How to Fix 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 reasons for the error

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 the construction and manipulation of 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.

Steps to Fix the error

Solution 1:  Upgrade the Keras and TensorFlow

pip install keras --upgrade

pip install tensorflow --upgrade

Troubleshooting Steps to Fix the AttributeError Issue

And Here is the latest version of Keras.

AttributeError - module keras.engine has no attribute layer

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.

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