[xdoctest] reformat example code with google style in No.297、298、302 (P…

…addlePaddle#56861) * 更改相关文件 * Update ir.py * 恢复相关文件 * Update ir.py * Delete python/paddle/incubate/optimizer/modelaverage.py * Delete modelaverage.py * 尝试恢复文件 * Revert "尝试恢复文件" This reverts commit 8a263cf. * Revert "恢复相关文件" This reverts commit 24249b8. * Revert "Revert "尝试恢复文件"" This reverts commit 1b833d6. * Revert "Revert "Revert "尝试恢复文件""" This reverts commit 64b3a81. * Revert "Delete python/paddle/incubate/optimizer/modelaverage.py" This reverts commit 6198629. * Revert "更改相关文件" This reverts commit a5ba675. * Apply suggestions from code review --------- Co-authored-by: Nyakku Shigure <[email protected]>
BeingGod · Sep 9, 2023 · d71d8f9 · d71d8f9
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diff --git a/python/paddle/incubate/optimizer/functional/bfgs.py b/python/paddle/incubate/optimizer/functional/bfgs.py
@@ -81,46 +81,46 @@ def minimize_bfgs(
  .. code-block:: python
  :name: code-example1
 
- # Example1: 1D Grid Parameters
- import paddle
- # Randomly simulate a batch of input data
- inputs = paddle. normal(shape=(100, 1))
- labels = inputs * 2.0
- # define the loss function
- def loss(w):
- y = w * inputs
- return paddle.nn.functional.square_error_cost(y, labels).mean()
- # Initialize weight parameters
- w = paddle.normal(shape=(1,))
- # Call the bfgs method to solve the weight that makes the loss the smallest, and update the parameters
- for epoch in range(0, 10):
- # Call the bfgs method to optimize the loss, note that the third parameter returned represents the weight
- w_update = paddle.incubate.optimizer.functional.minimize_bfgs(loss, w)[2]
- # Use paddle.assign to update parameters in place
- paddle. assign(w_update, w)
+ >>> # Example1: 1D Grid Parameters
+ >>> import paddle
+ >>> # Randomly simulate a batch of input data
+ >>> inputs = paddle. normal(shape=(100, 1))
+ >>> labels = inputs * 2.0
+ >>> # define the loss function
+ >>> def loss(w):
+ ...  y = w * inputs
+ ...  return paddle.nn.functional.square_error_cost(y, labels).mean()
+ >>> # Initialize weight parameters
+ >>> w = paddle.normal(shape=(1,))
+ >>> # Call the bfgs method to solve the weight that makes the loss the smallest, and update the parameters
+ >>> for epoch in range(0, 10):
+ ...  # Call the bfgs method to optimize the loss, note that the third parameter returned represents the weight
+ ...  w_update = paddle.incubate.optimizer.functional.minimize_bfgs(loss, w)[2]
+ ...  # Use paddle.assign to update parameters in place
+ ...  paddle. assign(w_update, w)
 
  .. code-block:: python
  :name: code-example2
 
- # Example2: Multidimensional Grid Parameters
- import paddle
- def flatten(x):
- return x. flatten()
- def unflatten(x):
- return x.reshape((2,2))
- # Assume the network parameters are more than one dimension
- def net(x):
- assert len(x.shape) > 1
- return x.square().mean()
- # function to be optimized
- def bfgs_f(flatten_x):
- return net(unflatten(flatten_x))
- x = paddle.rand([2,2])
- for i in range(0, 10):
- # Flatten x before using minimize_bfgs
- x_update = paddle.incubate.optimizer.functional.minimize_bfgs(bfgs_f, flatten(x))[2]
- # unflatten x_update, then update parameters
- paddle. assign(unflatten(x_update), x)
+ >>> # Example2: Multidimensional Grid Parameters
+ >>> import paddle
+ >>> def flatten(x):
+ ...  return x. flatten()
+ >>> def unflatten(x):
+ ...  return x.reshape((2,2))
+ >>> # Assume the network parameters are more than one dimension
+ >>> def net(x):
+ ...  assert len(x.shape) > 1
+ ...  return x.square().mean()
+ >>> # function to be optimized
+ >>> def bfgs_f(flatten_x):
+ ...  return net(unflatten(flatten_x))
+ >>> x = paddle.rand([2,2])
+ >>> for i in range(0, 10):
+ ...  # Flatten x before using minimize_bfgs
+ ...  x_update = paddle.incubate.optimizer.functional.minimize_bfgs(bfgs_f, flatten(x))[2]
+ ...  # unflatten x_update, then update parameters
+ ...  paddle.assign(unflatten(x_update), x)
  """
 
  if dtype not in ['float32', 'float64']:

diff --git a/python/paddle/incubate/optimizer/functional/lbfgs.py b/python/paddle/incubate/optimizer/functional/lbfgs.py
@@ -82,46 +82,46 @@ def minimize_lbfgs(
  .. code-block:: python
  :name: code-example1
 
