mindspore.get_grad

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mindspore.get_grad(gradients, identifier)[source]

When return_ids of mindspore.grad() is set to True, use its return value as gradients. Then find the specific gradient from gradients according to identifier .

As for gradient, two typical cases are included:

  1. identifier is the position of the specific tensor to get gradient.

  2. identifier is a parameter of a network.

Parameters
Returns

The gradient of the tensor on the position or in the parameter that specified by the identifier.

Raises
  • RuntimeError – If gradient is not found.

  • TypeError – If type of Args does not belong to required ones.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> from mindspore import grad, get_grad
>>>
>>>  # Cell object to be differentiated
>>> class Net(nn.Cell):
...     def construct(self, x, y, z):
...         return x * y * z
>>> x = Tensor([1, 2], mindspore.float32)
>>> y = Tensor([-2, 3], mindspore.float32)
>>> z = Tensor([0, 3], mindspore.float32)
>>> net = Net()
>>> out_grad = grad(net, grad_position=(1, 2), return_ids=True)(x, y, z)
>>> output = get_grad(out_grad, 1)
>>> print(output)
[0. 6.]