AttributeError: module ‘tensorflow’ has no attribute ‘reset_default_graph’ error occurs when you try to “reset the default graph in TensorFlow using the reset_default_graph() function, but the function is not available in the current version of TensorFlow that you are using.”
The reset_default_graph() function is a utility function in TensorFlow that clears the default graph and resets the global default graph. It is used to create and build a new computational graph from scratch.
How to fix it?
Solution 1: Upgrade the tensorflow
The easiest fix for the AttributeError: module ‘tensorflow’ has no attribute ‘reset_default_graph’ error is to “upgrade the tensorflow” using this command: pip install –upgrade tensorflow
In TensorFlow 1.0 and later versions, the default graph is now automatically created when you import TensorFlow. Therefore, there is no longer a need to manually reset the default graph.
If you try to use the reset_default_graph() function in TensorFlow 1.0 or later, you will get the error message “AttributeError: module ‘tensorflow’ has no attribute ‘reset_default_graph'”.
Solution 2: Using the tf.compat.v1.reset_default_graph() method
If you cannot upgrade to a newer version of TensorFlow or remove the “reset_default_graph()” function from your code, you can also use the “tf.compat.v1.reset_default_graph()” function. This function is a compatibility layer that allows you to use the reset_default_graph() function in TensorFlow 1.0 and later versions.
import tensorflow as tf tf.compat.v1.reset_default_graph()
This will disable eager execution and allow you to use TensorFlow 1.x style graph operations, including reset_default_graph(). But it’s generally recommended to write code in the TensorFlow 2.x style whenever possible, as it’s more modern and easier to work with. If you share your specific code, I can help you adapt it to TensorFlow 2.x if necessary!
- If you’re using TensorFlow 2.x or a later version, simply remove the reset_default_graph() function from your code, as it’s no longer necessary.
- If your code is specifically intended for TensorFlow 1.x and you need to use the reset_default_graph() function, you can either downgrade to TensorFlow 1.x or use the compatibility layer by importing tensorflow.compat.v1 and disable v2 behavior, as shown in my previous message.
I hope this will fix your issue and solve your confusion regarding the TensorFlow versions 1.0 and 2.0.