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未验证 提交 9dfd5d60 编写于 作者: C Candy2Tang 提交者： GitHub
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[xdoctest][task 108] Reformat example code with google style in... 

[xdoctest][task 108] Reformat example code with google style in python/paddle/optimizer/lbfgs.py (#56224)

* [xdoctest][task 108] test=docs_preview

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---------
Co-authored-by: NNyakku Shigure <sigure.qaq@gmail.com>
上级 61dd9844
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python/paddle/optimizer/lbfgs.py
浏览文件 @ 9dfd5d60
......@@ -25,7 +25,7 @@ __all__ = []
def _cubic_interpolate(x1, f1, g1, x2, f2, g2, bounds=None):
r"""Cubic interpolation between (x1, f1, g1) and (x2, f2, g2).
Use two points and their gradient to determine a cubic function and get the minimun point
Use two points and their gradient to determine a cubic function and get the minimum point
between them in the cubic curve.
Reference:
......@@ -38,7 +38,7 @@ def _cubic_interpolate(x1, f1, g1, x2, f2, g2, bounds=None):
bounds: bounds of interpolation area
Returns:
min_pos: the minimun point between the specified points in the cubic curve.
min_pos: the minimum point between the specified points in the cubic curve.
"""
# Compute bounds of interpolation area
if bounds is not None:
......@@ -338,14 +338,14 @@ class LBFGS(Optimizer):
parameters (list|tuple, optional): List/Tuple of ``Tensor`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. The default value is None.
weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \
It canbe a float value as coeff of L2 regularization or \
It can be a float value as coeff of L2 regularization or \
:ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`.
If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \
the regularization setting here in optimizer will be ignored for this parameter. \
Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of \
some derived class of ``GradientClipBase`` . There are three cliping strategies \
grad_clip (GradientClipBase, optional): Gradient clipping strategy, it's an instance of \
some derived class of ``GradientClipBase`` . There are three clipping strategies \
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` , \
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): Normally there is no need for user to set this property.
......@@ -358,45 +358,43 @@ class LBFGS(Optimizer):
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
np.random.seed(0)
np_w = np.random.rand(1).astype(np.float32)
np_x = np.random.rand(1).astype(np.float32)
inputs = [np.random.rand(1).astype(np.float32) for i in range(10)]
# y = 2x
targets = [2 * x for x in inputs]
class Net(paddle.nn.Layer):
def __init__(self):
super().__init__()
w = paddle.to_tensor(np_w)
self.w = paddle.create_parameter(shape=w.shape, dtype=w.dtype, default_initializer=paddle.nn.initializer.Assign(w))
def forward(self, x):
return self.w * x
net = Net()
opt = paddle.optimizer.LBFGS(learning_rate=1, max_iter=1, max_eval=None, tolerance_grad=1e-07, tolerance_change=1e-09, history_size=100, line_search_fn='strong_wolfe', parameters=net.parameters())
def train_step(inputs, targets):
def closure():
outputs = net(inputs)
loss = paddle.nn.functional.mse_loss(outputs, targets)
print('loss: ', loss.item())
opt.clear_grad()
loss.backward()
return loss
opt.step(closure)
for input, target in zip(inputs, targets):
input = paddle.to_tensor(input)
target = paddle.to_tensor(target)
train_step(input, target)
>>> import paddle
>>> import numpy as np
>>> paddle.disable_static()
>>> np.random.seed(0)
>>> np_w = np.random.rand(1).astype(np.float32)
>>> np_x = np.random.rand(1).astype(np.float32)
>>> inputs = [np.random.rand(1).astype(np.float32) for i in range(10)]
>>> # y = 2x
>>> targets = [2 * x for x in inputs]
>>> class Net(paddle.nn.Layer):
