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Implementing CNN In PyTorch (Testing On MNIST — 99.26% Test Accuracy)
- Importing necessary Python libraries like numpy, torch, and torchvision for implementing CNN on the MNIST dataset in PyTorch.
- Using PyTorch's MNIST class to load training and test images efficiently, with automatic downloading and organization.
- Understanding the tuple structure of data samples in PyTorch's MNIST dataset and the importance of image to tensor transformation.
- Visualizing random samples from the training set to gain insights into the handwriting styles and variation in digits.
- Preprocessing images by transforming them into tensors, essential for neural network processing.
- Creating a validation split and restructuring data for efficient training using PyTorch functions.
- Building a CNN architecture using PyTorch's Sequential container, stacking layers like convolution, batch normalization, and activation functions.
- Verifying the model architecture and setting up device-agnostic code to enable GPU acceleration if available.
- Selecting loss function, optimizer, and performance metrics, optimizing data handling for efficient training.
- Training the model with key phases like forward pass, backward pass, and validation, tracking metrics, and achieving near-perfect accuracy.
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