Attributeerror: Module ‘keras.backend’ has no attribute ‘get_session’

In this tutorial, you will learn how to Attributeerror: Module ‘keras.backend’ has no attribute ‘get_session’.

I will explain several solutions to this error and provide the best solution that you can use to get rid of it permanently.

Let’s start,

Attributeerror: Module ‘keras.backend’ has no attribute ‘get_session’

The error Attributeerror: Module ‘keras.backend’ has no attribute ‘get_session’, indicating that you are trying to access the get_session() attribute from the submodule keras.backend. That is, the attribute doesn’t exist in this module.

Let me show you the code that can generate this error,

from keras import backend
backend.get_session()
Attributeerror Module 'keras.backend' has no attribute 'get_session'

When you try to access the attribute get_session() from the keras.backend, look at the above output, it shows the error.

Also, if you try the code below, you will get the same error.

import tensorflow as tf
tf.keras.backend.get_session()

This error occurs because of an incompatibility between the version of Keras or TensorFlow Keras you are using and the code that is attempting to use the get_session() attribute or function.

As here, you are using Keras as a standalone, I would suggest you use the tensorflow instead of Keras, as in the new version of TensorFlow, Keras is integrated so that you can use all the Keras function from the submodule tf.keras.

To fix this error, there are several solutions that you will learn one by one. But here, I will explain the solution based on TensorFlow.

The first solution is to downgrade the version of TensorFlow. Here, you can use the below command to downgrade the TensorFlow version to 1.14.

pip install tensorflow==1.14
!pip install tensorflow==1.14

After downgrading, you will be able to access the get_session() attribute from the tf.keras

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If downgrading does not work, you can follow the next solution, which requires using the tf.compat.v1 module.

You can access all the functions or attributes from the older version of Tensorflow using this module.

import tensorflow as tf

tf.compat.v1.keras.backend.get_session()
Second Solution to Attributeerror Module 'keras.backend' has no attribute 'get_session'

You won’t get that error from the output because, this time, you are accessing the get_session() from the tf.compat.v1.keras.backend submodule. The tf.compat.v1 is a module that allows you to access all the methods or functions of TensorFlow version 1 in the environment of TensorFlow version 2.

The next solution is to use the sub-module tensorflow.python.keras, you can access the get_session() from this submodule as shown below.

import tensorflow.python.keras.backend as K
K.get_session()
Third Solution to Attributeerror Module 'keras.backend' has no attribute 'get_session'

Look when you used the tensorflow.python.keras.backends sub-module, you can access the get_session() function. Before using this method, also remember that the submodule tensorflow.python is private to tensorflow.

  • It would be best not to use the submodule, but since you want to run your code, you can.

The last solution is to use Tensorflow version 2 because this version doesn’t require a session; it supports eager execution by default.

The best solution is to adapt to the latest version code because you are still using the older version code; right now, you can access all the older versions using tf. compact.v1, but TensorFlow can also remove that module in the future.

Thus, the code will not work again, so always use the latest version and constantly check the changes in TensorFlow’s API.

This is how to resolve the error Attributeerror: Module ‘keras.backend’ has no attribute ‘get_session’.

Conclusion

In this tutorial you learned how to fix the error Attributeerror: Module ‘keras.backend’ has no attribute ‘get_session’

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You learned how to downgrade the Tensorflow version to solve the error and use the tf.compat.v1 module to solve that error. Then, you learned that the best solution to this error is to adapt to the code of the latest version of TensorFlow.

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