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This article mainly explains how to use register_backward_hook (hook) and register_forward_hook (hook). The content in the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn how to use register_backward_hook (hook) and register_forward_hook (hook).

Register_backward_hook (hook) Registers a backward hook on the module. Register a backpropagated hook function with a module. The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature: this hook function is called every time the gradient of the model input is calculated. The hook function should have the following signature form: hook (module, grad_input, grad_output)-> Tensor or None The grad_input and grad_output may be tuples if the module has multiple inputs or outputs. The hook should not modify its arguments, but it can optionally return a new gradient with respect to input that will be used in place of grad_input in subsequent computations. If the module has multiple input and output data, then grad_input and grad_output may be a tuple. The hook function should not modify its parameters, but it can optionally return a new gradient relative to the input input, which can be used to replace grad_input in subsequent calculations. Returns returns a handle that can be used to remove the added hook by calling handle.remove () returns a handle that removes the added hook function by calling handle.remove (). Return type returns type torch.utils.hooks.RemovableHandleWarning warning The current implementation will not have the presented behavior for complex Module that perform many operations. In some failurecases, grad_input and grad_output will only contain the gradientsfor a subset of the inputs and outputs. For such Module, you should use torch.Tensor.register_hook () directly on a specific input or output to get the required gradients. The current implementation does not show the behavior of complex modules that perform many operations. In some error cases, grad_input and grad_output can only contain the gradient of a subset of input and output data. For such modules, you should directly use torch.Tensor.register_hook () on specific input and output data to get the desired gradients. Register _ forward_hook (hook) Registers a forward hook on the module. The hook will be called every time after forward () has computed an output. It should have the following signature: hook (module, input, output)-> None or modified output The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after forward () is called. Returns a handle that can be used to remove the added hook by calling handle.remove () Return type torch.utils.hooks.RemovableHandle Thank you for your reading. This is the content of "how to use register_backward_hook (hook) and register_forward_hook (hook)". After studying this article I believe you have a deeper understanding of how to use register_backward_hook (hook) and register_forward_hook (hook), and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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