Autograd
深度学习框架中自动计算梯度的机制，是反向传播算法的工程实现基础，PyTorch和TensorFlow均内置此功能。
Core Facts
PyTorch uses a dynamic computation graph (Define-by-Run), tracking tensor operations via a directed acyclic graph (DAG) to enable automatic differentiation.
When loss.backward() is called, PyTorch traverses the computation graph in reverse, using the chain rule to automatically calculate the gradient of each parameter.
Timeline (last 90 days)
Each time PyTorch performs an operation on a tensor, it dynamically builds a computation graph in the background, recording all operations and their dependencies.
Autograd automatically tracks all operations on tensors and automatically computes gradients during backpropagation.
All Facts (4)
PyTorch uses a dynamic computation graph (Define-by-Run), tracking tensor operations via a directed acyclic graph (DAG) to enable automatic differentiation.
65%VerifiedWhen loss.backward() is called, PyTorch traverses the computation graph in reverse, using the chain rule to automatically calculate the gradient of each parameter.
65%UnverifiedEach time PyTorch performs an operation on a tensor, it dynamically builds a computation graph in the background, recording all operations and their dependencies.
50%UnverifiedAutograd automatically tracks all operations on tensors and automatically computes gradients during backpropagation.
50%