AttributeError: Add.Functions

如果出现 `AttributeError: Add.Functions` 的错误，可能是因为 `Add` 方法返回的对象不支持 `Functions` 属性，可以使用下面的代码来解决该问题：

import win32com.client as win32

app = win32.Dispatch("Word.Application")
doc = app.Documents.Add()
app.visible = True
s = app.Selection
objRange = s.Range

c1 = 10
c2 = 12

# 使用数学公式的方式生成公式字符串
om = s.OMaths.Add(objRange)
omf = app.OMathFunctions
sqrt = omf.Sqrt(omf.Nary(om.Range.Characters(3), om.Range.Characters(6)))
y1 = 'y=' + sqrt.BuildUp() + '=' + str(c1) + '+' + str(c2) + '=' + str(c1 + c2) + '^p '

objRange.Text = y1
om.Range.Select()

# 转化为数学公式
s.OMaths(1).BuildUp()

doc.Close(False)
app.Quit()

这样，就可以正确地添加根号符号，并将公式转化为数学公式了。

相关问题

AttributeError: Word.Application.OMathFunctions

如果出现 `AttributeError: Word.Application.OMathFunctions` 的错误，可能是因为 `OMathFunctions` 对象不可用。可以使用下面的代码来解决该问题：

import win32com.client as win32

app = win32.Dispatch("Word.Application")
doc = app.Documents.Add()
app.visible = True
s = app.Selection
objRange = s.Range

c1 = 10
c2 = 12

# 使用数学公式的方式生成公式字符串
om = s.OMaths.Add(objRange)
omf = app.OMathAutoCorrect
omf.Entries.Add("sqrt", r"\sqrt", False)
sqrt = om.Range.OMaths.Add(om.Range).Functions.Item("sqrt")
sqrt.Nary(om.Range.Characters(3), om.Range.Characters(6))
y1 = 'y=' + om.BuildUp() + '=' + str(c1) + '+' + str(c2) + '=' + str(c1 + c2) + '^p '

objRange.Text = y1
om.Range.Select()

# 转化为数学公式
s.OMaths(1).BuildUp()

doc.Close(False)
app.Quit()

这样，就可以正确地添加根号符号，并将公式转化为数学公式了。

AttributeError: module 'tensorflow.keras' has no attribute 'get_default_graph'

This error occurs when you try to access the `get_default_graph` function in `tensorflow.keras` module, which does not exist.

In TensorFlow 2.0, the default graph is now implicit and is managed automatically. You no longer need to create a graph and set it as the default graph. Therefore, you should remove any references to `get_default_graph` in your code.

Instead, you can use TensorFlow 2.0's eager execution mode, which allows you to execute TensorFlow operations immediately as they are called, without building a computational graph.

To enable eager execution, simply add the following line at the beginning of your code:

import tensorflow as tf
tf.compat.v1.enable_eager_execution()

With eager execution, you can use the `tf.function` decorator to define TensorFlow operations as functions, and then call them directly.