Скачать или смотреть Resolving the ValueError in Keras Model Fitting: A Step-by-Step Guide to Fixing Common Issues

Encountering a `ValueError` when fitting a Keras model can be frustrating. In this guide, we’ll explore a common issue related to the `flow_from_directory` method and how to effectively resolve it.
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Resolving the ValueError in Keras Model Fitting: A Step-by-Step Guide to Fixing Common Issues

When building a Convolutional Neural Network (CNN) for multi-class classification using Keras, you may encounter a perplexing error during model training. Specifically, the error message:

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

can be quite frustrating. This error typically indicates a problem with the way you're passing data generators to the fit function. In this guide, we will delve into the underlying causes and outline a straightforward solution to get your model training effectively.

Understanding the Problem

You are using the flow_from_directory method of the ImageDataGenerator class to load your training and validation datasets. The function generates batches of data from the images stored in a directory structure as required by Keras. However, two common mistakes can lead to the value error you're experiencing:

Incorrect Target Size: The shape of the input images specified in flow_from_directory must match the input shape expected by your model.

Passing Method References Instead of Execution: If you pass function references to the fit function instead of the generated data, Keras cannot handle it, resulting in errors.

Step-by-Step Solution

1. Correcting the Target Size

Your model expects input shape defined as (40, 24, 1) which includes the number of channels (grayscale in this case). However, when using flow_from_directory, you should only specify the height and width like this:

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

2. Correcting the Data Passing

Instead of passing the function name (reference), you need to call the functions to get the actual data generators. Here's how you can do it correctly:

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

This ensures that you are passing the data generated by the get_train_images and get_validation_images functions into the fit method, allowing Keras to utilize it properly.

Updated Code Example

Here’s how your adjusted code should look:

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

Conclusion

By ensuring the correct target sizes are set and passing the return values of your data-generating functions in the fit method, you can overcome that pesky ValueError. This key fix not only enhances the robustness of your model training but also saves valuable time.

Happy coding, and may your CNN model yield excellent results!