lstm validation loss not decreasing

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. Why are physically impossible and logically impossible concepts considered separate in terms of probability? 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. Dropout is used during testing, instead of only being used for training. 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). (+1) This is a good write-up. MathJax reference. Deep learning is all the rage these days, and networks with a large number of layers have shown impressive results. Data normalization and standardization in neural networks. We can then generate a similar target to aim for, rather than a random one. And struggled for a long time that the model does not learn. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? 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 . If the loss decreases consistently, then this check has passed. Two parts of regularization are in conflict. Using Kolmogorov complexity to measure difficulty of problems? I had this issue - while training loss was decreasing, the validation loss was not decreasing. 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. Asking for help, clarification, or responding to other answers. I just learned this lesson recently and I think it is interesting to share. 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. Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? The difference between the phonemes /p/ and /b/ in Japanese, Short story taking place on a toroidal planet or moon involving flying. rev2023.3.3.43278. This is an easier task, so the model learns a good initialization before training on the real task. 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 suggestions for randomization tests are really great ways to get at bugged networks. Thanks a bunch for your insight! . In particular, you should reach the random chance loss on the test set. You can study this further by making your model predict on a few thousand examples, and then histogramming the outputs. One caution about ReLUs is the "dead neuron" phenomenon, which can stymie learning; leaky relus and similar variants avoid this problem. MathJax reference. 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. 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. train.py model.py python. +1 for "All coding is debugging". +1 Learning like children, starting with simple examples, not being given everything at once! What is a word for the arcane equivalent of a monastery? thanks, I will try increasing my training set size, I was actually trying to reduce the number of hidden units but to no avail, thanks for pointing out! (The author is also inconsistent about using single- or double-quotes but that's purely stylistic. Ive seen a number of NN posts where OP left a comment like oh I found a bug now it works.. Neural networks are not "off-the-shelf" algorithms in the way that random forest or logistic regression are. It thus cannot overfit to accommodate them while losing the ability to respond correctly to the validation examples - which, after all, are generated by the same process as the training examples. The differences are usually really small, but you'll occasionally see drops in model performance due to this kind of stuff. I keep all of these configuration files. 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). Other networks will decrease the loss, but only very slowly. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks. How to handle a hobby that makes income in US. Otherwise, you might as well be re-arranging deck chairs on the RMS Titanic. Dealing with such a Model: Data Preprocessing: Standardizing and Normalizing the data. What could cause this? Aren't my iterations needed to train NN for XOR with MSE < 0.001 too high? The validation loss slightly increase such as from 0.016 to 0.018. Maybe in your example, you only care about the latest prediction, so your LSTM outputs a single value and not a sequence. I worked on this in my free time, between grad school and my job. Minimising the environmental effects of my dyson brain. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Not the answer you're looking for? 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. Solutions to this are to decrease your network size, or to increase dropout. Learn more about Stack Overflow the company, and our products. Thanks @Roni. Partner is not responding when their writing is needed in European project application, How do you get out of a corner when plotting yourself into a corner. Is it possible to share more info and possibly some code? The essential idea of curriculum learning is best described in the abstract of the previously linked paper by Bengio et al. While this is highly dependent on the availability of data. In my case it's not a problem with the architecture (I'm implementing a Resnet from another paper). There's a saying among writers that "All writing is re-writing" -- that is, the greater part of writing is revising. What is the purpose of this D-shaped ring at the base of the tongue on my hiking boots? This can be done by comparing the segment output to what you know to be the correct answer. Making statements based on opinion; back them up with references or personal experience. It is very weird. If the model isn't learning, there is a decent chance that your backpropagation is not working. The reason that I'm so obsessive about retaining old results is that this makes it very easy to go back and review previous experiments. This step is not as trivial as people usually assume it to be. The best method I've ever found for verifying correctness is to break your code into small segments, and verify that each segment works. 