do a lot of the work for you. The 'validation loss' metrics from the test data has been oscillating a lot after epochs but not really decreasing. split data in training/validation/test set, or in multiple folds if using cross-validation. I'm building a lstm model for regression on timeseries. Where does this (supposedly) Gibson quote come from? For me, the validation loss also never decreases. I checked and found while I was using LSTM: I simplified the model - instead of 20 layers, I opted for 8 layers. I understand that it might not be feasible, but very often data size is the key to success. 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. The best answers are voted up and rise to the top, Not the answer you're looking for? What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? 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. 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. Replacing broken pins/legs on a DIP IC package. All of these topics are active areas of research. What is the essential difference between neural network and linear regression. I'm training a neural network but the training loss doesn't decrease. Why is this the case? See this Meta thread for a discussion: What's the best way to answer "my neural network doesn't work, please fix" questions? I knew a good part of this stuff, what stood out for me is. Build unit tests. 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. 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?") The cross-validation loss tracks the training loss. 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 . $L^2$ regularization (aka weight decay) or $L^1$ regularization is set too large, so the weights can't move. Just at the end adjust the training and the validation size to get the best result in the test set. I'm not asking about overfitting or regularization. Connect and share knowledge within a single location that is structured and easy to search. Is this drop in training accuracy due to a statistical or programming error? rev2023.3.3.43278. 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. How to match a specific column position till the end of line? 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. What is a word for the arcane equivalent of a monastery? 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. The lstm_size can be adjusted . Otherwise, you might as well be re-arranging deck chairs on the RMS Titanic. 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. Training loss goes up and down regularly. This is called unit testing. Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. Pytorch. What am I doing wrong here in the PlotLegends specification? Why do many companies reject expired SSL certificates as bugs in bug bounties? Any advice on what to do, or what is wrong? here is my code and my outputs: 'Jupyter notebook' and 'unit testing' are anti-correlated. I am amazed how many posters on SO seem to think that coding is a simple exercise requiring little effort; who expect their code to work correctly the first time they run it; and who seem to be unable to proceed when it doesn't. It means that your step will minimise by a factor of two when $t$ is equal to $m$. 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. I tried using "adam" instead of "adadelta" and this solved the problem, though I'm guessing that reducing the learning rate of "adadelta" would probably have worked also. Has 90% of ice around Antarctica disappeared in less than a decade? 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. 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. (See: Why do we use ReLU in neural networks and how do we use it?) The differences are usually really small, but you'll occasionally see drops in model performance due to this kind of stuff. Can I add data, that my neural network classified, to the training set, in order to improve it? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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. Deep learning is all the rage these days, and networks with a large number of layers have shown impressive results. 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 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). 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. This is a non-exhaustive list of the configuration options which are not also regularization options or numerical optimization options. loss/val_loss are decreasing but accuracies are the same in LSTM! Did you need to set anything else? Welcome to DataScience. You can study this further by making your model predict on a few thousand examples, and then histogramming the outputs. Additionally, neural networks have a very large number of parameters, which restricts us to solely first-order methods (see: Why is Newton's method not widely used in machine learning?). ncdu: What's going on with this second size column? Use MathJax to format equations. You've decided that the best approach to solve your problem is to use a CNN combined with a bounding box detector, that further processes image crops and then uses an LSTM to combine everything. Ok, rereading your code I can obviously see that you are correct; I will edit my answer. The asker was looking for "neural network doesn't learn" so I majored there. Don't Overfit! How to prevent Overfitting in your Deep Learning Is your data source amenable to specialized network architectures? 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. Making sure that your model can overfit is an excellent idea. You just need to set up a smaller value for your learning rate. Making statements based on opinion; back them up with references or personal experience. Using Kolmogorov complexity to measure difficulty of problems? Designing a better optimizer is very much an active area of research. To make sure the existing knowledge is not lost, reduce the set learning rate. The order in which the training set is fed to the net during training may have an effect. 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. Data normalization and standardization in neural networks. This usually happens when your neural network weights aren't properly balanced, especially closer to the softmax/sigmoid. and "How do I choose a good schedule?"). This is a good addition. And struggled for a long time that the model does not learn. But the validation loss starts with very small . Without generalizing your model you will never find this issue. Hey there, I'm just curious as to why this is so common with RNNs. It only takes a minute to sign up. Calculating probabilities from d6 dice pool (Degenesis rules for botches and triggers), Minimising the environmental effects of my dyson brain. Specifically for triplet-loss models, there are a number of tricks which can improve training time and generalization. (This is an example of the difference between a syntactic and semantic error.). I'll let you decide. 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. 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. The best method I've ever found for verifying correctness is to break your code into small segments, and verify that each segment works. Do I need a thermal expansion tank if I already have a pressure tank? I am runnning LSTM for classification task, and my validation loss does not decrease. Now I'm working on it. When my network doesn't learn, I turn off all regularization and verify that the non-regularized network works correctly. . . Just want to add on one technique haven't been discussed yet. 