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. Conceptually this means that your output is heavily saturated, for example toward 0. Did you need to set anything else? Where $a$ is your learning rate, $t$ is your iteration number and $m$ is a coefficient that identifies learning rate decreasing speed. If it can't learn a single point, then your network structure probably can't represent the input -> output function and needs to be redesigned. Redoing the align environment with a specific formatting. For example, it's widely observed that layer normalization and dropout are difficult to use together. or bAbI. I don't know why that is. Towards a Theoretical Understanding of Batch Normalization, How Does Batch Normalization Help Optimization? But some recent research has found that SGD with momentum can out-perform adaptive gradient methods for neural networks. If you preorder a special airline meal (e.g. train the neural network, while at the same time controlling the loss on the validation set. I'm building a lstm model for regression on timeseries. Nowadays, many frameworks have built in data pre-processing pipeline and augmentation. 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. Linear Algebra - Linear transformation question. This can be done by setting the validation_split argument on fit () to use a portion of the training data as a validation dataset. The challenges of training neural networks are well-known (see: Why is it hard to train deep neural networks?). If you observed this behaviour you could use two simple solutions. Why do we use ReLU in neural networks and how do we use it? Even when a neural network code executes without raising an exception, the network can still have bugs! 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. (But I don't think anyone fully understands why this is the case.) And these elements may completely destroy the data. Then you can take a look at your hidden-state outputs after every step and make sure they are actually different. 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. That probably did fix wrong activation method. In particular, you should reach the random chance loss on the test set. I just attributed that to a poor choice for the accuracy-metric and haven't given it much thought. Finally, the best way to check if you have training set issues is to use another training set. "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. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Before combining $f(\mathbf x)$ with several other layers, generate a random target vector $\mathbf y \in \mathbb R^k$. Instead, I do that in a configuration file (e.g., JSON) that is read and used to populate network configuration details at runtime. 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. Set up a very small step and train it. ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. Dropout is used during testing, instead of only being used for training. The difference between the phonemes /p/ and /b/ in Japanese, Short story taking place on a toroidal planet or moon involving flying. Styling contours by colour and by line thickness in QGIS. What image preprocessing routines do they use? I couldn't obtained a good validation loss as my training loss was decreasing. We've added a "Necessary cookies only" option to the cookie consent popup. 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. Does not being able to overfit a single training sample mean that the neural network architecure or implementation is wrong? Did this satellite streak past the Hubble Space Telescope so close that it was out of focus? 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. 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. I think what you said must be on the right track. 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. Dealing with such a Model: Data Preprocessing: Standardizing and Normalizing the data. import imblearn import mat73 import keras from keras.utils import np_utils import os. However I don't get any sensible values for accuracy. Why is this the case? 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. This Medium post, "How to unit test machine learning code," by Chase Roberts discusses unit-testing for machine learning models in more detail. Connect and share knowledge within a single location that is structured and easy to search. Okay, so this explains why the validation score is not worse. 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. What am I doing wrong here in the PlotLegends specification? 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. I have prepared the easier set, selecting cases where differences between categories were seen by my own perception as more obvious. Ive seen a number of NN posts where OP left a comment like oh I found a bug now it works.. rev2023.3.3.43278. Learn more about Stack Overflow the company, and our products. But adding too many hidden layers can make risk overfitting or make it very hard to optimize the network. 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. I worked on this in my free time, between grad school and my job. Is there a solution if you can't find more data, or is an RNN just the wrong model? Decrease the initial learning rate using the 'InitialLearnRate' option of trainingOptions. Choosing the number of hidden layers lets the network learn an abstraction from the raw data. 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. Replacing broken pins/legs on a DIP IC package. How to match a specific column position till the end of line? 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. $f(\mathbf x) = \alpha(\mathbf W \mathbf x + \mathbf b)$, $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$, $\mathbf y = \begin{bmatrix}1 & 0 & 0 & \cdots & 0\end{bmatrix}$. Other explanations might be that this is because your network does not have enough trainable parameters to overfit, coupled with a relatively large number of training examples (and of course, generating the training and the validation examples with the same process). Welcome to DataScience. It is very weird. What's the best way to answer "my neural network doesn't work, please fix" questions? so given an explanation/context and a question, it is supposed to predict the correct answer out of 4 options. This verifies a few things. Before I was knowing that this is wrong, I did add Batch Normalisation layer after every learnable layer, and that helps. What is the purpose of this D-shaped ring at the base of the tongue on my hiking boots? Then I realized that it is enough to put Batch Normalisation before that last ReLU activation layer only, to keep improving loss/accuracy during training. Experiments on standard benchmarks show that Padam can maintain fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. Do not train a neural network to start with! +1, but "bloody Jupyter Notebook"? One way for implementing curriculum learning is to rank the training examples by difficulty. This paper introduces a physics-informed machine learning approach for pathloss prediction. Training loss goes down and up again. It only takes a minute to sign up. The problem I find is that the models, for various hyperparameters I try (e.g. