AttributeError: module ‘tensorflow’ has no attribute ‘variable_scope’ error occurs when you are using the ‘variable_scope’ attribute on TensorFlow version 2, and it does not exist because it is deprecated.
How to fix it?
To fix the AttributeError: module ‘tensorflow’ has no attribute ‘variable_scope’ error, use a “tf.Variable” directly or use Keras layers and models which manage their variables.
Alternatively, you can enable TensorFlow 1.x compatibility mode in TensorFlow 2.x by using tf.compat.v1. This will allow most TensorFlow 1.x code to run unchanged in TensorFlow 2.x. For example, you could change tf.variable_scope to tf.compat.v1.variable_scope.
However, remember that this compatibility mode is intended as a temporary solution to help transition to TensorFlow 2.x, and it may not be supported in future versions of TensorFlow. It’s generally recommended to update your code to use the new TensorFlow 2.x APIs where possible.
Here is an example of how you might update a piece of TensorFlow 1.x code using variable_scope:
# TensorFlow 1.x with tf.variable_scope('scope'): v = tf.get_variable('v', shape=) # TensorFlow 2.x with compatibility mode with tf.compat.v1.variable_scope('scope'): v = tf.compat.v1.get_variable('v', shape=)
And here is an example of how you might rewrite the code to use TensorFlow 2.x APIs directly:
# TensorFlow 2.x v = tf.Variable(tf.zeros(), name='scope/v')
I hope this helps you resolve your issue!