loss.backward()
时间: 2024-05-29 13:12:14 浏览: 17
The `loss.backward()` function is used in PyTorch to compute the gradients of the loss function with respect to the model parameters. This function is typically called after a forward pass through the model, during which the inputs are passed through the layers and the output is generated. The loss function is then computed based on the output and the target values.
The `loss.backward()` function then computes the gradients of the loss function with respect to each parameter in the model using the chain rule of calculus, and stores them in the `grad` attribute of each parameter tensor. These gradients can then be used to update the parameters of the model during the optimization process using an optimizer such as stochastic gradient descent (SGD).
In essence, `loss.backward()` is a critical step in training a neural network using backpropagation, as it enables the gradients to flow backward through the network to compute the gradients of the loss function with respect to the parameters of each layer, which can then be used to update the weights and biases of the network via the optimizer.
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