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 . To subscribe to this RSS feed, copy and paste this URL into your RSS reader. What's the channel order for RGB images? See if the norm of the weights is increasing abnormally with epochs. This is called unit testing. Choosing a clever network wiring can do a lot of the work for you. 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"? (which could be considered as some kind of testing). 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). Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by the magnitude of one order Neural networks and other forms of ML are "so hot right now". There are two features of neural networks that make verification even more important than for other types of machine learning or statistical models. The problem turns out to be the misunderstanding of the batch size and other features that defining an nn.LSTM. I checked and found while I was using LSTM: Thanks for contributing an answer to Data Science Stack Exchange! Welcome to DataScience. I edited my original post to accomodate your input and some information about my loss/acc values. Do they first resize and then normalize the image? Learn more about Stack Overflow the company, and our products. Designing a better optimizer is very much an active area of research. (No, It Is Not About Internal Covariate Shift). as a particular form of continuation method (a general strategy for global optimization of non-convex functions). If it is indeed memorizing, the best practice is to collect a larger dataset. It means that your step will minimise by a factor of two when $t$ is equal to $m$. 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. 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. A typical trick to verify that is to manually mutate some labels. To learn more, see our tips on writing great answers. If a law is new but its interpretation is vague, can the courts directly ask the drafters the intent and official interpretation of their law? (+1) Checking the initial loss is a great suggestion. $\begingroup$ 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. In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. If I run your code (unchanged - on a GPU), then the model doesn't seem to train. Do not train a neural network to start with! I agree with your analysis. Hence validation accuracy also stays at same level but training accuracy goes up. This informs us as to whether the model needs further tuning or adjustments or not. 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. Can archive.org's Wayback Machine ignore some query terms? The objective function of a neural network is only convex when there are no hidden units, all activations are linear, and the design matrix is full-rank -- because this configuration is identically an ordinary regression problem. What video game is Charlie playing in Poker Face S01E07? If your model is unable to overfit a few data points, then either it's too small (which is unlikely in today's age),or something is wrong in its structure or the learning algorithm. But for my case, training loss still goes down but validation loss stays at same level. I understand that it might not be feasible, but very often data size is the key to success. In my case the initial training set was probably too difficult for the network, so it was not making any progress. Making statements based on opinion; back them up with references or personal experience. This is a good addition. Use MathJax to format equations. Why do many companies reject expired SSL certificates as bugs in bug bounties? Neural networks in particular are extremely sensitive to small changes in your data. In all other cases, the optimization problem is non-convex, and non-convex optimization is hard. In the context of recent research studying the difficulty of training in the presence of non-convex training criteria Where does this (supposedly) Gibson quote come from? Often the simpler forms of regression get overlooked. Can I add data, that my neural network classified, to the training set, in order to improve it? How to handle a hobby that makes income in US. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Then make dummy models in place of each component (your "CNN" could just be a single 2x2 20-stride convolution, the LSTM with just 2 Set up a very small step and train it. Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? The best answers are voted up and rise to the top, Not the answer you're looking for? I am runnning LSTM for classification task, and my validation loss does not decrease. Residual connections are a neat development that can make it easier to train neural networks. Now I'm working on it. It only takes a minute to sign up. 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. 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. Trying to understand how to get this basic Fourier Series, Linear Algebra - Linear transformation question. How to handle a hobby that makes income in US. Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? Convolutional neural networks can achieve impressive results on "structured" data sources, image or audio data. The best answers are voted up and rise to the top, Not the answer you're looking for? My training loss goes down and then up again. padding them with data to make them equal length), the LSTM is correctly ignoring your masked data. 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. Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? ), have a look at a few samples (to make sure the import has gone well) and perform data cleaning if/when needed. How do you ensure that a red herring doesn't violate Chekhov's gun? In my case, I constantly make silly mistakes of doing Dense(1,activation='softmax') vs Dense(1,activation='sigmoid') for binary predictions, and the first one gives garbage results. 