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. 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. The validation loss is similar to the training loss and is calculated from a sum of the errors for each example in the validation set. 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). 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. Many of the different operations are not actually used because previous results are over-written with new variables. This tactic can pinpoint where some regularization might be poorly set. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Why is it hard to train deep neural networks? This step is not as trivial as people usually assume it to be. Dealing with such a Model: Data Preprocessing: Standardizing and Normalizing the data. 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. The Marginal Value of Adaptive Gradient Methods in Machine Learning, Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. So if you're downloading someone's model from github, pay close attention to their preprocessing. But for my case, training loss still goes down but validation loss stays at same level. 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. Might be an interesting experiment. Can I add data, that my neural network classified, to the training set, in order to improve it? Then you can take a look at your hidden-state outputs after every step and make sure they are actually different. Thanks for contributing an answer to Data Science Stack Exchange! 12 that validation loss and test loss keep decreasing when the training rounds are before 30 times. Just by virtue of opening a JPEG, both these packages will produce slightly different images. Asking for help, clarification, or responding to other answers. Making statements based on opinion; back them up with references or personal experience. Is your data source amenable to specialized network architectures? Specifically for triplet-loss models, there are a number of tricks which can improve training time and generalization. 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 usually happens when your neural network weights aren't properly balanced, especially closer to the softmax/sigmoid. and i used keras framework to build the network, but it seems the NN can't be build up easily. Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by the magnitude of one order +1, but "bloody Jupyter Notebook"? 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. To learn more, see our tips on writing great answers. First one is a simplest one. What is the purpose of this D-shaped ring at the base of the tongue on my hiking boots? See: Gradient clipping re-scales the norm of the gradient if it's above some threshold. You might want to simplify your architecture to include just a single LSTM layer (like I did) just until you convince yourself that the model is actually learning something. 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. Here is a simple formula: $$ 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. One way for implementing curriculum learning is to rank the training examples by difficulty. Learn more about Stack Overflow the company, and our products. In the given base model, there are 2 hidden Layers, one with 128 and one with 64 neurons. I reduced the batch size from 500 to 50 (just trial and error). First, it quickly shows you that your model is able to learn by checking if your model can overfit your data. 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! To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Why is this sentence from The Great Gatsby grammatical? I then pass the answers through an LSTM to get a representation (50 units) of the same length for answers. Scaling the testing data using the statistics of the test partition instead of the train partition; Forgetting to un-scale the predictions (e.g. I understand that it might not be feasible, but very often data size is the key to success. The 'validation loss' metrics from the test data has been oscillating a lot after epochs but not really decreasing. Likely a problem with the data? Dropout is used during testing, instead of only being used for training. Recurrent neural networks can do well on sequential data types, such as natural language or time series data. Is it suspicious or odd to stand by the gate of a GA airport watching the planes? 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. $$. This is an easier task, so the model learns a good initialization before training on the real task. Residual connections can improve deep feed-forward networks. Why are Suriname, Belize, and Guinea-Bissau classified as "Small Island Developing States"? ncdu: What's going on with this second size column? Trying to understand how to get this basic Fourier Series, Linear Algebra - Linear transformation question. What's the difference between a power rail and a signal line? Conceptually this means that your output is heavily saturated, for example toward 0. And these elements may completely destroy the data. Some common mistakes here are. Welcome to DataScience. My recent lesson is trying to detect if an image contains some hidden information, by stenography tools. 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. Continuing the binary example, if your data is 30% 0's and 70% 1's, then your intial expected loss around $L=-0.3\ln(0.5)-0.7\ln(0.5)\approx 0.7$. Loss was constant 4.000 and accuracy 0.142 on 7 target values dataset. 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 So this would tell you if your initialization is bad. How to react to a students panic attack in an oral exam? The problem I find is that the models, for various hyperparameters I try (e.g. Also, when it comes to explaining your model, someone will come along and ask "what's the effect of $x_k$ on the result?" By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Suppose that the softmax operation was not applied to obtain $\mathbf y$ (as is normally done), and suppose instead that some other operation, called $\delta(\cdot)$, that is also monotonically increasing in the inputs, was applied instead. The best method I've ever found for verifying correctness is to break your code into small segments, and verify that each segment works. Then training proceed with online hard negative mining, and the model is better for it as a result. What could cause my neural network model's loss increases dramatically? Nowadays, many frameworks have built in data pre-processing pipeline and augmentation. Lots of good advice there. I worked on this in my free time, between grad school and my job. Learn more about Stack Overflow the company, and our products. 