In this guide, we are going to cover multi-label classification and the challenges we may face implementing it.eXtreme MultiLabel Classification in less than 5 minutes (with movie genres!)
In multi-label classification, one data sample can belong to multiple classes labels. Where in multi-class classification, one data sample can belong to only one class. In multi-class classification, the neural network has the same number of output nodes as the number of classes. Each output node belongs to some class and outputs a score for that class.
Scores from the last layer are passed through a softmax layer. The softmax layer converts the score into probability values. At last, data is classified into a corresponding class, that has the highest probability value. Following is the code snippet for softmax function. We can build a neural net for multi-class classification as following in Keras. This is how we do a multi-class classification.
The only difference is that a data sample can belong to multiple classes. We have to handle a few things differently in multi-label classification. The following diagram illustrates the multilabel classification. We can build a neural net for multi-label classification as following in Keras. These are all essential changes we have to make for multi-label classification. The main challenge in multi-label classification is data imbalance. And we can not simply use sampling techniques as we can in multi-class classification.
Data imbalance is a well-known problem in Machine Learning. Where some classes in the dataset are more frequent than others, and the neural net just learns to predict the frequent classes.
For example, if a dataset consists of cat and dog images. If we train the neural net on this data, it will just learn to predict dog every time. In this case, we can easily balance the data using sampling techniques.
However, this problem gets real when we have multi-label data. And one movie can belong to multiple genres. There are total of 16 types of genres. Even we have an ideal movie-genre dataset 40K sampleswhere all genres are equal in numbers. And each movie has an average of 2 genres. We still have an imbalanced dataset because the network is seeing each genre only In this case, the network just learns to predict no genre at all.
A lot of research has been done to tackle the data imbalance problem in multi-label classification, which I would be covering it in my future blogs.Please cite us if you use the software. Estimator score method : Estimators have a score method providing a default evaluation criterion for the problem they are designed to solve.
GridSearchCV rely on an internal scoring strategy. This is discussed in the section The scoring parameter: defining model evaluation rules. Metric functions : The metrics module implements functions assessing prediction error for specific purposes.
These metrics are detailed in sections on Classification metricsMultilabel ranking metricsRegression metrics and Clustering metrics. Finally, Dummy estimators are useful to get a baseline value of those metrics for random predictions.
For the most common use cases, you can designate a scorer object with the scoring parameter; the table below shows all possible values. All scorer objects follow the convention that higher return values are better than lower return values. Thus metrics which measure the distance between the model and the data, like metrics. The values listed by the ValueError exception correspond to the functions measuring prediction accuracy described in the following sections.
The scorer objects for those functions are stored in the dictionary sklearn. The module sklearn. In such cases, you need to generate an appropriate scoring object. That function converts metrics into callables that can be used for model evaluation.
If a loss, the output of the python function is negated by the scorer object, conforming to the cross validation convention that scorers return higher values for better models. The default value is False. For a callable to be a scorer, it needs to meet the protocol specified by the following two rules:. It can be called with parameters estimator, X, ywhere estimator is the model that should be evaluated, X is validation data, and y is the ground truth target for X in the supervised case or None in the unsupervised case.
It returns a floating point number that quantifies the estimator prediction quality on Xwith reference to y. Again, by convention higher numbers are better, so if your scorer returns loss, that value should be negated. While defining the custom scoring function alongside the calling function should work out of the box with the default joblib backend lokyimporting it from another module will be a more robust approach and work independently of the joblib backend.
There are two ways to specify multiple scoring metrics for the scoring parameter:. Note that the dict values can either be scorer functions or one of the predefined metric strings. Currently only those scorer functions that return a single score can be passed inside the dict.Using my app a user will upload a photo of clothing they like ex.
Is there a way to combine the three CNNs into a single network? To learn how to perform multi-label classification with Keras, just keep reading. Our dataset consists of 2, images across six categoriesincluding:. The goal of our Convolutional Neural network will be to predict both color and clothing type.
I created this dataset by following my previous tutorial on How to quickly build a deep learning image dataset. The entire process of downloading the images and manually removing irrelevant images for each of the six classes took approximately 30 minutes. From there, open up the smallervggnet. Changing this value from softmax to sigmoid will enable us to perform multi-label classification with Keras. This process of random disconnects naturally helps the network to reduce overfitting as no one single node in the layer will be responsible for predicting a certain class, object, edge, or corner.
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Notice the numbers of filters, kernels, and pool sizes in this code block which work together to progressively reduce the spatial size but increase depth.
Refer to Line 14 of this script, smallervggnet. In fact, you may want to view them on your screen side-by-side to see the difference and read full explanations. Open up train. If this is your first deep learning rodeo, you have two options to ensure you have the proper libraries and packages ready to go:.