2 Usually when a model overfits, validation loss goes up and training loss goes down from the point of overfitting. This is because your model should start out close to randomly guessing. Try to set up it smaller and check your loss again. 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. If so, how close was it? This is especially useful for checking that your data is correctly normalized. 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. 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. 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. (This is an example of the difference between a syntactic and semantic error.). 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. Loss was constant 4.000 and accuracy 0.142 on 7 target values dataset. I couldn't obtained a good validation loss as my training loss was decreasing. rev2023.3.3.43278. 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. 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. The first step when dealing with overfitting is to decrease the complexity of the model. Is it possible to create a concave light? See, There are a number of other options. . $\endgroup$ My model look like this: And here is the function for each training sample. Many of the different operations are not actually used because previous results are over-written with new variables. (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. You just need to set up a smaller value for your learning rate. Try a random shuffle of the training set (without breaking the association between inputs and outputs) and see if the training loss goes down. Your learning could be to big after the 25th epoch. Since NNs are nonlinear models, normalizing the data can affect not only the numerical stability, but also the training time, and the NN outputs (a linear function such as normalization doesn't commute with a nonlinear hierarchical function). @Alex R. I'm still unsure what to do if you do pass the overfitting test. To verify my implementation of the model and understand keras, I'm using a toyproblem to make sure I understand what's going on. 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. Asking for help, clarification, or responding to other answers. Residual connections can improve deep feed-forward networks. 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 network initialization is often overlooked as a source of neural network bugs. Why is this sentence from The Great Gatsby grammatical? Linear Algebra - Linear transformation question. 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. If you can't find a simple, tested architecture which works in your case, think of a simple baseline. How to tell which packages are held back due to phased updates. Then incrementally add additional model complexity, and verify that each of those works as well. 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. Especially if you plan on shipping the model to production, it'll make things a lot easier. On the same dataset a simple averaged sentence embedding gets f1 of .75, while an LSTM is a flip of a coin. A standard neural network is composed of layers. Make sure you're minimizing the loss function, Make sure your loss is computed correctly. The Marginal Value of Adaptive Gradient Methods in Machine Learning, Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. Has 90% of ice around Antarctica disappeared in less than a decade? Connect and share knowledge within a single location that is structured and easy to search. 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. What could cause this? "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. It might also be possible that you will see overfit if you invest more epochs into the training. Check that the normalized data are really normalized (have a look at their range). Is it suspicious or odd to stand by the gate of a GA airport watching the planes? normalize or standardize the data in some way. Learn more about Stack Overflow the company, and our products. Shuffling the labels independently from the samples (for instance, creating train/test splits for the labels and samples separately); Accidentally assigning the training data as the testing data; When using a train/test split, the model references the original, non-split data instead of the training partition or the testing partition. 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. Thanks for contributing an answer to Data Science Stack Exchange! Also, when it comes to explaining your model, someone will come along and ask "what's the effect of $x_k$ on the result?" Just want to add on one technique haven't been discussed yet. Is it possible to rotate a window 90 degrees if it has the same length and width? Before I was knowing that this is wrong, I did add Batch Normalisation layer after every learnable layer, and that helps. 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. 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. I agree with this answer. MathJax reference. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. But some recent research has found that SGD with momentum can out-perform adaptive gradient methods for neural networks. Some examples are. Connect and share knowledge within a single location that is structured and easy to search. Using indicator constraint with two variables. Have a look at a few input samples, and the associated labels, and make sure they make sense. See: Gradient clipping re-scales the norm of the gradient if it's above some threshold. When my network doesn't learn, I turn off all regularization and verify that the non-regularized network works correctly. The training loss should now decrease, but the test loss may increase. This will avoid gradient issues for saturated sigmoids, at the output. 