GradientClipByGlobalNorm
- class
paddle.fluid.clip.
GradientClipByGlobalNorm
(clip_norm, group_name='default_group')[源代码]
通过多个 Tensor 的范数之和的比率,来剪切(clip)多个 Tensor ( Tensor 不是从该类传入, 通过 fluid.program_guard
的 main_program
参数传入,即公式中的
见代码实例)。
给定一个 Tensor 列表
和一个剪切比率 clip_norm
,返回该类的实例作为 set_gradient_clip
方法的第一个参数, set_gradient_clip
第二个参数是用来计算被剪切的 Tensor 列表(该值默认为 None
会基于所有 Tensor 列表来计算全局范数 global_norm
。
剪切过程如下:
其中:
- 参数:
- clip_norm (float) - 范数最大值
- group_name (str, optional) - 剪切的组名
代码示例
- import paddle.fluid as fluid
- import paddle.fluid.core as core
- import paddle
- place = core.CPUPlace()
- prog = fluid.framework.Program()
- startup_program = fluid.framework.Program()
- with fluid.program_guard(
- main_program=prog, startup_program=startup_program):
- image = fluid.layers.data(name='x', shape=[784], dtype='float32')
- label = fluid.layers.data(name='y', shape=[1], dtype='int64')
- hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
- hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
- predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
- cost = fluid.layers.cross_entropy(input=predict, label=label)
- avg_cost = fluid.layers.mean(cost)
- prog_clip = prog.clone()
- avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
- p_g = fluid.backward.append_backward(loss=avg_cost)
- p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip)
- with fluid.program_guard(main_program=prog_clip, startup_program=startup_program):
- fluid.clip.set_gradient_clip(
- fluid.clip.GradientClipByGlobalNorm(clip_norm=2.0))
- p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip)
- grad_list = [elem[1] for elem in p_g]
- grad_clip_list = [elem[1] for elem in p_g_clip]
- train_reader = paddle.batch(
- paddle.reader.shuffle(
- paddle.dataset.mnist.train(), buf_size=8192),
- batch_size=128)
- exe = fluid.Executor(place)
- feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
- exe.run(startup_program)
- count = 0
- for data in train_reader():
- count += 1
- print("count:%s" % count)
- if count > 5:
- break
- out = exe.run(prog, feed=feeder.feed(data), fetch_list=grad_list)
- out_clip = exe.run(prog_clip,
- feed=feeder.feed(data),
- fetch_list=grad_clip_list)