"Module 'keras.utils.generic_utils' has no attribute 'get_custom_objects':" A Deep Dive into Segmentation Models and Keras Compatibility
Many machine learning enthusiasts encounter the error "Module 'keras.utils.generic_utils' has no attribute 'get_custom_objects'" when working with segmentation models, especially when importing libraries like segmentation_models. Let's break down this error and understand how to resolve it.
Understanding the Problem
The error message signifies a clash between the versions of Keras used by segmentation_models and the one currently active in your Python environment. Essentially, segmentation_models expects a specific version of Keras that includes a function called get_custom_objects within the keras.utils.generic_utils module, but your environment is likely using an incompatible version.
The Scenario and Code Example
Imagine you're trying to implement a segmentation model using segmentation_models in your project. You might start with the following code:
import segmentation_models as sm
# ... rest of your code ...
model = sm.Unet('resnet34', input_shape=(256, 256, 3), classes=21, activation='softmax')
Running this code might throw the error "Module 'keras.utils.generic_utils' has no attribute 'get_custom_objects'".
Analysis and Clarification
The segmentation_models library, designed for image segmentation, relies heavily on Keras, a deep learning framework. segmentation_models uses Keras's get_custom_objects function to handle custom layers and activation functions defined within the library. If the Keras version you're using doesn't have this function, you will encounter this error.
Solution: Matching Versions
The solution lies in ensuring your Keras version is compatible with the segmentation_models library. The most common approach is to install a compatible version of Keras:
pip install keras==2.6.0 # or any compatible version specified by segmentation_models
Important: The exact version might vary. Refer to the segmentation_models documentation for the latest compatibility details. You can also use pip show keras to see your current Keras version.
Additional Insights
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Virtual Environments: Using virtual environments (e.g.,
venvorconda) is highly recommended when working with different projects. Virtual environments help isolate dependencies and prevent version conflicts. -
TensorFlow and Keras Integration: TensorFlow now includes Keras as an integral part. If you're using TensorFlow, you likely have Keras installed with it. However, always double-check compatibility.
Conclusion
The "Module 'keras.utils.generic_utils' has no attribute 'get_custom_objects'" error is a common compatibility issue encountered while using segmentation_models. By carefully managing Keras versions, leveraging virtual environments, and referring to documentation, you can effectively resolve this issue and delve into the world of image segmentation.