1) Train your model on a single data point. Instead, I do that in a configuration file (e.g., JSON) that is read and used to populate network configuration details at runtime. This can be a source of issues. If your training/validation loss are about equal then your model is underfitting. Basically, the idea is to calculate the derivative by defining two points with a $\epsilon$ interval. 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. And when the training rounds are after 30 times validation loss and test loss tend to be stable after 30 training . Some examples: When it first came out, the Adam optimizer generated a lot of interest. You have to check that your code is free of bugs before you can tune network performance! Find centralized, trusted content and collaborate around the technologies you use most. 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. Thanks for contributing an answer to Cross Validated! and all you will be able to do is shrug your shoulders. remove regularization gradually (maybe switch batch norm for a few layers). LSTM Training loss decreases and increases, Sequence lengths in LSTM / BiLSTMs and overfitting, Why does the loss/accuracy fluctuate during the training? You can easily (and quickly) query internal model layers and see if you've setup your graph correctly. 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. 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). What's the difference between a power rail and a signal line? The problem I find is that the models, for various hyperparameters I try (e.g. For example, it's widely observed that layer normalization and dropout are difficult to use together. The second one is to decrease your learning rate monotonically. I had this issue - while training loss was decreasing, the validation loss was not decreasing. How do you ensure that a red herring doesn't violate Chekhov's gun? I am getting different values for the loss function per epoch. An application of this is to make sure that when you're masking your sequences (i.e. 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). If you don't see any difference between the training loss before and after shuffling labels, this means that your code is buggy (remember that we have already checked the labels of the training set in the step before). No change in accuracy using Adam Optimizer when SGD works fine. Learning . How do you ensure that a red herring doesn't violate Chekhov's gun? I just copied the code above (fixed the scaler bug) and reran it on CPU. learning rate) is more or less important than another (e.g. vegan) just to try it, does this inconvenience the caterers and staff? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. model.py . The experiments show that significant improvements in generalization can be achieved. 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. Connect and share knowledge within a single location that is structured and easy to search. rev2023.3.3.43278. This leaves how to close the generalization gap of adaptive gradient methods an open problem. any suggestions would be appreciated. Alternatively, rather than generating a random target as we did above with $\mathbf y$, we could work backwards from the actual loss function to be used in training the entire neural network to determine a more realistic target. If your neural network does not generalize well, see: What should I do when my neural network doesn't generalize well? I have two stacked LSTMS as follows (on Keras): Train on 127803 samples, validate on 31951 samples. Double check your input data. My dataset contains about 1000+ examples. Accuracy on training dataset was always okay. What is the best question generation state of art with nlp? First one is a simplest one. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. ncdu: What's going on with this second size column? What am I doing wrong here in the PlotLegends specification? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. I am training a LSTM model to do question answering, i.e. Lots of good advice there. As an example, two popular image loading packages are cv2 and PIL. Use MathJax to format equations. @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. Why do many companies reject expired SSL certificates as bugs in bug bounties? import imblearn import mat73 import keras from keras.utils import np_utils import os. It also hedges against mistakenly repeating the same dead-end experiment. Why is Newton's method not widely used in machine learning? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Large non-decreasing LSTM training loss. This can help make sure that inputs/outputs are properly normalized in each layer. The order in which the training set is fed to the net during training may have an effect. Selecting a label smoothing factor for seq2seq NMT with a massive imbalanced vocabulary. What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? We design a new algorithm, called Partially adaptive momentum estimation method (Padam), which unifies the Adam/Amsgrad with SGD to achieve the best from both worlds. And these elements may completely destroy the data. What image preprocessing routines do they use? Replacing broken pins/legs on a DIP IC package. Where $a$ is your learning rate, $t$ is your iteration number and $m$ is a coefficient that identifies learning rate decreasing speed. "FaceNet: A Unified Embedding for Face Recognition and Clustering" Florian Schroff, Dmitry Kalenichenko, James Philbin. when a talkative person goes quiet, domain eukarya kingdom protista examples, brett waterman speech,