... def __init__(self):
... super().__init__()
... w = paddle.to_tensor(np_w)
... self.w = paddle.create_parameter(shape=w.shape, dtype=w.dtype, default_initializer=paddle.nn.initializer.Assign(w))
...
... def forward(self, x):
... return self.w * x
...
>>> net = Net()
>>> opt = paddle.optimizer.LBFGS(learning_rate=1, max_iter=1, max_eval=None, tolerance_grad=1e-07, tolerance_change=1e-09, history_size=100, line_search_fn='strong_wolfe', parameters=net.parameters())
>>> def train_step(inputs, targets):
... def closure():
... outputs = net(inputs)
... loss = paddle.nn.functional.mse_loss(outputs, targets)
... print('loss: ', loss.item())
... opt.clear_grad()
... loss.backward()
... return loss
... opt.step(closure)
...
>>> for input, target in zip(inputs, targets):
... input = paddle.to_tensor(input)
... target = paddle.to_tensor(target)
... train_step(input, target)
"""
def __init__(
......@@ -458,41 +456,41 @@ class LBFGS(Optimizer):
Examples:
.. code-block:: python
import paddle
paddle.disable_static()
net = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.LBFGS(
learning_rate=1,
max_iter=1,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=100,
line_search_fn='strong_wolfe',
parameters=net.parameters(),
)
def train_step(inputs, targets):
def closure():
outputs = net(inputs)
loss = paddle.nn.functional.mse_loss(outputs, targets)
opt.clear_grad()
loss.backward()
return loss
opt.step(closure)
inputs = paddle.rand([10, 10], dtype="float32")
targets = paddle.to_tensor([2 * x for x in inputs])
n_iter = 0
while n_iter < 20:
loss = train_step(inputs, targets)
n_iter = opt.state_dict()["state"]["func_evals"]
print("n_iter:", n_iter)
>>> import paddle
>>> paddle.disable_static()
>>> net = paddle.nn.Linear(10, 10)
>>> opt = paddle.optimizer.LBFGS(
... learning_rate=1,
... max_iter=1,
... max_eval=None,
... tolerance_grad=1e-07,
... tolerance_change=1e-09,
... history_size=100,
... line_search_fn='strong_wolfe',
... parameters=net.parameters(),
>>> )
>>> def train_step(inputs, targets):
... def closure():
... outputs = net(inputs)
... loss = paddle.nn.functional.mse_loss(outputs, targets)
... opt.clear_grad()
... loss.backward()
... return loss
...
... opt.step(closure)
...
>>> inputs = paddle.rand([10, 10], dtype="float32")
>>> targets = paddle.to_tensor([2 * x for x in inputs])
>>> n_iter = 0
>>> while n_iter < 20:
... loss = train_step(inputs, targets)
... n_iter = opt.state_dict()["state"]["func_evals"]
... print("n_iter:", n_iter)
...
"""
packed_state = {}
......@@ -559,34 +557,34 @@ class LBFGS(Optimizer):
Examples:
.. code-block:: python
import paddle
paddle.disable_static()
inputs = paddle.rand([10, 10], dtype="float32")
targets = paddle.to_tensor([2 * x for x in inputs])
net = paddle.nn.Linear(10, 10)
opt = paddle.optimizer.LBFGS(
learning_rate=1,
max_iter=1,
max_eval=None,
tolerance_grad=1e-07,
tolerance_change=1e-09,
history_size=100,
line_search_fn='strong_wolfe',
parameters=net.parameters(),
)
def closure():
outputs = net(inputs)
loss = paddle.nn.functional.mse_loss(outputs, targets)
print("loss:", loss.item())
opt.clear_grad()
loss.backward()
return loss
opt.step(closure)
>>> import paddle
>>> paddle.disable_static()
>>> inputs = paddle.rand([10, 10], dtype="float32")
>>> targets = paddle.to_tensor([2 * x for x in inputs])
>>> net = paddle.nn.Linear(10, 10)
>>> opt = paddle.optimizer.LBFGS(
... learning_rate=1,
... max_iter=1,
... max_eval=None,
... tolerance_grad=1e-07,
... tolerance_change=1e-09,
... history_size=100,
... line_search_fn='strong_wolfe',
... parameters=net.parameters(),
>>> )
>>> def closure():
... outputs = net(inputs)
... loss = paddle.nn.functional.mse_loss(outputs, targets)
... print("loss:", loss.item())
... opt.clear_grad()
... loss.backward()
... return loss
...
>>> opt.step(closure)
"""
with paddle.no_grad():
......
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