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未验证 提交 2d50a64d 编写于 作者: iSerendipity's avatar iSerendipity 提交者： GitHub
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[xdoctest][task 292] reformat example code with google style in... 

[xdoctest][task 292] reformat example code with google style in `python/paddle/nn/functional/norm.py` (#56825)
上级 e4699231
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Showing with 81 addition and 61 deletion
python/paddle/nn/functional/norm.py
浏览文件 @ 2d50a64d
...@@ -53,28 +53,29 @@ def normalize(x, p=2, axis=1, epsilon=1e-12, name=None): ...@@ -53,28 +53,29 @@ def normalize(x, p=2, axis=1, epsilon=1e-12, name=None):
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
import paddle.nn.functional as F >>> import paddle.nn.functional as F
paddle.disable_static() >>> paddle.disable_static()
x = paddle.arange(6, dtype="float32").reshape([2,3]) >>> x = paddle.arange(6, dtype="float32").reshape([2,3])
y = F.normalize(x) >>> y = F.normalize(x)
print(y) >>> print(y)
# Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[0. , 0.44721359, 0.89442718], [[0. , 0.44721359, 0.89442718],
# [0.42426404, 0.56568539, 0.70710671]]) [0.42426404, 0.56568539, 0.70710671]])
y = F.normalize(x, p=1.5) >>> y = F.normalize(x, p=1.5)
print(y) >>> print(y)
# Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[0. , 0.40862012, 0.81724024], [[0. , 0.40862012, 0.81724024],
# [0.35684016, 0.47578689, 0.59473360]]) [0.35684016, 0.47578689, 0.59473360]])
y = F.normalize(x, axis=0) >>> y = F.normalize(x, axis=0)
print(y) >>> print(y)
# Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[0. , 0.24253564, 0.37139067], [[0. , 0.24253564, 0.37139067],
# [1. , 0.97014254, 0.92847669]]) [1. , 0.97014254, 0.92847669]])
""" """
if in_dygraph_mode(): if in_dygraph_mode():
...@@ -148,31 +149,29 @@ def batch_norm( ...@@ -148,31 +149,29 @@ def batch_norm(
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
x = paddle.arange(12, dtype="float32").reshape([2, 1, 2, 3]) >>> x = paddle.arange(12, dtype="float32").reshape([2, 1, 2, 3])
print(x) >>> print(x)
# Tensor(shape=[2, 1, 2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, Tensor(shape=[2, 1, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[[[0. , 1. , 2. ], [[[[0. , 1. , 2. ],
# [3. , 4. , 5. ]]], [3. , 4. , 5. ]]],
[[[6. , 7. , 8. ],
# [[[6. , 7. , 8. ], [9. , 10., 11.]]]])
# [9. , 10., 11.]]]]) >>> running_mean = paddle.to_tensor([0], dtype="float32")
>>> running_variance = paddle.to_tensor([1], dtype="float32")
running_mean = paddle.to_tensor([0], dtype="float32") >>> weight = paddle.to_tensor([2], dtype="float32")
running_variance = paddle.to_tensor([1], dtype="float32") >>> bias = paddle.to_tensor([1], dtype="float32")
weight = paddle.to_tensor([2], dtype="float32")
bias = paddle.to_tensor([1], dtype="float32") >>> batch_norm_out = paddle.nn.functional.batch_norm(x, running_mean,
... running_variance, weight, bias)
batch_norm_out = paddle.nn.functional.batch_norm(x, running_mean, >>> print(batch_norm_out)
running_variance, weight, bias) Tensor(shape=[2, 1, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
print(batch_norm_out) [[[[1. , 2.99998999 , 4.99997997 ],
# Tensor(shape=[2, 1, 2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, [6.99996948 , 8.99995995 , 10.99994946]]],
# [[[[1. , 2.99998999 , 4.99997997 ], [[[12.99993896, 14.99992943, 16.99991989],
# [6.99996948 , 8.99995995 , 10.99994946]]], [18.99990845, 20.99989891, 22.99988937]]]])
# [[[12.99993896, 14.99992943, 16.99991989],
# [18.99990845, 20.99989891, 22.99988937]]]])
""" """
assert len(x.shape) >= 2, "input dim must be larger than 1" assert len(x.shape) >= 2, "input dim must be larger than 1"
...@@ -300,11 +299,21 @@ def layer_norm( ...@@ -300,11 +299,21 @@ def layer_norm(
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
>>> paddle.seed(2023)
>>> x = paddle.rand((2, 2, 2, 3))
>>> layer_norm_out = paddle.nn.functional.layer_norm(x, x.shape[1:])
>>> print(layer_norm_out)
Tensor(shape=[2, 2, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[ 0.87799639, -0.32706568, -1.23529339],
[ 1.01540327, -0.66222906, -0.72354043]],
[[ 1.24183702, 0.45458138, -0.33506915],
[ 0.41468468, 1.26852870, -1.98983312]]],
[[[ 0.02837803, 1.27684665, -0.90110683],
[-0.94709367, -0.15110941, -1.16546965]],
[[-0.82010198, 0.11218392, -0.86506516],
[ 1.09489357, 0.19107464, 2.14656854]]]])
x = paddle.rand((2, 2, 2, 3))
layer_norm_out = paddle.nn.functional.layer_norm(x, x.shape[1:])
print(layer_norm_out)
""" """
input_shape = list(x.shape) input_shape = list(x.shape)
input_ndim = len(input_shape) input_ndim = len(input_shape)
...@@ -415,12 +424,21 @@ def instance_norm( ...@@ -415,12 +424,21 @@ def instance_norm(
.. code-block:: python .. code-block:: python
import paddle >>> import paddle
>>> paddle.seed(2023)
x = paddle.rand((2, 2, 2, 3)) >>> x = paddle.rand((2, 2, 2, 3))
instance_norm_out = paddle.nn.functional.instance_norm(x) >>> instance_norm_out = paddle.nn.functional.instance_norm(x)
print(instance_norm_out) >>> print(instance_norm_out)
Tensor(shape=[2, 2, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[ 1.25768495, -0.18054862, -1.26451230],
[ 1.42167914, -0.58056390, -0.65373862]],
[[ 0.95882601, 0.25075224, -0.45947552],
[ 0.21486834, 0.98283297, -1.94780385]]],
[[[ 0.40697321, 1.90885782, -0.71117985],
[-0.76650119, 0.19105314, -1.02920341]],
[[-1.06926346, -0.18710862, -1.11180890],
[ 0.74275863, -0.11246002, 1.73788261]]]])
""" """
if in_dygraph_mode(): if in_dygraph_mode():
...@@ -510,13 +528,15 @@ def local_response_norm( ...@@ -510,13 +528,15 @@ def local_response_norm(
Examples: Examples:
.. code-block:: python .. code-block:: python
>>> import paddle
import paddle >>> x = paddle.rand(shape=(3, 3, 112, 112), dtype="float32")
>>> y = paddle.nn.functional.local_response_norm(x, size=5)
>>> print(y.shape)
[3, 3, 112, 112]
x = paddle.rand(shape=(3, 3, 112, 112), dtype="float32")
y = paddle.nn.functional.local_response_norm(x, size=5)
print(y.shape) # [3, 3, 112, 112]
""" """
if not in_dynamic_mode(): if not in_dynamic_mode():
check_variable_and_dtype( check_variable_and_dtype(
......
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