Attributeerror: Module ‘tensorflow’ has no attribute ‘variable_scope’

In this TensorFlow tutorial, you learn how to resolve the error Attributeerror: Module ‘tensorflow’ has no attribute ‘variable_scope’.

I have a project where I used the variable_scope() function from tensorflow. After a long time, I updated my Tensorlfow, and this error began to appear.

Then, I found the solution for this error, so here you will learn how to fix it using the compatibility mode in TensorFlow.

Attributeerror: Module ‘tensorflow’ has no attribute ‘variable_scope’

This error Attributeerror: Module ‘tensorflow’ has no attribute ‘variable_scope’, indicating that you are trying to access the variable_scope attribute from the tensorflow module, but this variable doesn’t exist.

I want you to show the code that can generate this error.

import tensorflow as tf
print(tf.variable_scope())
Attributeerror Module 'tensorflow' has no attribute 'variable_scope'

Look, when you try to access the variable_scope() function from the tf, it shows the error, but what are the reasons that are causing this error? there are several reasons, but I am going to explain the exact reason that is responsible for this error.

That is because of the changes in the Tensorflow API. When you upgrade your tensorflow library to the latest version, something 2.x, this error appears.

Because your code is compatible with TensorFlow version 1.x. and Tensorflow version 2.x has been changed, so you are getting the error.

So you have a solution: stick with the old version but must use the compatibility mode.

So you have installed TensorFlow version 2.x to access the function of Tensorflow version 1.x; use the module tf. compat.v1 to access the variable_scope() function.

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The complete syntax is given below.

tf.compat.v1.variable_scope(
    name_or_scope,
    default_name=None,
    values=None,
    initializer=None,
    regularizer=None,
    caching_device=None,
    partitioner=None,
    custom_getter=None,
    reuse=None,
    dtype=None,
    use_resource=None,
    constraint=None,
    auxiliary_name_scope=True
)

Where parameters are :

  • name_or_scope: This parameter defines the string or variable scope.
  • default_name: The name used by default if the name or scope parameter is None will be uniquified. Name or Scope is unnecessary and can be None because it won’t be used if provided.
  • Values: The list of arguments for Tensors that the op function receives.
  • initializer: By default, it takes no value and initializer for variables within this scope.
  • regularize regularizer by default for variables in this scope.

Let’s take an example of how to access the variable_scope() function in compatibility mode of version 1.x in Tensorflow version 2.x.

Run the code below.

import tensorflow as tf

new_tens = tf.compat.v1.variable_scope(default_name='tens',values=[2,3],dtype=tf.int32, name_or_scope='tens')
print(new_tens)
Solution to Attributeerror Module 'tensorflow' has no attribute 'variable_scope'

The above created variable new_tens using the variable_scope() function from the tf.compat.v1 module.

This is how to access the variable_scope() function in the TensorFlow 2.x using the tf.compat_v1 module.

But remember, this variable_scope() has been removed from the TensorFlow version 2.x.

Another approach I can suggest is to migrate from TensorFlow 1.x to 2.x so that you can visit the official documentation Migrate from TensorFlow 1.x to TensorFlow 2.

Conclusion

You just learned how to fix the error Attributeerror: Module ‘tensorflow’ has no attribute ‘variable_scope’ using the compatibility mode in the TensorFlwo version 2.x environment.

Where you have used the tf.compat.v1 module to access the varaible_scope() function in the environment of the TensorFlow 2.

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