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
# use as generator
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
# use as decorator
@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()