lstm validation loss not decreasing lstm validation loss not decreasing

Replacing broken pins/legs on a DIP IC package. If you want to write a full answer I shall accept it. 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"? Has 90% of ice around Antarctica disappeared in less than a decade? I think what you said must be on the right track. The first step when dealing with overfitting is to decrease the complexity of the model. A standard neural network is composed of layers. +1 Learning like children, starting with simple examples, not being given everything at once! "FaceNet: A Unified Embedding for Face Recognition and Clustering" Florian Schroff, Dmitry Kalenichenko, James Philbin. Although it can easily overfit to a single image, it can't fit to a large dataset, despite good normalization and shuffling. 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). Since either on its own is very useful, understanding how to use both is an active area of research. For me, the validation loss also never decreases. Nowadays, many frameworks have built in data pre-processing pipeline and augmentation. 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. We've added a "Necessary cookies only" option to the cookie consent popup. 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). 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. How to Diagnose Overfitting and Underfitting of LSTM Models; Overfitting and Underfitting With Machine Learning Algorithms; Articles. \alpha(t + 1) = \frac{\alpha(0)}{1 + \frac{t}{m}} How to match a specific column position till the end of line? Ive seen a number of NN posts where OP left a comment like oh I found a bug now it works.. 1 2 . You want the mini-batch to be large enough to be informative about the direction of the gradient, but small enough that SGD can regularize your network. $f(\mathbf x) = \alpha(\mathbf W \mathbf x + \mathbf b)$, $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$, $\mathbf y = \begin{bmatrix}1 & 0 & 0 & \cdots & 0\end{bmatrix}$. Any advice on what to do, or what is wrong? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. 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. "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks" by Jinghui Chen, Quanquan Gu. 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 $\mathbf W \in \mathbb R^{k \times d}$. 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. Making statements based on opinion; back them up with references or personal experience. It takes 10 minutes just for your GPU to initialize your model. 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. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Recurrent neural networks can do well on sequential data types, such as natural language or time series data. ), @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. 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. This is called unit testing. First one is a simplest one. If I make any parameter modification, I make a new configuration file. Learn more about Stack Overflow the company, and our products. The validation loss slightly increase such as from 0.016 to 0.018. 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. Then you can take a look at your hidden-state outputs after every step and make sure they are actually different. If I run your code (unchanged - on a GPU), then the model doesn't seem to train. Deep learning is all the rage these days, and networks with a large number of layers have shown impressive results. Convolutional neural networks can achieve impressive results on "structured" data sources, image or audio data. Maybe in your example, you only care about the latest prediction, so your LSTM outputs a single value and not a sequence. rev2023.3.3.43278. Finally, the best way to check if you have training set issues is to use another training set. Asking for help, clarification, or responding to other answers. This can be done by comparing the segment output to what you know to be the correct answer. Minimising the environmental effects of my dyson brain. Additionally, the validation loss is measured after each epoch. read data from some source (the Internet, a database, a set of local files, etc. I am runnning LSTM for classification task, and my validation loss does not decrease. 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. What should I do? Even when a neural network code executes without raising an exception, the network can still have bugs! 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. Thank you itdxer. All the answers are great, but there is one point which ought to be mentioned : is there anything to learn from your data ? But the validation loss starts with very small . 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. It means that your step will minimise by a factor of two when $t$ is equal to $m$. 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. (One key sticking point, and part of the reason that it took so many attempts, is that it was not sufficient to simply get a low out-of-sample loss, since early low-loss models had managed to memorize the training data, so it was just reproducing germane blocks of text verbatim in reply to prompts -- it took some tweaking to make the model more spontaneous and still have low loss.). Minimising the environmental effects of my dyson brain. 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.). 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). How to handle a hobby that makes income in US. 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. This is a very active area of research. Using Kolmogorov complexity to measure difficulty of problems? When I set up a neural network, I don't hard-code any parameter settings. Why is Newton's method not widely used in machine learning? if you're getting some error at training time, update your CV and start looking for a different job :-). 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. 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). 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. In my case the initial training set was probably too difficult for the network, so it was not making any progress. Here is a simple formula: $$ remove regularization gradually (maybe switch batch norm for a few layers). Making statements based on opinion; back them up with references or personal experience. I'll let you decide. However I don't get any sensible values for accuracy. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary. See: In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. What is a word for the arcane equivalent of a monastery? As you commented, this in not the case here, you generate the data only once. Is your data source amenable to specialized network architectures? MathJax reference. I reduced the batch size from 500 to 50 (just trial and error). Often the simpler forms of regression get overlooked. 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. 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. I try to maximize the difference between the cosine similarities for the correct and wrong answers, correct answer representation should have a high similarity with the question/explanation representation while wrong answer should have a low similarity, and minimize this loss. 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. Without generalizing your model you will never find this issue. The safest way of standardizing packages is to use a requirements.txt file that outlines all your packages just like on your training system setup, down to the keras==2.1.5 version numbers. Finally, I append as comments all of the per-epoch losses for training and validation. What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? Is it possible to rotate a window 90 degrees if it has the same length and width? 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. The suggestions for randomization tests are really great ways to get at bugged networks. 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. There are 252 buckets. The experiments show that significant improvements in generalization can be achieved. But some recent research has found that SGD with momentum can out-perform adaptive gradient methods for neural networks. Other networks will decrease the loss, but only very slowly. Use MathJax to format equations. The main point is that the error rate will be lower in some point in time. Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by the magnitude of one order and "How do I choose a good schedule?"). See if the norm of the weights is increasing abnormally with epochs. 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.