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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

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