[xdoctest] reformat example code with google style in No. 299

python/paddle/incubate/optimizer/lbfgs.py

@@ -76,45 +76,43 @@ class LBFGS(Optimizer):
  Examples:
  .. code-block:: python
 
- import paddle
- import numpy as np
- from paddle.incubate.optimizer import LBFGS
-
- 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 = 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
+ >>> from paddle.incubate.optimizer import LBFGS
+
+ >>> 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 = 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)
 
  """