lstm validation loss not decreasing

How to handle hidden-cell output of 2-layer LSTM in PyTorch? Some examples: When it first came out, the Adam optimizer generated a lot of interest. For cripes' sake, get a real IDE such as PyCharm or VisualStudio Code and create a well-structured code, rather than cooking up a Notebook! What could cause this? 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). I don't know why that is. Where does this (supposedly) Gibson quote come from? For an example of such an approach you can have a look at my experiment. There's a saying among writers that "All writing is re-writing" -- that is, the greater part of writing is revising. One caution about ReLUs is the "dead neuron" phenomenon, which can stymie learning; leaky relus and similar variants avoid this problem. normalize or standardize the data in some way. This is called unit testing. This can help make sure that inputs/outputs are properly normalized in each layer. Okay, so this explains why the validation score is not worse. As the OP was using Keras, another option to make slightly more sophisticated learning rate updates would be to use a callback like. Towards a Theoretical Understanding of Batch Normalization, How Does Batch Normalization Help Optimization? Before combining $f(\mathbf x)$ with several other layers, generate a random target vector $\mathbf y \in \mathbb R^k$. I just attributed that to a poor choice for the accuracy-metric and haven't given it much thought. Neural networks in particular are extremely sensitive to small changes in your data. The asker was looking for "neural network doesn't learn" so I majored there. 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. "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks" by Jinghui Chen, Quanquan Gu. +1 Learning like children, starting with simple examples, not being given everything at once! This will avoid gradient issues for saturated sigmoids, at the output. It is very weird. ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. What video game is Charlie playing in Poker Face S01E07? A place where magic is studied and practiced? If you want to write a full answer I shall accept it. This means that if you have 1000 classes, you should reach an accuracy of 0.1%. Why are physically impossible and logically impossible concepts considered separate in terms of probability? Likely a problem with the data? Training loss goes down and up again. In the given base model, there are 2 hidden Layers, one with 128 and one with 64 neurons. If you preorder a special airline meal (e.g. What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? Problem is I do not understand what's going on here. 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. $$. Is there a proper earth ground point in this switch box? If it is indeed memorizing, the best practice is to collect a larger dataset. Do new devs get fired if they can't solve a certain bug? I had a model that did not train at all. Why is it hard to train deep neural networks? Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Replacing broken pins/legs on a DIP IC package. For example, it's widely observed that layer normalization and dropout are difficult to use together. This is especially useful for checking that your data is correctly normalized. The network initialization is often overlooked as a source of neural network bugs. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Choosing a clever network wiring can do a lot of the work for you. Making sure that your model can overfit is an excellent idea. 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). 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 particular, you should reach the random chance loss on the test set. For example, suppose we are building a classifier to classify 6 and 9, and we use random rotation augmentation Why can't scikit-learn SVM solve two concentric circles? 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. (which could be considered as some kind of testing). Especially if you plan on shipping the model to production, it'll make things a lot easier. 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"? 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. Here, we formalize such training strategies in the context of machine learning, and call them curriculum learning. Learn more about Stack Overflow the company, and our products. I am getting different values for the loss function per epoch. Choosing the number of hidden layers lets the network learn an abstraction from the raw data. (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. Learning . Is it possible to create a concave light? If the model isn't learning, there is a decent chance that your backpropagation is not working. history = model.fit(X, Y, epochs=100, validation_split=0.33) +1, but "bloody Jupyter Notebook"? I knew a good part of this stuff, what stood out for me is. Minimising the environmental effects of my dyson brain. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? 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. here is my code and my outputs: Ok, rereading your code I can obviously see that you are correct; I will edit my answer. This means writing code, and writing code means debugging. MathJax reference. While this is highly dependent on the availability of data. Find centralized, trusted content and collaborate around the technologies you use most. 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. Continuing the binary example, if your data is 30% 0's and 70% 1's, then your intial expected loss around $L=-0.3\ln(0.5)-0.7\ln(0.5)\approx 0.7$. It only takes a minute to sign up. So this would tell you if your initialization is bad. Why is this the case? Just by virtue of opening a JPEG, both these packages will produce slightly different images. What image loaders do they use? How to match a specific column position till the end of line? Setting the learning rate too large will cause the optimization to diverge, because you will leap from one side of the "canyon" to the other. Other people insist that scheduling is essential. How can I fix this? Even when a neural network code executes without raising an exception, the network can still have bugs! 