What are the functions of the Torch framework?
The Torch framework is an open-source machine learning library specifically designed for building deep learning models. It offers a wide range of features, including:
- Tensor operations: Torch offers powerful tensor manipulation capabilities similar to NumPy, allowing for matrix operations, indexing, and slicing.
- Automatic differentiation: Torch can automatically calculate the derivatives of tensors, which is very useful when training neural networks.
- Torch offers a variety of deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN).
- Training models: Torch offers features for model training, including optimizers (like stochastic gradient descent and Adam optimizer), loss functions (such as cross-entropy and mean squared error), and learning rate schedulers.
- Model evaluation: Torch offers various evaluation metrics such as accuracy, precision, recall, and F1 score to assess the performance of the model.
- Data Loader: Torch offers tools for loading and preprocessing data, making it easy to handle common data formats such as images, text, and audio.
- Distributed training: Torch supports distributed training, enabling parallel training on multiple GPUs or machines.
- Model deployment: Torch provides the functionality to deploy trained models into production environments, allowing the models to be converted into C/C++ code or exported using the ONNX format.
In conclusion, the Torch framework offers a wide range of features and tools that make it convenient for users to build, train, and deploy deep learning models.
