Basic Image Classification with TensorFlow

Kaggle link: https://www.kaggle.com/code/rasikasrimal/handwrittendigitidentifymodel

Task 1: Introduction

Welcome to Basic Image Classification with TensorFlow.

This graph describes the problem that we are trying to solve visually. We want to create and train a model that takes an image of a handwritten digit as input and predicts the class of that digit, i.e., it predicts the digit or the class of the input image.

Task 2: The Dataset

We will use the MNIST dataset, which consists of handwritten digit images.

• Import MNIST: We import the dataset and split it into training and test sets.
• Shapes of Imported Arrays: We check the shapes of the training and test sets.
• Plot an Image Example: We plot an example image from the training set.
• Display Labels: We display the labels of the training set.

Task 3: One Hot Encoding

We convert the labels into a one-hot encoded format.

Original Label	One-Hot Encoded Label
5	[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
7	[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
1	[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]

Task 4: Neural Networks

Linear Equations

A linear equation represents the relationship between inputs and outputs.

$$ y = w_1 \cdot x_1 + w_2 \cdot x_2 + w_3 \cdot x_3 + b $$

This equation can be vectorized as:

$$ y = W \cdot X + b $$

Neural Networks

Neural networks consist of multiple layers of neurons, which allow them to learn complex functions.

Task 5: Preprocessing the Examples

Unrolling N-dimensional Arrays to Vectors

We reshape the training and test images into vectors.

Data Normalization

We normalize the data to have a mean of 0 and a standard deviation of 1.

Task 6: Creating a Model

Creating the Model

We create a neural network model with two hidden layers.

Activation Functions

The activation function introduces non-linearity into the model:

$$ A = f(Z) $$

Compiling the Model

We compile the model with a specific optimizer and loss function.

Task 7: Training the Model

Training the Model

We train the model on the normalized training data.

Evaluating the Model

We evaluate the model on the normalized test data and print the accuracy.

Task 8: Predictions

Predictions on Test Set

We make predictions on the test set and plot the results.