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. It takes 10 minutes just for your GPU to initialize your model. rev2023.3.3.43278. There's a saying among writers that "All writing is re-writing" -- that is, the greater part of writing is revising. See this Meta thread for a discussion: What's the best way to answer "my neural network doesn't work, please fix" questions? Basically, the idea is to calculate the derivative by defining two points with a $\epsilon$ interval. What's the difference between a power rail and a signal line? It is very weird. 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. Learn more about Stack Overflow the company, and our products. Your learning could be to big after the 25th epoch. Where does this (supposedly) Gibson quote come from? (This is an example of the difference between a syntactic and semantic error.). or bAbI. We hypothesize that This leaves how to close the generalization gap of adaptive gradient methods an open problem. I am so used to thinking about overfitting as a weakness that I never explicitly thought (until you mentioned it) that the. Making sure that your model can overfit is an excellent idea. You have to check that your code is free of bugs before you can tune network performance! train the neural network, while at the same time controlling the loss on the validation set. Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. What is the essential difference between neural network and linear regression. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". 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). Check that the normalized data are really normalized (have a look at their range). That probably did fix wrong activation method. While this is highly dependent on the availability of data. It could be that the preprocessing steps (the padding) are creating input sequences that cannot be separated (perhaps you are getting a lot of zeros or something of that sort). 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. How to match a specific column position till the end of line? It only takes a minute to sign up. How to handle hidden-cell output of 2-layer LSTM in PyTorch? My training loss goes down and then up again. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? Replacing broken pins/legs on a DIP IC package. Ive seen a number of NN posts where OP left a comment like oh I found a bug now it works.. Neural networks and other forms of ML are "so hot right now". rev2023.3.3.43278. If your neural network does not generalize well, see: What should I do when my neural network doesn't generalize well? This will avoid gradient issues for saturated sigmoids, at the output. If we do not trust that $\delta(\cdot)$ is working as expected, then since we know that it is monotonically increasing in the inputs, then we can work backwards and deduce that the input must have been a $k$-dimensional vector where the maximum element occurs at the first element. Did you need to set anything else? In particular, you should reach the random chance loss on the test set. $L^2$ regularization (aka weight decay) or $L^1$ regularization is set too large, so the weights can't move. 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! If you preorder a special airline meal (e.g. As an example, two popular image loading packages are cv2 and PIL. The essential idea of curriculum learning is best described in the abstract of the previously linked paper by Bengio et al. 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. Otherwise, you might as well be re-arranging deck chairs on the RMS Titanic. import imblearn import mat73 import keras from keras.utils import np_utils import os. Before combining $f(\mathbf x)$ with several other layers, generate a random target vector $\mathbf y \in \mathbb R^k$. This is achieved by including in the training phase simultaneously (i) physical dependencies between. No change in accuracy using Adam Optimizer when SGD works fine. Connect and share knowledge within a single location that is structured and easy to search. The suggestions for randomization tests are really great ways to get at bugged networks. If I run your code (unchanged - on a GPU), then the model doesn't seem to train. I had this issue - while training loss was decreasing, the validation loss was not decreasing. 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. I had a model that did not train at all. Can archive.org's Wayback Machine ignore some query terms? 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. Why do many companies reject expired SSL certificates as bugs in bug bounties? Did any DOS compatibility layers exist for any UNIX-like systems before DOS started to become outmoded? If so, how close was it? My model look like this: And here is the function for each training sample. Do not train a neural network to start with! 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. 3) Generalize your model outputs to debug. The best answers are voted up and rise to the top, Not the answer you're looking for? To make sure the existing knowledge is not lost, reduce the set learning rate. rev2023.3.3.43278. Where $a$ is your learning rate, $t$ is your iteration number and $m$ is a coefficient that identifies learning rate decreasing speed. 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. visualize the distribution of weights and biases for each layer. (which could be considered as some kind of testing). Why does Mister Mxyzptlk need to have a weakness in the comics? I have prepared the easier set, selecting cases where differences between categories were seen by my own perception as more obvious. 