lstm validation loss not decreasing

And when the training rounds are after 30 times validation loss and test loss tend to be stable after 30 training . The best answers are voted up and rise to the top, Not the answer you're looking for? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. I reduced the batch size from 500 to 50 (just trial and error). Thanks for contributing an answer to Stack Overflow! 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). 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. 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). anonymous2 (Parker) May 9, 2022, 5:30am #1. any suggestions would be appreciated. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. I think what you said must be on the right track. 'Jupyter notebook' and 'unit testing' are anti-correlated. Increase the size of your model (either number of layers or the raw number of neurons per layer) . A standard neural network is composed of layers. The best answers are voted up and rise to the top, Not the answer you're looking for? 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. The best method I've ever found for verifying correctness is to break your code into small segments, and verify that each segment works. I then pass the answers through an LSTM to get a representation (50 units) of the same length for answers. 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. 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. Short story taking place on a toroidal planet or moon involving flying. Scaling the inputs (and certain times, the targets) can dramatically improve the network's training. Thank you itdxer. My recent lesson is trying to detect if an image contains some hidden information, by stenography tools. 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. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. The first step when dealing with overfitting is to decrease the complexity of the model. 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. 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. If the model isn't learning, there is a decent chance that your backpropagation is not working. To learn more, see our tips on writing great answers. 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. All the answers are great, but there is one point which ought to be mentioned : is there anything to learn from your data ? Should I put my dog down to help the homeless? Thanks for contributing an answer to Cross Validated! See: Gradient clipping re-scales the norm of the gradient if it's above some threshold. As the most upvoted answer has already covered unit tests, I'll just add that there exists a library which supports unit tests development for NN (only in Tensorflow, unfortunately). Seeing as you do not generate the examples anew every time, it is reasonable to assume that you would reach overfit, given enough epochs, if it has enough trainable parameters. 3) Generalize your model outputs to debug. 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. This is achieved by including in the training phase simultaneously (i) physical dependencies between. If you haven't done so, you may consider to work with some benchmark dataset like SQuAD Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by the magnitude of one order Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary. 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. ), have a look at a few samples (to make sure the import has gone well) and perform data cleaning if/when needed. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? (See: What is the essential difference between neural network and linear regression), Classical neural network results focused on sigmoidal activation functions (logistic or $\tanh$ functions). 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. Making sure that your model can overfit is an excellent idea. The training loss should now decrease, but the test loss may increase. Here's an example of a question where the problem appears to be one of model configuration or hyperparameter choice, but actually the problem was a subtle bug in how gradients were computed. Curriculum learning is a formalization of @h22's answer. Large non-decreasing LSTM training loss. model.py . I edited my original post to accomodate your input and some information about my loss/acc values. My model architecture is as follows (if not relevant please ignore): I pass the explanation (encoded) and question each through the same lstm to get a vector representation of the explanation/question and add these representations together to get a combined representation for the explanation and question. The scale of the data can make an enormous difference on training. 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?. Other networks will decrease the loss, but only very slowly. 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. Do they first resize and then normalize the image? Writing good unit tests is a key piece of becoming a good statistician/data scientist/machine learning expert/neural network practitioner. 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 . Connect and share knowledge within a single location that is structured and easy to search. Styling contours by colour and by line thickness in QGIS. I am writing a program that make use of the build in LSTM in the Pytorch, however the loss is always around some numbers and does not decrease significantly. In all other cases, the optimization problem is non-convex, and non-convex optimization is hard. This paper introduces a physics-informed machine learning approach for pathloss prediction. Model compelxity: Check if the model is too complex. 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 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. Finally, I append as comments all of the per-epoch losses for training and validation. Try to set up it smaller and check your loss again. normalize or standardize the data in some way. Edit: I added some output of an experiment: Training scores can be expected to be better than those of the validation when the machine you train can "adapt" to the specifics of the training examples while not successfully generalizing; the greater the adaption to the specifics of the training examples and the worse generalization, the bigger the gap between training and validation scores (in favor of the training scores). Does Counterspell prevent from any further spells being cast on a given turn? Be advised that validation, as it is calculated at the end of each epoch, uses the "best" machine trained in that epoch (that is, the last one, but if constant improvement is the case then the last weights should yield the best results - at least for training loss, if not for validation), while the train loss is calculated as an average of the performance per each epoch. Is there a proper earth ground point in this switch box? Just by virtue of opening a JPEG, both these packages will produce slightly different images. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. What is the best question generation state of art with nlp? read data from some source (the Internet, a database, a set of local files, etc. Do I need a thermal expansion tank if I already have a pressure tank? However, when I did replace ReLU with Linear activation (for regression), no Batch Normalisation was needed any more and model started to train significantly better. Check the accuracy on the test set, and make some diagnostic plots/tables. $L^2$ regularization (aka weight decay) or $L^1$ regularization is set too large, so the weights can't move. How to react to a students panic attack in an oral exam? I borrowed this example of buggy code from the article: Do you see the error? Wide and deep neural networks, and neural networks with exotic wiring, are the Hot Thing right now in machine learning. How to match a specific column position till the end of line? The posted answers are great, and I wanted to add a few "Sanity Checks" which have greatly helped me in the past. How to interpret intermitent decrease of loss? . This is a good addition. Asking for help, clarification, or responding to other answers. However, I am running into an issue with very large MSELoss that does not decrease in training (meaning essentially my network is not training). See if you inverted the training set and test set labels, for example (happened to me once -___-), or if you imported the wrong file. This means that if you have 1000 classes, you should reach an accuracy of 0.1%. This usually happens when your neural network weights aren't properly balanced, especially closer to the softmax/sigmoid. 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. I checked and found while I was using LSTM: I simplified the model - instead of 20 layers, I opted for 8 layers. Lol. What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? 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.