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https://github.com/adityashrm21/tensorflowjs-live-classification-iris

A tensorflow.js web application to perform classification and prediction on the Iris dataset.
https://github.com/adityashrm21/tensorflowjs-live-classification-iris

classification iris-dataset keras keras-tensorflow tensorflow tensorflow-experiments tensorflow-js tensorflow-tutorials tensorflowjs

Last synced: 13 days ago
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A tensorflow.js web application to perform classification and prediction on the Iris dataset.

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# Live Classification using TensorFlow.JS on Iris dataset

A tensorflow.js web application to perform classification and prediction on the Iris dataset using TensorFlow Keras API in JavaScript.



## Live Demo



  



## Dataset



The dataset used is Iris and the two linearly separable species (Virginica and Versicolor) are combined together to convert it into a binary classification problem. The original iris dataset looks like this:






  





As we can see the two classes 1 and 2 are linearly inseparable and we combine them together for this tutorial. After we combine the classes the new graph would look like this:






  





Also, note that only 2 (sepal width, petal width) out of the 4 (sepal width, petal width, sepal length, petal length) features have been selected for classification.



## Classification Model in tf.js



Classification is performed using this dataset in tensorflow.js and the application takes user input in the form of `Sepal Width` and `Petal Width` through the browser and the model classifies the examples into one of the two categories created above and displays them in different colors.



The network use consists of only one densely connected hidden layer with sigmoid activation. The loss function used is `binaryCrossentropy` with `SGD` optimizer with a learning rate of 0.01. Training is done for 150 epochs before the model finishes to make predictions.



## Graph



The library used to plot the graph in JS is [chart.js](https://www.chartjs.org/). Though I have no experience in JavaScript, this library was intuitive and simple to use. Visit the [documentation](https://www.chartjs.org/docs/latest/) of the website to see what all you can do with __chart.js__.



## Dependencies



- tensorflowjs - 0.6.7

- chart.js



## How to use this repo



- For now, I haven't created a web server to host this application and therefore, the best and simplest way would be to clone this repository and then running `tfjs.html` in your browser.

- Make sure to let the model run first before trying to make predictions (it runs every time you refresh the page and takes around 5-10 seconds as tensorflow.js is slower than the python tensorflow).

- You can check if the model finished running by opening the inspect element window in your browser using the `f12` key or through settings.

- When the model finishes training, it will print `Model finished training!` to the console.

- Play all you want after this point!



Don't forget to leave a star if you liked the project!



#### Contributing



This is my first JavaScript encounter and this application may be a total turndown for JS developers out there. I will improve the project and do some more awesome ones as I improve my JS skills. Raise an issue if you would like to collaborate or contribute or have awesome ideas to work on using TFJS!



#### Resources



- [TensorFlow.js tutorials](https://js.tensorflow.org/tutorials/how-to-get-started.html)

- [TensorFlow.js getting started](https://js.tensorflow.org/#getting-started)

- [Pythonprogramming.net tutorial](https://pythonprogramming.net/deep-learning-browser-introduction-tensorflowjs/)

- [Chart.js documentation](https://www.chartjs.org/docs/latest/)

- A lot of stackoverflow on [tensorflow.js](https://stackoverflow.com/search?q=tensorflowjs) and [chart.js](https://stackoverflow.com/search?q=chartjs)