PyTorch's dynamic computational graph, also known as Define-by-Run, is one of its most distinguishing and powerful features. In contrast to static computational graphs used by earlier versions of frameworks like TensorFlow 1.x, PyTorch's approach offers several advantages that make it particularly well-suited for research and development.
PyTorch's dynamic computational graph, also known as Define-by-Run, is one of its most distinguishing and powerful features. In contrast to static computational graphs used by earlier versions of frameworks like TensorFlow 1.x, PyTorch's approach offers several advantages that make it particularly well-suited for research and development.