err.backward() optimizer.step()
时间: 2024-06-03 14:08:01 浏览: 22
These two lines of code are commonly used together in PyTorch for training neural networks.
err.backward() calculates the gradients of the loss function with respect to each parameter in the neural network. This is done using automatic differentiation, which is a key feature of PyTorch.
optimizer.step() updates the weights of the neural network using the gradients calculated in the previous step. This step is essential for the training process, as it ensures that the network moves towards the minimum of the loss function.
In summary, err.backward() calculates gradients and optimizer.step() updates the weights of the neural network based on these gradients. This process is typically repeated multiple times during the training process to iteratively improve the performance of the network.
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