What is the method for using the Torch framework?

The Torch framework is a deep learning framework used for building and training neural network models. Here are the basic usage methods of the Torch framework:

1. Install Torch: Use the pip command or conda command to install the Torch framework.
2. Importing necessary libraries: Import torch and other required libraries in your Python code.
3. Build model: Construct neural network model using classes from the torch.nn module. Optionally define the network’s layers and activation functions.
4. Define the loss function: Define the model’s loss function using the loss function class from the torch.nn module, such as cross-entropy loss function.
5. Define the optimizer: Define the model’s optimizer using the optimizer class in the torch.optim module, such as the stochastic gradient descent optimizer.
6. Model training: Train the model using a training dataset by feeding the input data to the model, calculating the loss between the model’s output and the actual labels, and then updating the model’s parameters using an optimizer.
7. Make predictions: Use a trained model to predict new data. Pass the input data to the model and then retrieve the model’s output result.
8. Saving and loading models: use the torch.save() function to save the trained model, and use the torch.load() function to load the saved model.

The above is the basic usage of the Torch framework, which can be customized and extended according to specific needs and tasks.