grad¶

paddle.incubate.autograd. grad ( outputs, inputs, grad_outputs=None ) [source]

Reverse mode of automatic differentiation.

Note

ONLY available in the static graph mode and primitive operators

Parameters

• outputs (Tensor|Sequence[Tensor]) – The output Tensor or Tensors.

• inputs (Tensor|Sequence[Tensor]) – The input Tensor or Tensors.

• grad_outputs (Tensor|Sequence[Tensor]) – Optional, the gradient Tensor or Tensors of outputs which has the same shape with outputs, Defaults to None, in this case is equivalent to all ones.

Returns

The gradients for inputs.

Return type

grad_inputs(Tensor|Tensors)

Examples

import numpy as np
import paddle

paddle.enable_static()
paddle.incubate.autograd.enable_prim()

startup_program = paddle.static.Program()
main_program = paddle.static.Program()
with paddle.static.program_guard(main_program, startup_program):
    x = paddle.static.data('x', shape=[1], dtype='float32')
    x.stop_gradients = False
    y = x * x
    x_grad = paddle.incubate.autograd.grad(y, x)
    paddle.incubate.autograd.prim2orig()

exe = paddle.static.Executor()
exe.run(startup_program)
x_grad = exe.run(main_program, feed={'x': np.array([2.]).astype('float32')}, fetch_list=[x_grad])
print(x_grad)
# [array([4.], dtype=float32)]

paddle.incubate.autograd.disable_prim()
paddle.disable_static()