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. Is there a proper earth ground point in this switch box? 12 that validation loss and test loss keep decreasing when the training rounds are before 30 times. But adding too many hidden layers can make risk overfitting or make it very hard to optimize the network. The suggestions for randomization tests are really great ways to get at bugged networks. Variables are created but never used (usually because of copy-paste errors); Expressions for gradient updates are incorrect; The loss is not appropriate for the task (for example, using categorical cross-entropy loss for a regression task). Any time you're writing code, you need to verify that it works as intended. ", 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. 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. history = model.fit(X, Y, epochs=100, validation_split=0.33) anonymous2 (Parker) May 9, 2022, 5:30am #1. If I run your code (unchanged - on a GPU), then the model doesn't seem to train. The problem I find is that the models, for various hyperparameters I try (e.g. Theoretically Correct vs Practical Notation, Replacing broken pins/legs on a DIP IC package, Partner is not responding when their writing is needed in European project application. One caution about ReLUs is the "dead neuron" phenomenon, which can stymie learning; leaky relus and similar variants avoid this problem. remove regularization gradually (maybe switch batch norm for a few layers). Increase the size of your model (either number of layers or the raw number of neurons per layer) . The training loss should now decrease, but the test loss may increase. I have two stacked LSTMS as follows (on Keras): Train on 127803 samples, validate on 31951 samples. ncdu: What's going on with this second size column? The problem turns out to be the misunderstanding of the batch size and other features that defining an nn.LSTM. What can be the actions to decrease? Set up a very small step and train it. To verify my implementation of the model and understand keras, I'm using a toyproblem to make sure I understand what's going on. Making sure the derivative is approximately matching your result from backpropagation should help in locating where is the problem. I had a model that did not train at all. Especially if you plan on shipping the model to production, it'll make things a lot easier. The second one is to decrease your learning rate monotonically. [Solved] Validation Loss does not decrease in LSTM? 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. Why is it hard to train deep neural networks? A lot of times you'll see an initial loss of something ridiculous, like 6.5. I worked on this in my free time, between grad school and my job. I borrowed this example of buggy code from the article: Do you see the error? Do new devs get fired if they can't solve a certain bug? First, it quickly shows you that your model is able to learn by checking if your model can overfit your data. Asking for help, clarification, or responding to other answers. Training loss decreasing while Validation loss is not decreasing But these networks didn't spring fully-formed into existence; their designers built up to them from smaller units. 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. Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by the magnitude of one order As an example, imagine you're using an LSTM to make predictions from time-series data. How to react to a students panic attack in an oral exam? Dropout is used during testing, instead of only being used for training. My training loss goes down and then up again. How to handle a hobby that makes income in US. 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). 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. Check the accuracy on the test set, and make some diagnostic plots/tables. (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. However, training become somehow erratic so accuracy during training could easily drop from 40% down to 9% on validation set. See: Comprehensive list of activation functions in neural networks with pros/cons. Are there tables of wastage rates for different fruit and veg? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Initialization over too-large an interval can set initial weights too large, meaning that single neurons have an outsize influence over the network behavior. so given an explanation/context and a question, it is supposed to predict the correct answer out of 4 options. Trying to understand how to get this basic Fourier Series, Linear Algebra - Linear transformation question. That probably did fix wrong activation method. Too few neurons in a layer can restrict the representation that the network learns, causing under-fitting. What could cause this? Might be an interesting experiment. 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. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? 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. rev2023.3.3.43278. Have a look at a few input samples, and the associated labels, and make sure they make sense. As the OP was using Keras, another option to make slightly more sophisticated learning rate updates would be to use a callback like. I agree with your analysis. 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. Other networks will decrease the loss, but only very slowly. Why is this the case? 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. Is it correct to use "the" before "materials used in making buildings are"? I am training an LSTM to give counts of the number of items in buckets. The posted answers are great, and I wanted to add a few "Sanity Checks" which have greatly helped me in the past. Too many neurons can cause over-fitting because the network will "memorize" the training data. Decrease the initial learning rate using the 'InitialLearnRate' option of trainingOptions. So this would tell you if your initialization is bad. Ive seen a number of NN posts where OP left a comment like oh I found a bug now it works.. tensorflow - Why the LSTM can't reduce the loss - Stack Overflow the opposite test: you keep the full training set, but you shuffle the labels. This problem is easy to identify. All the answers are great, but there is one point which ought to be mentioned : is there anything to learn from your data ? 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. No change in accuracy using Adam Optimizer when SGD works fine. Is it possible to rotate a window 90 degrees if it has the same length and width? 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. :). By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. self.rnn = nn.RNNinput_size = input_sizehidden_ size = hidden_ sizebatch_first = TrueNameError'input_size'. In the given base model, there are 2 hidden Layers, one with 128 and one with 64 neurons. 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. Continuing the binary example, if your data is 30% 0's and 70% 1's, then your intial expected loss around $L=-0.3\ln(0.5)-0.7\ln(0.5)\approx 0.7$. And these elements may completely destroy the data. The best answers are voted up and rise to the top, Not the answer you're looking for? Scaling the testing data using the statistics of the test partition instead of the train partition; Forgetting to un-scale the predictions (e.g. 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. 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. Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. The network initialization is often overlooked as a source of neural network bugs.