Convolutional Neural Network, CIFAR-10

In this notebook, I will train a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 database. The CIFAR-10 dataset consists of small color images grouped into ten classes, including objects like airplanes, automobiles, birds, cats, and more.

Project Overview

The process will be broken down into the following steps:

1. Load Libraries

• Importing essential libraries including PyTorch, NumPy, and Matplotlib for data manipulation, machine learning tasks, and visualization.

2. Load and Visualize the Data

• Utilizing PyTorch to download and preprocess the CIFAR-10 dataset.
• Splitting the dataset into training and testing sets, with a portion allocated for validation.
• Creating data loaders for efficient loading of batches during training, validation, and testing.

3. Visualize a Batch of Training Data

• Visualizing a batch of training images to gain insights into the nature of the data.
• Displaying 20 images at a time, each labeled with its respective class.

4. View an Image in More Detail

• Selecting and visualizing a single image in detail by extracting the RGB channels.
• Creating subplots for each channel and annotating pixel values to understand their contribution to the overall color.

5. Define the Network Architecture

• Defining a CNN architecture named "Net" for image classification.
• The architecture includes convolutional layers, max-pooling layers, fully connected layers, and dropout layers for regularization.

6. Specify Loss Function and Optimizer

• Loading necessary libraries and specifying categorical cross-entropy as the loss function.
• Checking for GPU availability and moving the model to GPU if possible.
• Choosing stochastic gradient descent (SGD) as the optimizer with a learning rate of 0.01.

7. Train the Network

• Training the CNN on the CIFAR-10 dataset for 35 epochs.
• Monitoring and printing training and validation losses after each epoch.
• Saving the model state if the validation loss decreases, ensuring the best-performing model is retained.

8. Load the Model with the Lowest Validation Loss

• Loading the model state from the file 'model_cifar.pt,' representing the model with the lowest validation loss.

9. Test the Trained Network

• Evaluating the performance of the trained CNN on the test set.
• Calculating and printing the average test loss, test accuracy for each class, and overall test accuracy.

10. Visualize Sample Test Results

• Visualizing a batch of test images along with their predicted and true labels.
• Displaying images in a grid with color-coded titles (green for correct predictions, red for incorrect), providing a quick overview of the model's performance.