- # Example1: 1D Grid Parameters
- import paddle
- # Randomly simulate a batch of input data
- inputs = paddle. normal(shape=(100, 1))
- labels = inputs * 2.0
- # define the loss function
- def loss(w):
- y = w * inputs
- return paddle.nn.functional.square_error_cost(y, labels).mean()
- # Initialize weight parameters
- w = paddle.normal(shape=(1,))
- # Call the bfgs method to solve the weight that makes the loss the smallest, and update the parameters
- for epoch in range(0, 10):
- # Call the bfgs method to optimize the loss, note that the third parameter returned represents the weight
- w_update = paddle.incubate.optimizer.functional.minimize_bfgs(loss, w)[2]
- # Use paddle.assign to update parameters in place
- paddle. assign(w_update, w)
+ >>> # Example1: 1D Grid Parameters
+ >>> import paddle
+ >>> # Randomly simulate a batch of input data
+ >>> inputs = paddle. normal(shape=(100, 1))
+ >>> labels = inputs * 2.0
+ >>> # define the loss function
+ >>> def loss(w):
+ ...  y = w * inputs
+ ...  return paddle.nn.functional.square_error_cost(y, labels).mean()
+ >>> # Initialize weight parameters
+ >>> w = paddle.normal(shape=(1,))
+ >>> # Call the bfgs method to solve the weight that makes the loss the smallest, and update the parameters
+ >>> for epoch in range(0, 10):
+ ...  # Call the bfgs method to optimize the loss, note that the third parameter returned represents the weight
+ ...  w_update = paddle.incubate.optimizer.functional.minimize_bfgs(loss, w)[2]
+ ...  # Use paddle.assign to update parameters in place
+ ...  paddle.assign(w_update, w)
 
  .. code-block:: python
  :name: code-example2
 
- # Example2: Multidimensional Grid Parameters
- import paddle
- def flatten(x):
- return x. flatten()
- def unflatten(x):
- return x.reshape((2,2))
- # Assume the network parameters are more than one dimension
- def net(x):
- assert len(x.shape) > 1
- return x.square().mean()
- # function to be optimized
- def bfgs_f(flatten_x):
- return net(unflatten(flatten_x))
- x = paddle.rand([2,2])
- for i in range(0, 10):
- # Flatten x before using minimize_bfgs
- x_update = paddle.incubate.optimizer.functional.minimize_bfgs(bfgs_f, flatten(x))[2]
- # unflatten x_update, then update parameters
- paddle. assign(unflatten(x_update), x)
+ >>> # Example2: Multidimensional Grid Parameters
+ >>> import paddle
+ >>> def flatten(x):
+ ...  return x. flatten()
+ >>> def unflatten(x):
+ ...  return x.reshape((2,2))
+ >>> # Assume the network parameters are more than one dimension
+ >>> def net(x):
+ ...  assert len(x.shape) > 1
+ ...  return x.square().mean()
+ >>> # function to be optimized
+ >>> def bfgs_f(flatten_x):
+ ...  return net(unflatten(flatten_x))
+ >>> x = paddle.rand([2,2])
+ >>> for i in range(0, 10):
+ ...  # Flatten x before using minimize_bfgs
+ ...  x_update = paddle.incubate.optimizer.functional.minimize_bfgs(bfgs_f, flatten(x))[2]
+ ...  # unflatten x_update, then update parameters
+ ...  paddle.assign(unflatten(x_update), x)
 
  """
  if dtype not in ['float32', 'float64']:

diff --git a/python/paddle/incubate/passes/ir.py b/python/paddle/incubate/passes/ir.py
@@ -469,16 +469,16 @@ def RegisterPass(function=None, input_specs={}):
  Examples:
  .. code-block:: python
 
- import paddle
- from paddle.fluid.ir import RegisterPass
-
- @RegisterPass
- def multi_add_to_addn():
- def pattern(x, y, z):
- return paddle.add(paddle.add(x, y), z)
- def replace(x, y, z):
- return paddle.add_n([x, y, z])
- return pattern, replace
+  >>> import paddle
+  >>> from paddle.fluid.ir import RegisterPass
+
+  >>> @RegisterPass
+  >>> def multi_add_to_addn():
+ ... def pattern(x, y, z):
+ ...  return paddle.add(paddle.add(x, y), z)
+ ... def replace(x, y, z):
+ ...  return paddle.add_n([x, y, z])
+ ... return pattern, replace
  """
 
  def _is_pass_pair(check_pair):