Linear Regression Tutorial with TensorFlow Core API

Linear regression is a fundamental machine learning algorithm that is used to predict a continuous value based on one or more input features. In this tutorial, we will be using TensorFlow’s Core API to build a simple linear regression model.

Step 1: Import TensorFlow

Before we start building our linear regression model, we need to import the TensorFlow library. You can do this by adding the following code snippet to your Python script:

<script src="https://cdnjs.cloudflare.com/ajax/libs/tensorflow/2.8.0/tf.min.js"></script>

Step 2: Generate Data

Next, we need to generate some sample data for our model. For this tutorial, we will generate a simple dataset with one input feature and one output feature. You can use the following code snippet to create the input and output arrays:

<script>
        const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
        const ys = tf.tensor2d([2, 4, 6, 8], [4, 1]);
    </script>

Step 3: Build the Model

Now that we have our data, we can build our linear regression model using TensorFlow’s Core API. We will create a simple model with one dense layer that takes the input feature and outputs the predicted value. You can use the following code snippet to build the model:

<script>
        const model = tf.sequential();
        model.add(tf.layers.dense({units: 1, inputShape: [1]}));
    </script>

Step 4: Compile the Model

Next, we need to compile our model by specifying the loss function and optimizer. For linear regression, we can use Mean Squared Error as the loss function and Stochastic Gradient Descent as the optimizer. You can compile the model using the following code snippet:

<script>
        model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
    </script>

Step 5: Train the Model

Finally, we can train our model using the generated data. We will fit the model to the input and output arrays for a specified number of epochs. You can train the model using the following code snippet:

<script>
        model.fit(xs, ys, {epochs: 100}).then(() => {
            // Model training complete
        });
    </script>

Conclusion

Congratulations! You have successfully built and trained a simple linear regression model using TensorFlow’s Core API. Linear regression is a powerful tool for predicting continuous values and can be used in a wide range of machine learning applications.