mindspore.ops.vjp
- mindspore.ops.vjp(fn, inputs, v)[source]
Compute the vector-jacobian-product of the given network. vjp matches reverse-mode differentiation.
Note
This function is subjected to change in the future.
- Parameters
fn (Union[Function, Cell]) – The function or net that takes Tensor inputs and returns single Tensor or tuple of Tensors.
inputs (Union[Tensor, tuple[Tensor], list[Tensor]]) – The inputs to fn .
v (Union[Tensor, tuple[Tensor], list[Tensor]]) – The vector in vector-jacobian-product. The shape and type of v should be the same as fn(inputs) .
- Returns
net_output (Union[Tensor, tuple[Tensor]]) - The result of fn(inputs) .
vjp (Union[Tensor, tuple[Tensor]]) - The result of vector-jacobian-product.
- Raises
TypeError – inputs or v does not belong to required types.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import ops >>> from mindspore import Tensor >>> class Net(nn.Cell): ... def construct(self, x, y): ... return x**3 + y >>> x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32)) >>> y = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32)) >>> v = Tensor(np.array([[1, 1], [1, 1]]).astype(np.float32)) >>> output = ops.vjp(Net(), (x, y), v) >>> print(output[0]) [[ 2. 10.] [30. 68.]] >>> print(output[1]) (Tensor(shape=[2, 2], dtype=Float32, value= [[ 3.00000000e+00, 1.20000000e+01], [ 2.70000000e+01, 4.80000000e+01]]), Tensor(shape=[2, 2], dtype=Float32, value= [[ 1.00000000e+00, 1.00000000e+00], [ 1.00000000e+00, 1.00000000e+00]]))