The two pre-configured environments I recommend are:. If you insist on setting up your own environment and you have time to debug and troubleshootI suggest that you follow any of the following blog posts:.
Be sure to refer to the previous post as needed for explanations of these arguments. Additional detail is provided in the previous post. First, we load each image into memory Line GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
Already on GitHub? Sign in to your account. Or Do you give one simple example how to implement multi-label classification. I thought binary crossentropy was only for binary classification where y label is only 0 or 1.
Now that the y label is in the format of [1,0,1,0, Thanks,My last layer is softmax layer. I want to know how the accuracy is calculated? In your true labels, there are so many zeros, right? It's bounded, it's loss computation which is in proportion to the gradient applied is more plausible.
In that case it computes crossetnropy over each output and then compute their average. This issue has been automatically marked as stale because it has not had recent activity. It will be closed after 30 days if no further activity occurs, but feel free to re-open a closed issue if needed. I need to classify attributes in a face like colour of eye, hair, skin; facial hair, lighting and so on. Each has few sub-categories in it. Which one will be better in this case?
Or should I combine both as some subclasses are binary? I think you should have different loss functions for different outputs.
So I should choose binary cross entropy for binary-class classification and categorical-cross entropy for multi-class classification? And combine them together afterwards in the same model? And the loss functions can be a list or dictionary if you named the outputs. So I have three one hot encoded vectors. For a single on the loss function to choose would be categorical cross entropy.
What will Keras do in a case like these? Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. New issue. Jump to bottom. Labels stale. Copy link Quote reply.
I believe so.I hope it's clear from the labels. How exactly would you evaluate your model in the end?
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The output of the network is a float value between 0 and 1, but you want 1 true or 0 false as prediction in the end. So you have to find a threshold for each label. How is this done?
I have trouble coding out the accuracy since the prediction variable for normal one label classification requires the max. How do we work our way around this? Thank you Renthal. I just wasted 2 hours on this and finally read your comment. The code in this gist is incorrect. As Renthal said, the leftmost columns for each example should be the ground truth class indices.
The remaining columns should be filled with Of course, each example may belong to different number of classes. Skip to content.
Multi-label classification with Keras
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Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am training a multi-label classification model for detecting attributes of clothes. I am using transfer learning in Keras, retraining the last few layers of the vgg model.
Metrics like accuracy, precision, recall, etc.
Binary cross-entropy, hamming loss, etc. If we use this loss, we will train a CNN to output a probability over the C classes for each image. It is used for multi-class classification. It is a Sigmoid activation plus a Cross-Entropy loss. Unlike Softmax loss it is independent for each vector component classmeaning that the loss computed for every CNN output vector component is not affected by other component values.
Now for handling class imbalance, you can use weighted Sigmoid Cross-Entropy loss. Show the love and vote here. Learn more. Which loss function and metrics to use for multi-label classification with very high ratio of negatives to positives? Ask Question. Asked 4 months ago. Active 1 month ago. Viewed times. I am using the deep fashion dataset. So, which metrics and loss functions can I use to measure my model correctly?
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Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. I'm training a neural network to classify a set of objects into n-classes. Each object can belong to multiple classes at the same time multi-class, multi-label. I read that for multi-class problems it is generally recommended to use softmax and categorical cross entropy as the loss function instead of mse and I understand more or less why.
For my problem of multi-label it wouldn't make sense to use softmax of course as each class probability should be independent from the other. So my final layer is just sigmoid units that squash their inputs into a probability range Now I'm not sure what loss function I should use for this. Looking at the definition of categorical crossentropy I believe it would not apply well to this problem as it will only take into account the output of neurons that should be 1 and ignores the others.
Binary cross entropy sounds like it would fit better, but I only see it ever mentioned for binary classification problems with a single output neuron. But for my case this direct loss function was not converging. You can make your own like in this Example. All that is required is that each row of labels is a valid probability distribution. If they are not, the computation of the gradient will be incorrect. The trick is to model the partition function and the distribution separately, thus exploiting the power of softmax.
So the objective would be to to model the matrix in a per-sample manner. Then it's a matter of modeling the two separately. More sophisticated modeling like Poisson unit would probably work better. Then you can choose to apply distributed loss KL on distribution and MSE on partitionor you can try the following loss on their product.
In practical, the choice of optimiser also makes a huge difference. My experience with the factorisation approach is it works best under Adadelta Adagrad dont work for me, didnt try RMSprop yet, performances of SGD is subject to parameter. My experience with sigmoid cross-entropy was not very pleasant. At the moment I am using a modified KL-divergence. It takes the form. They are called pseudo-distributions for not being normalised. I haven't used keras yet.
I'm a newbie here but I'll try give it a shot with this question. The author of that tutorial use categorical cross entropy loss function, and there is other thread that may help you to find solution here.