How to implement Linear Classification model in TensorFlow?

The two most common supervised learning tasks are linear regression and linear classifier. Linear regression predicts a value while the linear classifier predicts a class. This tutorial is focused on Linear Classifier.

Technically, in a linear model we will use the simplest function to predict the label $\mathbf{y_i}$ of the image $\mathbf{x_i}$. We’ll do so by using a linear mapping like $f(\mathbf{x_i}, \mathbf{W}, \mathbf{b})=\mathbf{W}\mathbf{x_i}+\mathbf{b}$ where $\mathbf{W}$ and $\mathbf{b}$ are called weight matrix and bias vector respectively.

Steps to implement Linear Classification in TensorFlow:

• Import required Libraries
• Load the Data
• Specifying the Data Dimensions
• Randomize the Data
• Load the Data and Display the sizes
• Hyper Parameter Tuning
• TensorFlow Variables of proper size and initialization for generating the weight and bias variables of the desired shape.
• Place Holders for input and corresponding labels
• Create the model structure
• Defining the Loss Function, Optimizer, accuracy and Predicted Class
• Initialize the all variables
• Train the Data
• Test the Data
• Evaluate the model
• Visualize the results