32 Keras Multi Label Classification

Text classification is a common task where machine learning is applied. The dataset well be using in todays keras multi label classification tutorial is meant to mimic switajs question at the top of this post although slightly simplified for the sake of the blog post.

Deep Neural Network For Hierarchical Extreme Multi Label Text

Performing multi label text classification with keras.

Keras multi label classification. Data imbalance is a well known problem in machine learning. Be it questions on a qa platform a support request an insurance claim or a business inquiry all of these are usually written in free form text and use vocabulary which might be specific to a certain field. There are two ways to create multi label classification models.

This type of problem comes under multi label image classification where an instance can be classified into multiple classes among the predefined classes. Our dataset consists of 2167 images across six categories including. In the first approach we can use a single dense layer with six outputs with a sigmoid activation functions and binary cross entropy loss functions.

Using single dense output layer and using multiple dense output layers. And we can not simply use sampling techniques as we can in multi class classification. This model capable of detecting different types of toxicity like threats obscenity insults and identity based hate.

In this tutorial we create a multi label text classification model for predicts a probability of each type of toxicity for each comment. Multi label text classification in tensorflow keras. The main challenge in multi label classification is data imbalance.

Black jeans 344 images blue dress 386 images blue jeans 356 images. Keras multi label image classification the objective of this study is to develop a deep learning model that will identify the natural scenes from images.

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