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SigureMo merged 2 commits into from
Feb 1, 2026
Merged

[CodeStyle] Use pycon for code-block language marker - part9 #77611

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9310eb3
[CodeStyle] Change python to pycon - part9
ShigureNyako Jan 31, 2026
11564da
fix Assign docstring sample shapes
ShigureNyako Jan 31, 2026
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41 changes: 25 additions & 16 deletions python/paddle/nn/initializer/assign.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,7 +200,7 @@ class Assign(NumpyArrayInitializer):
A parameter initialized by the input numpy array, list, or tensor.

Examples:
.. code-block:: python
.. code-block:: pycon

>>> import paddle
>>> import numpy as np
Expand All @@ -209,55 +209,64 @@ class Assign(NumpyArrayInitializer):
>>> data_1 = paddle.ones(shape=[1, 2], dtype='float32')
>>> weight_attr_1 = paddle.ParamAttr(
... name="linear_weight_1",
... initializer=paddle.nn.initializer.Assign(np.array([2, 2])))
... initializer=paddle.nn.initializer.Assign(np.array([[2, 2], [2, 2]])),
... )
>>> bias_attr_1 = paddle.ParamAttr(
... name="linear_bias_1",
... initializer=paddle.nn.initializer.Assign(np.array([2])))
... initializer=paddle.nn.initializer.Assign(np.array([2, 2])),
... )
>>> linear_1 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_1, bias_attr=bias_attr_1)
>>> print(linear_1.weight.numpy())
[2. 2.]
[[2. 2.]
[2. 2.]]
>>> print(linear_1.bias.numpy())
[2.]
[2. 2.]

>>> res_1 = linear_1(data_1)
>>> print(res_1.numpy())
[6.]
[[6. 6.]]

>>> # python list
>>> data_2 = paddle.ones(shape=[1, 2], dtype='float32')
>>> weight_attr_2 = paddle.ParamAttr(
... name="linear_weight_2",
... initializer=paddle.nn.initializer.Assign([2, 2]))
... initializer=paddle.nn.initializer.Assign([[2, 2], [2, 2]]),
... )
>>> bias_attr_2 = paddle.ParamAttr(
... name="linear_bias_2",
... initializer=paddle.nn.initializer.Assign([2]))
... initializer=paddle.nn.initializer.Assign([2, 2]),
... )
>>> linear_2 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_2, bias_attr=bias_attr_2)
>>> print(linear_2.weight.numpy())
[2. 2.]
[[2. 2.]
[2. 2.]]
>>> print(linear_2.bias.numpy())
[2.]
[2. 2.]

>>> res_2 = linear_2(data_2)
>>> print(res_2.numpy())
[6.]
[[6. 6.]]

>>> # tensor
>>> data_3 = paddle.ones(shape=[1, 2], dtype='float32')
>>> weight_attr_3 = paddle.ParamAttr(
... name="linear_weight_3",
... initializer=paddle.nn.initializer.Assign(paddle.full([2], 2)))
... initializer=paddle.nn.initializer.Assign(paddle.full([2, 2], 2)),
... )
>>> bias_attr_3 = paddle.ParamAttr(
... name="linear_bias_3",
... initializer=paddle.nn.initializer.Assign(paddle.full([1], 2)))
... initializer=paddle.nn.initializer.Assign(paddle.full([2], 2)),
... )
>>> linear_3 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_3, bias_attr=bias_attr_3)
>>> print(linear_3.weight.numpy())
[2. 2.]
[[2. 2.]
[2. 2.]]
>>> print(linear_3.bias.numpy())
[2.]
[2. 2.]

>>> res_3 = linear_3(data_3)
>>> print(res_3.numpy())
[6.]
[[6. 6.]]
"""

def __init__(
Expand Down
27 changes: 15 additions & 12 deletions python/paddle/nn/initializer/bilinear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ class Bilinear(Initializer):

Examples:

.. code-block:: python
.. code-block:: pycon

>>> import math

Expand All @@ -53,18 +53,21 @@ class Bilinear(Initializer):
>>> C = 2
>>> B = 8
>>> H = W = 32
>>> w_attr = paddle.ParamAttr(learning_rate=0.,
... regularizer=L2Decay(0.),
... initializer=nn.initializer.Bilinear())
>>> w_attr = paddle.ParamAttr(
... learning_rate=0.0,
... regularizer=L2Decay(0.0),
... initializer=nn.initializer.Bilinear(),
... )
>>> data = paddle.rand([B, 3, H, W], dtype='float32')
>>> conv_up = nn.Conv2DTranspose(3,
... out_channels=C,
... kernel_size=2 * factor - factor % 2,
... padding=int(
... math.ceil((factor - 1) / 2.)),
... stride=factor,
... weight_attr=w_attr,
... bias_attr=False)
>>> conv_up = nn.Conv2DTranspose(
... 3,
... out_channels=C,
... kernel_size=2 * factor - factor % 2,
... padding=int(math.ceil((factor - 1) / 2.0)),
... stride=factor,
... weight_attr=w_attr,
... bias_attr=False,
... )
>>> x = conv_up(data)

Where, `out_channels=C` and `groups=C` means this is channel-wise transposed
Expand Down
2 changes: 1 addition & 1 deletion python/paddle/nn/initializer/initializer.py
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Expand Up @@ -169,7 +169,7 @@ def calculate_gain(
A float value, which is the recommended gain for this nonlinearity function.

Examples:
.. code-block:: python
.. code-block:: pycon

>>> import paddle

Expand Down
4 changes: 2 additions & 2 deletions python/paddle/nn/initializer/kaiming.py
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Expand Up @@ -309,7 +309,7 @@ class KaimingNormal(MSRAInitializer):
It is recommended to set fan_in to None for most cases.

