AttributeError: module ‘keras.engine’ has no attribute ‘layer’

AttributeError: module keras.engine has no attribute layer error typically occurs when “there is an incompatibility between the versions of Keras and TensorFlow.”

Common Causes

Several factors can contribute to the occurrence of the “AttributeError: module keras.engine has no attribute layer” error. By identifying the potential causes, you can efficiently troubleshoot and resolve the issue.

Some common causes include:

1. The Keras module cannot find the specified layer attribute in the engine module. The layer attribute is a fundamental part of Keras that allows constructing and manipulating neural network layers.
2. Incompatibility between Keras versions.
3. Typographical errors in the code.
4. Incorrect Keras installation.
5. Misuse or incorrect usage of Keras layers.
6. Compatibility issues with backend frameworks.

Troubleshooting Steps to Fix the “AttributeError” Issue

Solution 1:  Upgrade the Keras and TensorFlow

pip install keras --upgrade

pip install tensorflow --upgrade

And Here is the latest version of Keras.

You can verify the Keras installation on your system using the below code.

import keras

print(keras.__version__)

You can also verify the TensorFlow version using the below code.

import tensorflow as tf

print(tf.__version__)

Solution 2: Keras and TensorFlow Upgrade using conda

You can upgrade the Tensorflow or Keras using the below command.

conda install -c conda-forge tensorflow

conda install -c conda-forge keras

Solution 3: Changing the Import Statement

Import like this to avoid the issue.

import keras.engine as KE

Solution 4: Confirm the Correct Usage of Keras Layers

Double-check the usage of Keras layers in your code. Verify that you are correctly utilizing the layer attribute and referring to the appropriate modules and objects. Consult the Keras documentation and examples for guidance if needed.

Solution 5: Check Compatibility with Backend Frameworks

Keras supports multiple backend frameworks, such as TensorFlow and Theano. Ensure that your chosen backend is compatible with the Keras version you are using. Refer to the Keras documentation for information on backend compatibility and configuration.

That’s it.

Krunal

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.