Linear Regression with TensorFlow Tutorial

Welcome to our tutorial on linear regression using TensorFlow. In this tutorial, we will walk you through the steps of building a simple linear regression model using TensorFlow.

What is Linear Regression?

Linear regression is a simple yet powerful technique used for predicting a continuous value given one or more input features. It is widely used in various fields such as finance, economics, and machine learning.

Getting Started with TensorFlow

If you haven’t already installed TensorFlow, you can do so by running the following command:


pip install tensorflow


Once you have TensorFlow installed, you can start building your linear regression model. Here is a simple example to get you started:

import tensorflow as tf

# Define the input data
X = [1, 2, 3, 4, 5]
Y = [2, 4, 6, 8, 10]

# Define the parameters of the model
W = tf.Variable(0.0)
b = tf.Variable(0.0)

# Define the linear regression model
def linear_regression(x):
    return W*x + b

# Define the loss function
def loss(y_pred, y_true):
    return tf.reduce_mean(tf.square(y_pred - y_true))

# Define the optimizer
optimizer = tf.optimizers.SGD(learning_rate=0.01)

# Training loop
epochs = 1000
for i in range(epochs):
    with tf.GradientTape() as tape:
        predictions = linear_regression(X)
        error = loss(predictions, Y)
    gradients = tape.gradient(error, [W, b])
    optimizer.apply_gradients(zip(gradients, [W, b]))

print("Model trained successfully!")
    

Conclusion

Congratulations! You have now successfully built a simple linear regression model using TensorFlow. Feel free to play around with different datasets and hyperparameters to improve the performance of your model.

Thank you for following along with our tutorial. We hope you found it helpful!