# Implementation Plan - CIFAR-10 CNN Classifier

This plan outlines the steps to build, train, and evaluate a Convolutional Neural Network (CNN) for the CIFAR-10 dataset.

## 1. Environment Setup
- Verify installation of `torch`, `torchvision`, `matplotlib`.
- Import necessary modules.

## 2. Data Preparation
- Load CIFAR-10 dataset using `torchvision.datasets`.
- Normalize and transform data to Tensors.
- Explore data shapes and visualize sample images.

## 3. Model Architecture
- Build a PyTorch `nn.Module` CNN:
  - Input layer: 32x32x3 images.
  - Multiple Convolutional blocks (Conv2d -> BatchNorm2d -> ReLU -> MaxPool2d -> Dropout).
  - Flatten layer.
  - Fully connected layers with BatchNorm and Dropout.
  - Output layer: 10 units.

## 4. Training Configuration
- Loss Function: `nn.CrossEntropyLoss()`.
- Optimizer: `optim.Adam`.
- Device: Use CUDA if available, else CPU.

## 5. Model Training
- Train the model on the training set.
- Validate on the test/validation set.
- Save the training history.

## 6. Evaluation and Visualization
- Evaluate the model on the test set.
- Plot Training vs. Validation Accuracy/Loss.
- Display a confusion matrix or classification report.
- Save the final model.

## 7. Inference Script (Optional)
- Create a script to load the model and predict labels for new images.