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[xdoctest][task 337] reformat example code with google style in python/paddle/base/framework.py #57151

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Sep 11, 2023
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[xdoctest][task 337] reformat example code with google style in python/paddle/base/framework.py #57151

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[Doctest]fix No.337, test=docs_preview
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7731270
fix
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fix loss
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6c22ad6
fix type
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fix type
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17 changes: 10 additions & 7 deletions python/paddle/base/framework.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1006,10 +1006,10 @@ def name_scope(prefix=None):
>>> with paddle.static.name_scope("s1"):
... a = paddle.static.data(name='data', shape=[None, 1], dtype='int32')
... b = a + 1
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这里仍然使用 scalar 的话，基本不会走下面的分支的

>>> with paddle.static.name_scope("s2"):
... c = b * 1
>>> with paddle.static.name_scope("s3"):
... d = c / 1
... with paddle.static.name_scope("s2"):
... c = b * 1
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下面这几个也需要改哦

... with paddle.static.name_scope("s3"):
... d = c / 1
>>> with paddle.static.name_scope("s1"):
... f = paddle.tensor.pow(d, 2.0)
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这个也要 😂

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😊是的，新的改好啦~

>>> with paddle.static.name_scope("s4"):
Expand Down Expand Up @@ -1538,7 +1538,7 @@ def numpy(self):

>>> import paddle.base as base
>>> from paddle.base.dygraph.base import to_variable
>>> from paddle.base.dygraph import Linear
>>> from paddle.nn import Linear
>>> import numpy as np

>>> data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
Expand Down Expand Up @@ -1628,6 +1628,7 @@ def gradient(self):
... inputs2.append(tmp)
... ret2 = paddle.add_n(inputs2)
... loss2 = paddle.sum(ret2)
... loss2.retain_grads()
... loss2.backward()
... print(loss2.gradient())

Expand Down Expand Up @@ -1664,7 +1665,7 @@ def clear_gradient(self):
.. code-block:: python

>>> import paddle
>>> import paddle.base as base
>>> import paddle.fluid as base
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fluid？

>>> import numpy as np

>>> x = np.ones([2, 2], np.float32)
Expand All @@ -1676,11 +1677,13 @@ def clear_gradient(self):
... inputs2.append(tmp)
... ret2 = paddle.add_n(inputs2)
... loss2 = paddle.sum(ret2)
... loss2.retain_grads()
... loss2.backward()
... print(loss2.gradient())
... loss2.clear_gradient()
... print("After clear {}".format(loss2.gradient()))

1.0
After clear 0.0
"""
pass

Expand Down