imperative programming (dynamic graph)
Create a context which disables dygraph gradient calculation. In this mode, the result of every computation will have stop_gradient=True.
Also functions as a decorator. (Make sure to instantiate without parenthesis.)
import numpy as np import paddle.fluid as fluid # use as generator data = np.array([[2, 3], [4, 5]]).astype('float32') with fluid.dygraph.guard(): l0 = fluid.Linear(2, 2) # l0.weight.gradient() is None l1 = fluid.Linear(2, 2) with fluid.dygraph.no_grad(): # l1.weight.stop_gradient is False tmp = l1.weight * 2 # tmp.stop_gradient is True x = fluid.dygraph.to_variable(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 @fluid.dygraph.no_grad def test_layer(): with fluid.dygraph.guard(): inp = np.ones([3, 1024], dtype='float32') t = fluid.dygraph.base.to_variable(inp) linear1 = fluid.Linear(1024, 4, bias_attr=False) linear2 = fluid.Linear(4, 4) ret = linear1(t) dy_ret = linear2(ret) test_layer()