no_grad

class paddle. no_grad

创建一个上下文来禁用动态图梯度计算。在此模式下，每次计算的结果都将具有stop_gradient=True。

也可以用作一个装饰器（需要创建实例对象作为装饰器）。

代码示例

import numpy as np
import paddle
# 用作生成器
data = np.array([[2, 3], [4, 5]]).astype('float32')
l0 = paddle.nn.Linear(2, 2)  # l0.weight.gradient() is None
l1 = paddle.nn.Linear(2, 2)
with paddle.no_grad():
    # l1.weight.stop_gradient is False
    tmp = l1.weight * 2  # tmp.stop_gradient is True
x = paddle.to_tensor(data)
y = l0(x) + tmp
o = l1(y)
o.backward()
print(tmp.gradient() is None)  # True
print(l0.weight.gradient() is None)  # False
# 用作装饰器
@paddle.no_grad()
def test_layer():
    inp = np.ones([3, 1024], dtype='float32')
    t = paddle.to_tensor(inp)
    linear1 = paddle.nn.Linear(1024, 4, bias_attr=False)
    linear2 = paddle.nn.Linear(4, 4)
    ret = linear1(t)
    dy_ret = linear2(ret)
test_layer()