Machine Learning - III
This code implements a deep learning model for classifying images from the CIFAR-10 dataset using PyTorch. The model is a Convolutional Neural Network (CNN) with several convolutional layers followed by fully connected layers. It uses data augmentation for training, including random cropping, horizontal flipping, and normalization. The dataset is split into training and testing sets, and data loaders are used to manage mini-batches for both.
Key Components:
- Device Configuration: It automatically selects between GPU (CUDA) and CPU based on availability.
- Hyperparameters: The model is trained for 30 epochs with a batch size of 128 and a learning rate of 0.001.
- Data Preprocessing: Different transformations are applied for training and testing to improve the model's generalization.
- CNN Architecture: The model includes several convolutional layers with batch normalization and ReLU activations. It uses max-pooling to reduce spatial dimensions and ends with an adaptive average pooling layer.
- Training Loop: The model is trained using the Adam optimizer and CrossEntropyLoss for classification. A learning rate scheduler is used to adjust the learning rate every 10 epochs.
- Accuracy Calculation: A function computes the accuracy on the test set after each epoch.
- Model Saving: The model's state dictionary is saved to disk after training for later use or inference.
After training, the model's final accuracy is printed, and the model is saved for future use. This code provides a foundational example of building, training, and evaluating a CNN for image classification tasks.
- CUDA vs. CPU: The code adapts to different hardware setups. While training on a GPU (with CUDA support) is significantly faster, the script is also designed to fall back to CPU if needed.
- Model Complexity: The architecture includes a substantial number of parameters, making it capable of learning complex features from images. However, it also requires significant computation, especially on larger datasets or if trained for more epochs.
This code is an excellent starting point for building image classification models in PyTorch. It can be extended with additional features such as data augmentation, different CNN architectures, or other datasets, making it versatile for various deep learning projects.
