qianyxxx / pytorch_learning

Project Title

This project is a collection of Python scripts that demonstrate various functionalities related to machine learning, specifically deep learning using PyTorch. The scripts cover a range of topics from data preprocessing, model creation, training, testing, and visualization of results.

Files Description

• nn_seq.py: This script defines a convolutional neural network model using PyTorch's Sequential API.

• read_data.py: This script reads image data from a directory and creates a PyTorch Dataset object.

• rename_dataset.py: This script renames images in a dataset and writes the labels to a text file.

• test.py: This script loads a pre-trained model and uses it to make predictions on a single image.

• test_tensorboard.py: This script demonstrates how to use TensorBoard for visualization of model training.

• train.py: This script trains a model on the CIFAR-10 dataset and saves the model after each epoch.

• train_gpu_1.py and train_gpu_2.py: These scripts are variations of train.py that demonstrate how to use a GPU for training if available.

• transforms.py and transformsV2.py: These scripts demonstrate how to use PyTorch's transforms for data augmentation.

• nn_relu.py: This script demonstrates the use of ReLU activation function in a neural network.

• nn_module.py: This script demonstrates the basic structure of a PyTorch Module.

• nn_maxpool.py: This script demonstrates the use of MaxPooling in a convolutional neural network.

• nn_optim.py: This script demonstrates how to use PyTorch's SGD optimizer.

• nn_loss_network.py: This script demonstrates how to calculate the loss of a network using CrossEntropyLoss.

• nn_linear.py: This script demonstrates the use of a Linear layer in a neural network.

• nn_loss.py: This script demonstrates how to calculate different types of loss functions in PyTorch.

• nn_conv2d.py: This script demonstrates the use of a Conv2D layer in a convolutional neural network.

• nn_conv.py: This script demonstrates how to perform a 2D convolution operation using PyTorch's functional API.

Requirements

• Python 3.6 or above
• PyTorch 1.0 or above
• torchvision
• PIL
• TensorBoard

Usage

Each script can be run independently. For example, to run nn_seq.py, use the following command:

python nn_seq.py

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT