set_gradient_clip
注意:该API仅支持【静态图】模式
paddle.fluid.clip.
set_gradient_clip
(clip, param_list=None, program=None)[源代码]
给指定参数做梯度裁剪。
- 参数:
- clip (BaseGradientClipAttr) - BaseGradientClipAttr子类的实例,如 GradientClipByGlobalNorm 等,用于描述具体的裁剪方法和属性。
- param_list (list(Variable),可选) - 需要裁剪的参数列表,可以是参数或参数名称列表。默认值为None,表示裁剪
program
中的所有参数。 - program (Program,可选) - 参数所在的Program。默认值为None,表示使用 default_main_program。
返回: 无。
代码示例
- import paddle.fluid as fluid
- def network():
- image = fluid.layers.data(name='image', shape=[28], dtype='float32')
- param_attr1 = fluid.ParamAttr("fc1_param")
- fc1 = fluid.layers.fc(image, size=10, param_attr=param_attr1)
- param_attr2 = fluid.ParamAttr("fc2_param")
- fc2 = fluid.layers.fc(fc1, size=10, param_attr=param_attr2)
- loss = fluid.layers.reduce_mean(fc2)
- return loss
- # network 1: clip all parameter gradient
- with fluid.program_guard(fluid.Program(), fluid.Program()):
- loss = network()
- fluid.clip.set_gradient_clip(
- fluid.clip.GradientClipByGlobalNorm(clip_norm=2.0))
- sgd = fluid.optimizer.SGD(learning_rate=1e-3)
- sgd.minimize(loss)
- # network 2: clip parameter gradient by name
- with fluid.program_guard(fluid.Program(), fluid.Program()):
- loss = network()
- fluid.clip.set_gradient_clip(
- fluid.clip.GradientClipByValue(min=-1.0, max=1.0),
- param_list=["fc1_param", "fc2_param"])
- sgd = fluid.optimizer.SGD(learning_rate=1e-3)
- sgd.minimize(loss)
- # network 3: clip parameter gradient by var
- with fluid.program_guard(fluid.Program(), fluid.Program()):
- loss = network()
- param_var1 = fluid.default_main_program().global_block().var("fc1_param")
- param_var2 = fluid.default_main_program().global_block().var("fc2_param")
- fluid.clip.set_gradient_clip(
- fluid.clip.GradientClipByValue(min=-1.0, max=1.0),
- param_list=[param_var1, param_var2])
- sgd = fluid.optimizer.SGD(learning_rate=1e-3)
- sgd.minimize(loss)