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. Then you can take a look at your hidden-state outputs after every step and make sure they are actually different. Why does $[0,1]$ scaling dramatically increase training time for feed forward ANN (1 hidden layer)? Neural Network - Estimating Non-linear function, Poor recurrent neural network performance on sequential 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. See if you inverted the training set and test set labels, for example (happened to me once -___-), or if you imported the wrong file. You need to test all of the steps that produce or transform data and feed into the network. Can I tell police to wait and call a lawyer when served with a search warrant? The differences are usually really small, but you'll occasionally see drops in model performance due to this kind of stuff. Multi-layer perceptron vs deep neural network, My neural network can't even learn Euclidean distance. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? Finally, I append as comments all of the per-epoch losses for training and validation. (But I don't think anyone fully understands why this is the case.) When resizing an image, what interpolation do they use? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. What am I doing wrong here in the PlotLegends specification? Try to set up it smaller and check your loss again. 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). Dropout is used during testing, instead of only being used for training. I had this issue - while training loss was decreasing, the validation loss was not decreasing. I'm building a lstm model for regression on timeseries. Then training proceed with online hard negative mining, and the model is better for it as a result. (See: Why do we use ReLU in neural networks and how do we use it?) My model look like this: And here is the function for each training sample. Residual connections can improve deep feed-forward networks. This will help you make sure that your model structure is correct and that there are no extraneous issues. And these elements may completely destroy the data. visualize the distribution of weights and biases for each layer. 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. To make sure the existing knowledge is not lost, reduce the set learning rate. 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! (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). See: In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. To learn more, see our tips on writing great answers. This problem is easy to identify. 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. Is it possible to rotate a window 90 degrees if it has the same length and width? Accuracy (0-1 loss) is a crappy metric if you have strong class imbalance. it is shown in Fig. Double check your input data. 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 never had to get here, but if you're using BatchNorm, you would expect approximately standard normal distributions. How do you ensure that a red herring doesn't violate Chekhov's gun? If you haven't done so, you may consider to work with some benchmark dataset like SQuAD Specifically for triplet-loss models, there are a number of tricks which can improve training time and generalization. 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. How to react to a students panic attack in an oral exam? Curriculum learning is a formalization of @h22's answer. The first step when dealing with overfitting is to decrease the complexity of the model. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? This can be done by comparing the segment output to what you know to be the correct answer. 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 We can then generate a similar target to aim for, rather than a random one. Do they first resize and then normalize the image? 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. and "How do I choose a good schedule?"). To learn more, see our tips on writing great answers. learning rate) is more or less important than another (e.g. :). From this I calculate 2 cosine similarities, one for the correct answer and one for the wrong answer, and define my loss to be a hinge loss, i.e. Calculating probabilities from d6 dice pool (Degenesis rules for botches and triggers), Minimising the environmental effects of my dyson brain. This is a very active area of research. What should I do when my neural network doesn't learn? For me, the validation loss also never decreases. Making statements based on opinion; back them up with references or personal experience. 6 Answers Sorted by: 36 The model is overfitting right from epoch 10, the validation loss is increasing while the training loss is decreasing. Why does momentum escape from a saddle point in this famous image? 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. The challenges of training neural networks are well-known (see: Why is it hard to train deep neural networks?). 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. 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. Then incrementally add additional model complexity, and verify that each of those works as well. Scaling the testing data using the statistics of the test partition instead of the train partition; Forgetting to un-scale the predictions (e.g. The posted answers are great, and I wanted to add a few "Sanity Checks" which have greatly helped me in the past. My dataset contains about 1000+ examples. A standard neural network is composed of layers. 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. What should I do? 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.

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