mindspore.ops.grad

mindspore.ops.grad(fn, grad_position=0, sens_param=False)[source]

A wrapper function to generate the gradient function for the input function.

Parameters

fn (Union(Cell, function)) – Function to do GradOperation.
grad_position (Union(int, tuple[int])) – If int, get the gradient with respect to single input. If tuple, get the gradients with respect to selected inputs. ‘grad_position’ begins with 0. Default: 0.
sens_param (bool) – Whether to append sensitivity (gradient with respect to output) as input. If sens_param is False, a ‘ones_like(outputs)’ sensitivity will be attached automatically. Default: False.

Returns

Function, returns the gradient function for the input function or cell.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import numpy as np
>>> import mindspore.nn as nn
>>> import mindspore.context as context
>>> from mindspore import Tensor
>>> from mindspore.ops.functional import grad
>>> context.set_context(mode=context.GRAPH_MODE)
>>> class Net(nn.Cell):
...     def construct(self, x, y, z):
...         return x*y*z
>>> x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
>>> y = Tensor(np.array([[-2, 3], [-1, 2]]).astype(np.float32))
>>> z = Tensor(np.array([[0, 3], [5, -1]]).astype(np.float32))
>>> net = Net()
>>> output = grad(net, grad_position=(1, 2))(x, y, z)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 0.00000000e+00,  6.00000000e+00],
 [ 1.50000000e+01, -4.00000000e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[-2.00000000e+00,  6.00000000e+00],
 [-3.00000000e+00,  8.00000000e+00]]))