mindspore.ops.derivative

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mindspore.ops.derivative(fn, primals, order)[source]

This function is designed to calculate the higher order differentiation of given composite function. To figure out order-th order differentiations, original inputs and order must be provided together. In particular, the value of input first order derivative is set to 1, while the other to 0.

Note

If primals is tensor of int type, it will be converted to tensor of float type.

Parameters

  • fn (Union[Cell, function]) – Function to do TaylorOperation.

  • primals (Union[Tensor, tuple[Tensor]]) – The inputs to fn.

  • order (int) – The order of differentiation.

Returns

Tuple(out_primals, out_series)

  • out_primals (Union[Tensor, list[Tensor]]) - The output of fn(primals).

  • out_series (Union[Tensor, list[Tensor]]) - The order-th order of derivative of output with respect to the inputs.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import nn
>>> mindspore.set_context(mode=mindspore.GRAPH_MODE)
>>> class Net(nn.Cell):
...     def __init__(self):
...         super().__init__()
...         self.sin = mindspore.ops.Sin()
...         self.exp = mindspore.ops.Exp()
...     def construct(self, x):
...         out1 = self.sin(x)
...         out2 = self.exp(out1)
...         return out2
>>>
>>> primals = mindspore.tensor([[1, 2], [3, 4]], mindspore.float32)
>>> order = 3
>>> net = Net()
>>> out_primals, out_series = mindspore.ops.derivative(net, primals, order)
>>> print(out_primals, out_series)
[[2.319777  2.4825778]
 [1.1515628 0.4691642]] [[-4.0515366   3.6724353 ]
 [ 0.5053504  -0.52061415]]