Attributeerror: module keras.utils has no attribute sequence

Attributeerror: module keras.utils has no attribute sequence error occurs because of an outdated version of keras. The syntax of the import statement is on the basis of a higher version of Keras and underlining the Keras version is lower will create this type of incompatibility. Actually, the root cause for this error is an inner refactoring of Keras module in the latest version which has changed the internal file structure in the higher version. In this article, we will understand how we can fix this error.

Attributeerror: module keras.utils has no attribute sequence ( Solution )-

As we have already explained to you that the only way to fix this error is to upgrade keras. It is very simple to upgrade any python package if you are using pip package manager but in case if you are building

Solution 1: Upgrading keras version using pip –

The below command will install the latest version for Keras.

pip install keras

You can also make it version specific by providing the version name.

pip install keras==2.8.0

Solution 2: Upgrading Keras version using Conda-

Run the below command if your package manager is conda.

conda install -c conda-forge keras

Solution 3: Upgrading Keras version using source code –

If you want to upgrade keras via source code then you need to download the respective release branch and then run –

python install setup.py

Notes :

This is not only the error that occurs because of this incompatibility but there are similar errors which has the same root cause and similar solution.

1. Attributeerror: module ‘keras.utils’ has no attribute ‘to_categorical’
2. Attributeerror module ‘keras.utils’ has no attribute ‘get_value’
3. Attributeerror module ‘keras.utils’ has no attribute ‘load_img’
4. Attributeerror module ‘keras.utils’ has no attribute ‘plot_model’

There are several other modules like  ‘generic utils’, ‘all utils’, ‘normalize’  are associated with this type of error. So don’t get confused with different modules the solution will remain the same for all.

Thanks

Data Science Learner Team