Скачать или смотреть Resolving ValueError in Keras Model Predictions: Handling NoneType Input

Discover how to fix the `ValueError` encountered in Keras when trying to make predictions with a CNN model. Learn about image preprocessing and the correct input shapes for model predictions.
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Resolving ValueError in Keras Model Predictions: Handling NoneType Input

If you're working with Keras and have encountered the frustrating ValueError: Failed to find data adapter that can handle input: <class 'NoneType'>, <class 'NoneType'> while making predictions with your model, don't worry! You're not alone, and this post will guide you through the steps to resolve this issue.

Understanding the Problem

You have created a Convolutional Neural Network (CNN) model in Keras, saved it, and are now trying to load it in a new script to make predictions. You expect the model to accept an image input of size 128x128. However, upon executing your prediction code, you receive an error indicating that the input being passed is of type NoneType. This can be quite confusing, especially when you believe you have correctly prepared your image.

Analyzing the Code

Here's the snippet of your code that might be causing the issue:

[[See Video to Reveal this Text or Code Snippet]]

Identifying Key Issues

Resizing the Image Incorrectly:
You are using the resize method after converting your image to an array. The issue is that resize is applied to a PIL image object, not an array, which returns None.

Input Shape for the Model:
Your model expects a 4-dimensional input array (batch size, height, width, channels). By simply passing the image array without reshaping, you are likely providing the model with the wrong input format.

How to Fix It

To resolve the ValueError, you can make the following adjustments to your code:

Step-by-Step Modification

Load and Resize before Converting to Array:
Ensure that you resize the image before converting it into a NumPy array.

Correctly Reshape the Input:
After loading and resizing your image, reshape it into a 4-dimensional array. This can be achieved using the reshape function.

Here’s the revised code:

[[See Video to Reveal this Text or Code Snippet]]

Key Changes Made:

Resizing Before Conversion: The resizing operation is now done on the PIL image object, ensuring the output is still a proper image before converting to an array.

Reshaping: img.reshape(-1, 128, 128, 3) reshapes the image array so it matches the expected input format of the model.

Conclusion

By following these modifications, you should be able to avoid the ValueError and successfully perform predictions with your Keras model. Handling images and ensuring they are in the correct format can sometimes be tricky, especially when transitioning between different libraries. Remember to always check the shape and type of your inputs to prevent such errors in the future.

If you found this information helpful, consider giving it a thumbs up! Happy coding!