Examples:
.. code-block:: python
.. code-block:: pycon

>>> import paddle
>>> import paddle.nn as nn
Expand Down Expand Up @@ -363,7 +363,7 @@ class KaimingUniform(MSRAInitializer):
It is recommended to set fan_in to None for most cases.

Examples:
.. code-block:: python
.. code-block:: pycon

>>> import paddle
>>> import paddle.nn as nn
Expand Down
16 changes: 10 additions & 6 deletions python/paddle/nn/initializer/normal.py
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Expand Up @@ -148,17 +148,19 @@ class Normal(NormalInitializer):
A parameter initialized by Random Normal (Gaussian) distribution.

Examples:
.. code-block:: python
.. code-block:: pycon

>>> import paddle

>>> data = paddle.ones(shape=[3, 1, 2], dtype='float32')
>>> weight_attr = paddle.framework.ParamAttr(
... name="linear_weight",
... initializer=paddle.nn.initializer.Normal(mean=0.0, std=2.0))
... initializer=paddle.nn.initializer.Normal(mean=0.0, std=2.0),
... )
>>> bias_attr = paddle.framework.ParamAttr(
... name="linear_bias",
... initializer=paddle.nn.initializer.Normal(mean=0.0, std=2.0))
... initializer=paddle.nn.initializer.Normal(mean=0.0, std=2.0),
... )
>>> # doctest: +SKIP('name has been used')
>>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
>>> print(linear.weight)
Expand Down Expand Up @@ -359,17 +361,19 @@ class TruncatedNormal(TruncatedNormalInitializer):
A parameter initialized by truncated normal distribution (Gaussian distribution).

Examples:
.. code-block:: python
.. code-block:: pycon

>>> import paddle

>>> data = paddle.ones(shape=[3, 1, 2], dtype='float32')
>>> weight_attr = paddle.framework.ParamAttr(
... name="linear_weight",
... initializer=paddle.nn.initializer.TruncatedNormal(mean=0.0, std=2.0))
... initializer=paddle.nn.initializer.TruncatedNormal(mean=0.0, std=2.0),
... )
>>> bias_attr = paddle.framework.ParamAttr(
... name="linear_bias",
... initializer=paddle.nn.initializer.TruncatedNormal(mean=0.0, std=2.0))
... initializer=paddle.nn.initializer.TruncatedNormal(mean=0.0, std=2.0),
... )
>>> # doctest: +SKIP('name has been used')
>>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
>>> print(linear.weight)
Expand Down
2 changes: 1 addition & 1 deletion python/paddle/nn/initializer/orthogonal.py
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Expand Up @@ -56,7 +56,7 @@ class Orthogonal(Initializer):
A parameter initialized by orthogonal initialized.

Examples:
.. code-block:: python
.. code-block:: pycon

>>> import paddle

Expand Down
16 changes: 10 additions & 6 deletions python/paddle/nn/initializer/xavier.py
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Expand Up @@ -316,17 +316,19 @@ class XavierNormal(XavierInitializer):
A parameter initialized by Xavier weight, using a normal distribution.

Examples:
.. code-block:: python
.. code-block:: pycon

>>> import paddle
>>> paddle.seed(1)
>>> data = paddle.ones(shape=[3, 1, 2], dtype='float32')
>>> weight_attr = paddle.framework.ParamAttr(
... name="linear_weight",
... initializer=paddle.nn.initializer.XavierNormal())
... initializer=paddle.nn.initializer.XavierNormal(),
... )
>>> bias_attr = paddle.framework.ParamAttr(
... name="linear_bias",
... initializer=paddle.nn.initializer.XavierNormal())
... initializer=paddle.nn.initializer.XavierNormal(),
... )
>>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
>>> print(linear.weight)
Parameter containing:
Expand Down Expand Up @@ -386,17 +388,19 @@ class XavierUniform(XavierInitializer):
A parameter initialized by Xavier weight, using a uniform distribution.

Examples:
.. code-block:: python
.. code-block:: pycon

>>> import paddle
>>> paddle.seed(1)
>>> data = paddle.ones(shape=[3, 1, 2], dtype='float32')
>>> weight_attr = paddle.framework.ParamAttr(
... name="linear_weight",
... initializer=paddle.nn.initializer.XavierUniform())
... initializer=paddle.nn.initializer.XavierUniform(),
... )
>>> bias_attr = paddle.framework.ParamAttr(
... name="linear_bias",
... initializer=paddle.nn.initializer.XavierUniform())
... initializer=paddle.nn.initializer.XavierUniform(),
... )
>>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
>>> print(linear.weight)
Parameter containing:
Expand Down
2 changes: 1 addition & 1 deletion python/paddle/nn/utils/clip_grad_norm_.py
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Expand Up @@ -54,7 +54,7 @@ def clip_grad_norm_(
Total norm of the parameter gradients (treated as a single vector).

Example:
.. code-block:: python
.. code-block:: pycon

>>> import paddle

Expand Down
2 changes: 1 addition & 1 deletion python/paddle/nn/utils/spectral_norm_hook.py
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Expand Up @@ -209,7 +209,7 @@ def spectral_norm(
Layer, the original layer with the spectral norm hook.

Examples:
.. code-block:: python
.. code-block:: pycon

>>> import paddle
>>> from paddle.nn import Conv2D
Expand Down
2 changes: 1 addition & 1 deletion python/paddle/nn/utils/weight_norm_hook.py
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Expand Up @@ -233,7 +233,7 @@ def remove_weight_norm(layer: Layer, name: str = 'weight') -> Layer:
Layer, the origin layer without weight norm

Examples:
.. code-block:: python
.. code-block:: pycon

>>> import paddle
>>> from paddle.nn import Conv2D
Expand Down
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