TensorFlow.js Tutorial: A Neuron Implementation for Linear Regression

Welcome to this TensorFlow.js tutorial where we will be implementing a neuron for linear regression using TensorFlow.js. TensorFlow.js is an open-source library that allows you to build, train, and deploy machine learning models in the browser or on Node.js.

Linear Regression

Linear regression is a simple but powerful technique used for predicting a continuous value based on one or more input features. In this tutorial, we will be implementing a neuron for linear regression which will learn the relationship between the input features and the target value.

Implementing a Neuron for Linear Regression

First, we need to import TensorFlow.js into our HTML file. You can do this by adding the following script tag:

        
            <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
        
    

Next, let’s define our neuron for linear regression:

        
            const model = tf.sequential();
            model.add(tf.layers.dense({units: 1, inputShape: [1]}));
            model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
        
    

Now we can train our neuron using some sample data:

        
            const xs = tf.tensor2d([1, 2, 3, 4, 5], [5, 1]);
            const ys = tf.tensor2d([2, 4, 6, 8, 10], [5, 1]);
            
            model.fit(xs, ys, {epochs: 100}).then(() => {
                // Model training complete
            });
        
    

Conclusion

Congratulations! You have successfully implemented a neuron for linear regression using TensorFlow.js. This is just the beginning of your journey into machine learning with TensorFlow.js. Feel free to experiment with different parameters and datasets to further improve your model.