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?. Asking for help, clarification, or responding to other answers. This problem is easy to identify. What should I do when my neural network doesn't learn? Wide and deep neural networks, and neural networks with exotic wiring, are the Hot Thing right now in machine learning. Any advice on what to do, or what is wrong? And when the training rounds are after 30 times validation loss and test loss tend to be stable after 30 training . Training loss goes up and down regularly. But some recent research has found that SGD with momentum can out-perform adaptive gradient methods for neural networks. This is because your model should start out close to randomly guessing. 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. 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. 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. +1 for "All coding is debugging". (+1) This is a good write-up. Testing on a single data point is a really great idea. Thanks @Roni. \alpha(t + 1) = \frac{\alpha(0)}{1 + \frac{t}{m}} (LSTM) models you are looking at data that is adjusted according to the data . And the loss in the training looks like this: Is there anything wrong with these codes? Styling contours by colour and by line thickness in QGIS. See if the norm of the weights is increasing abnormally with epochs. 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. So this does not explain why you do not see overfit. 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. 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. What is going on? Minimising the environmental effects of my dyson brain. What Is the Difference Between 'Man' And 'Son of Man' in Num 23:19? 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. ), The most common programming errors pertaining to neural networks are, Unit testing is not just limited to the neural network itself. 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. 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). Styling contours by colour and by line thickness in QGIS. Instead, I do that in a configuration file (e.g., JSON) that is read and used to populate network configuration details at runtime. How can this new ban on drag possibly be considered constitutional? You can study this further by making your model predict on a few thousand examples, and then histogramming the outputs. Do I need a thermal expansion tank if I already have a pressure tank? 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, let $\ell (\mathbf x,\mathbf y) = (f(\mathbf x) - \mathbf y)^2$ be a loss function. There are a number of variants on stochastic gradient descent which use momentum, adaptive learning rates, Nesterov updates and so on to improve upon vanilla SGD. +1 Learning like children, starting with simple examples, not being given everything at once! One caution about ReLUs is the "dead neuron" phenomenon, which can stymie learning; leaky relus and similar variants avoid this problem. 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. As you commented, this in not the case here, you generate the data only once. If your training/validation loss are about equal then your model is underfitting. What's the channel order for RGB images? 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 How to react to a students panic attack in an oral exam? 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 validation loss < training loss and validation accuracy < training accuracy, Keras stateful LSTM returns NaN for validation loss, Validation loss keeps fluctuating about training loss, Validation loss is lower than the training loss, Understanding output of LSTM for regression, Understanding Training and Test Loss Plots, Understanding LSTM Training and Validation Graph and their metrics (LSTM Keras), Validation loss much higher than training loss, LSTM RNN regression: validation loss erratic during training. 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. 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. It only takes a minute to sign up. Making statements based on opinion; back them up with references or personal experience. Prior to presenting data to a neural network. 6) Standardize your Preprocessing and Package Versions. And struggled for a long time that the model does not learn. I just copied the code above (fixed the scaler bug) and reran it on CPU. The reason is many packages are rescaling images to certain size and this operation completely destroys the hidden information inside. normalize or standardize the data in some way. The lstm_size can be adjusted . Then I add each regularization piece back, and verify that each of those works along the way. Tuning configuration choices is not really as simple as saying that one kind of configuration choice (e.g. This can help make sure that inputs/outputs are properly normalized in each layer. How to handle a hobby that makes income in US. Most of the entries in the NAME column of the output from lsof +D /tmp do not begin with /tmp. Deep learning is all the rage these days, and networks with a large number of layers have shown impressive results. @Alex R. I'm still unsure what to do if you do pass the overfitting test. What to do if training loss decreases but validation loss does not decrease? Why is this the case? rev2023.3.3.43278. This question is intentionally general so that other questions about how to train a neural network can be closed as a duplicate of this one, with the attitude that "if you give a man a fish you feed him for a day, but if you teach a man to fish, you can feed him for the rest of his life." ), have a look at a few samples (to make sure the import has gone well) and perform data cleaning if/when needed. My code is GPL licensed, can I issue a license to have my code be distributed in a specific MIT licensed project? Ok, rereading your code I can obviously see that you are correct; I will edit my answer. The posted answers are great, and I wanted to add a few "Sanity Checks" which have greatly helped me in the past. I'm building a lstm model for regression on timeseries. (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. Do new devs get fired if they can't solve a certain bug? 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. 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.