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. 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. ), @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. 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. 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. What is a word for the arcane equivalent of a monastery? \alpha(t + 1) = \frac{\alpha(0)}{1 + \frac{t}{m}} (For example, the code may seem to work when it's not correctly implemented. It only takes a minute to sign up. See: Gradient clipping re-scales the norm of the gradient if it's above some threshold. Asking for help, clarification, or responding to other answers. What to do if training loss decreases but validation loss does not decrease? Many of the different operations are not actually used because previous results are over-written with new variables. Why zero amount transaction outputs are kept in Bitcoin Core chainstate database? Finally, I append as comments all of the per-epoch losses for training and validation. Do they first resize and then normalize the image? This is because your model should start out close to randomly guessing. This is a very active area of research. Testing on a single data point is a really great idea. Not the answer you're looking for? How can this new ban on drag possibly be considered constitutional? First one is a simplest one. $\endgroup$ +1 for "All coding is debugging". No change in accuracy using Adam Optimizer when SGD works fine. visualize the distribution of weights and biases for each layer. 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. How can change in cost function be positive? I knew a good part of this stuff, what stood out for me is. Switch the LSTM to return predictions at each step (in keras, this is return_sequences=True). First, build a small network with a single hidden layer and verify that it works correctly. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. So if you're downloading someone's model from github, pay close attention to their preprocessing. 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.). . Too many neurons can cause over-fitting because the network will "memorize" the training data. Since either on its own is very useful, understanding how to use both is an active area of research. As the OP was using Keras, another option to make slightly more sophisticated learning rate updates would be to use a callback like. Then incrementally add additional model complexity, and verify that each of those works as well. Connect and share knowledge within a single location that is structured and easy to search. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. What's the channel order for RGB images? I'm possibly being too negative, but frankly I've had enough with people cloning Jupyter Notebooks from GitHub, thinking it would be a matter of minutes to adapt the code to their use case and then coming to me complaining that nothing works. Neural networks in particular are extremely sensitive to small changes in your data. First, it quickly shows you that your model is able to learn by checking if your model can overfit your data. This informs us as to whether the model needs further tuning or adjustments or not. any suggestions would be appreciated. and "How do I choose a good schedule?"). Using Kolmogorov complexity to measure difficulty of problems? Is it possible to share more info and possibly some code? Find centralized, trusted content and collaborate around the technologies you use most. 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. I agree with your analysis. In my case the initial training set was probably too difficult for the network, so it was not making any progress. 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. My recent lesson is trying to detect if an image contains some hidden information, by stenography tools. The cross-validation loss tracks the training loss. Some common mistakes here are. The comparison between the training loss and validation loss curve guides you, of course, but don't underestimate the die hard attitude of NNs (and especially DNNs): they often show a (maybe slowly) decreasing training/validation loss even when you have crippling bugs in your code. Connect and share knowledge within a single location that is structured and easy to search. I understand that it might not be feasible, but very often data size is the key to success. Styling contours by colour and by line thickness in QGIS. Lots of good advice there. How to Diagnose Overfitting and Underfitting of LSTM Models; Overfitting and Underfitting With Machine Learning Algorithms; Articles. In the Machine Learning Course by Andrew Ng, he suggests running Gradient Checking in the first few iterations to make sure the backpropagation is doing the right thing. A place where magic is studied and practiced? Is it possible to rotate a window 90 degrees if it has the same length and width? :). By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Check the accuracy on the test set, and make some diagnostic plots/tables. Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. If this works, train it on two inputs with different outputs. You need to test all of the steps that produce or transform data and feed into the network. If nothing helped, it's now the time to start fiddling with hyperparameters. (See: Why do we use ReLU in neural networks and how do we use it?) Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? How to match a specific column position till the end of line? Making statements based on opinion; back them up with references or personal experience. The best answers are voted up and rise to the top, Not the answer you're looking for? . Why is Newton's method not widely used in machine learning? 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. Double check your input data. As an example, two popular image loading packages are cv2 and PIL. 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. To learn more, see our tips on writing great answers. 1 2 . 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 learn more, see our tips on writing great answers. Use MathJax to format equations. Learn more about Stack Overflow the company, and our products. I added more features, which I thought intuitively would add some new intelligent information to the X->y pair. pixel values are in [0,1] instead of [0, 255]). The 'validation loss' metrics from the test data has been oscillating a lot after epochs but not really decreasing. Weight changes but performance remains the same. 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. Accuracy (0-1 loss) is a crappy metric if you have strong class imbalance. 