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)

# network 4: use set_gradient_clip and minimize(grad_clip=clip) together
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")
    clip1 = fluid.clip.GradientClipByValue(min=-1.0, max=1.0)
    clip2 = fluid.clip.GradientClipByNorm(clip_norm=1.0)
    # 设置梯度裁剪策略：clip1
    fluid.clip.set_gradient_clip(clip1)

    # 设置梯度裁剪策略：clip2
    sgd = fluid.optimizer.SGD(learning_rate=1e-3, grad_clip=clip2)
    sgd.minimize(loss)
    # 有设置冲突时，set_gradient_clip将不会生效，将以clip2的策略进行梯度裁剪