本文整理汇总了Python中chainer.optimizer.GradientMethod方法的典型用法代码示例。如果您正苦于以下问题：Python optimizer.GradientMethod方法的具体用法？Python optimizer.GradientMethod怎么用？Python optimizer.GradientMethod使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类chainer.optimizer的用法示例。
在下文中一共展示了optimizer.GradientMethod方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_cleargrad
# 需要导入模块: from chainer import optimizer [as 别名]
# 或者: from chainer.optimizer import GradientMethod [as 别名]
def test_cleargrad(self, backend_config):
class CleargradHook(object):
name = 'Cleargrad'
timing = 'pre'
def __init__(self, _):
pass
def __call__(self, opt):
for param in opt.target.params():
# Clear all grads
param.cleargrad()
target = self.target
target.to_device(backend_config.device)
# TODO(niboshi): Do not use SGD in GradientMethod test
opt = optimizers.SGD(lr=1)
opt.setup(target)
opt.add_hook(CleargradHook(self))
opt.add_hook(DummyHook(self))
opt.update() 示例2: setUp
# 需要导入模块: from chainer import optimizer [as 别名]
# 或者: from chainer.optimizer import GradientMethod [as 别名]
def setUp(self):
self.optimizer = chainer.GradientMethod()
self.target = chainer.ChainList(
SimpleLink(np.arange(3).astype(np.float32),
np.arange(3).astype(np.float32)),
SimpleLink(np.arange(3).astype(np.float32),
np.arange(3).astype(np.float32)))
self.optimizer.create_update_rule = mock.MagicMock 示例3: test_upate
# 需要导入模块: from chainer import optimizer [as 别名]
# 或者: from chainer.optimizer import GradientMethod [as 别名]
def test_upate(self):
initial_p = np.array([1., 2., 3.], np.float32)
x = chainerx.array([2., 4., 6.], np.float32)
expected_p = 4. * initial_p - 6. * backend.CpuDevice().send(x)
class ChainerxUpdateRule(optimizer.UpdateRule):
call_count = 0
def update_core_chainerx(self, param):
# p <= 3 * p - 2 * (dy/dp)
array = param.array
t1 = param.array.as_grad_stopped() * 3.
t2 = param.grad.as_grad_stopped() * 2.
delta = t1 - t2
array += delta
self.call_count += 1
class ChainerxOptimizer(optimizer.GradientMethod):
def create_update_rule(self):
return ChainerxUpdateRule(self.hyperparam)
class Link(chainer.Link):
def __init__(self):
super(Link, self).__init__()
with self.init_scope():
self.p = chainer.Parameter(initial_p)
def forward(self, x):
return 3. * x * self.p
link = Link()
link.to_device('native:0')
y = link(x)
y.backward()
optimizer_ = ChainerxOptimizer()
optimizer_.setup(link)
optimizer_.update()
assert link.p.update_rule.call_count == 1
np.testing.assert_array_equal(
backend.CpuDevice().send(link.p.array), expected_p) 示例4: create
# 需要导入模块: from chainer import optimizer [as 别名]
# 或者: from chainer.optimizer import GradientMethod [as 别名]
def create(self, device):
class MyLink(chainer.Link):
def __init__(self):
super(MyLink, self).__init__()
with self.init_scope():
self.p1 = chainer.Parameter() # uninitialized
self.p2 = chainer.Parameter( # initialized, with grad
np.array([3, 2], np.float32))
self.p2.grad = np.array([13, 12], np.float32)
self.p3 = chainer.Parameter( # initialized, without grad
np.array([5, 7], np.float32))
call_record = []
override_pattern = self.override_pattern
class MyUpdateRule(optimizer.UpdateRule):
if override_pattern == 'generic':
def update_core(self, param):
call_record.append(('update_core', param))
elif override_pattern == 'cpu_gpu':
def update_core_cpu(self, param):
call_record.append(('update_core_cpu', param))
def update_core_gpu(self, param):
call_record.append(('update_core_gpu', param))
elif override_pattern == 'cpu_gpu_chx':
def update_core_cpu(self, param):
call_record.append(('update_core_cpu', param))
def update_core_gpu(self, param):
call_record.append(('update_core_gpu', param))
def update_core_chainerx(self, param):
call_record.append(('update_core_chainerx', param))
else:
assert False, override_pattern
class MyOptimizer(optimizer.GradientMethod):
def create_update_rule(self):
return MyUpdateRule()
optimizer_ = MyOptimizer()
target = MyLink()
target.to_device(device)
optimizer_.setup(target)
return optimizer_, call_record