'Jupyter notebook' and 'unit testing' are anti-correlated. 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. As you commented, this in not the case here, you generate the data only once. The main point is that the error rate will be lower in some point in time. See: In training a triplet network, I first have a solid drop in loss, but eventually the loss slowly but consistently increases. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. history = model.fit(X, Y, epochs=100, validation_split=0.33) The funny thing is that they're half right: coding, It is really nice answer. 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. 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?. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? As a simple example, suppose that we are classifying images, and that we expect the output to be the $k$-dimensional vector $\mathbf y = \begin{bmatrix}1 & 0 & 0 & \cdots & 0\end{bmatrix}$. Too few neurons in a layer can restrict the representation that the network learns, causing under-fitting. I just learned this lesson recently and I think it is interesting to share. See, There are a number of other options. I checked and found while I was using LSTM: Thanks for contributing an answer to Data Science Stack Exchange! I just copied the code above (fixed the scaler bug) and reran it on CPU. 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). By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Neglecting to do this (and the use of the bloody Jupyter Notebook) are usually the root causes of issues in NN code I'm asked to review, especially when the model is supposed to be deployed in production. How do you ensure that a red herring doesn't violate Chekhov's gun? Other networks will decrease the loss, but only very slowly. There's a saying among writers that "All writing is re-writing" -- that is, the greater part of writing is revising. A lot of times you'll see an initial loss of something ridiculous, like 6.5. ncdu: What's going on with this second size column? It only takes a minute to sign up. I borrowed this example of buggy code from the article: Do you see the error? 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). Give or take minor variations that result from the random process of sample generation (even if data is generated only once, but especially if it is generated anew for each epoch). as a particular form of continuation method (a general strategy for global optimization of non-convex functions). 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 . Go back to point 1 because the results aren't good. How Intuit democratizes AI development across teams through reusability. Can archive.org's Wayback Machine ignore some query terms? Just as it is not sufficient to have a single tumbler in the right place, neither is it sufficient to have only the architecture, or only the optimizer, set up correctly. Making sure that your model can overfit is an excellent idea. These data sets are well-tested: if your training loss goes down here but not on your original data set, you may have issues in the data set. What should I do when my neural network doesn't generalize well? Is it suspicious or odd to stand by the gate of a GA airport watching the planes? Solutions to this are to decrease your network size, or to increase dropout. 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. 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. Why are physically impossible and logically impossible concepts considered separate in terms of probability? 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. 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. I am training a LSTM model to do question answering, i.e. The differences are usually really small, but you'll occasionally see drops in model performance due to this kind of stuff. Learning . Why is this sentence from The Great Gatsby grammatical? Especially if you plan on shipping the model to production, it'll make things a lot easier. normalize or standardize the data in some way. Are there tables of wastage rates for different fruit and veg? Loss was constant 4.000 and accuracy 0.142 on 7 target values dataset. The validation loss slightly increase such as from 0.016 to 0.018. 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. learning rate) is more or less important than another (e.g. Multi-layer perceptron vs deep neural network, My neural network can't even learn Euclidean distance. Can archive.org's Wayback Machine ignore some query terms? Connect and share knowledge within a single location that is structured and easy to search. Check the data pre-processing and augmentation. 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. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? Any time you're writing code, you need to verify that it works as intended. If your neural network does not generalize well, see: What should I do when my neural network doesn't generalize well? 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). 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. Care to comment on that? I reduced the batch size from 500 to 50 (just trial and error). 6 Answers Sorted by: 36 The model is overfitting right from epoch 10, the validation loss is increasing while the training loss is decreasing. (+1) Checking the initial loss is a great suggestion. Thanks for contributing an answer to Data Science Stack Exchange! What am I doing wrong here in the PlotLegends specification? 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. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. What could cause this? In all other cases, the optimization problem is non-convex, and non-convex optimization is hard. What can be the actions to decrease? Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? 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. Likely a problem with the data? If the loss decreases consistently, then this check has passed. Or the other way around? Why do many companies reject expired SSL certificates as bugs in bug bounties? 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. Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? It also hedges against mistakenly repeating the same dead-end experiment. Then I add each regularization piece back, and verify that each of those works along the way. To set the gradient threshold, use the 'GradientThreshold' option in trainingOptions. ncdu: What's going on with this second size column? And the loss in the training looks like this: Is there anything wrong with these codes? Then training proceed with online hard negative mining, and the model is better for it as a result. However training as well as validation loss pretty much converge to zero, so I guess we can conclude that the problem is to easy because training and validation data are generated in exactly the same way.
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