How to Fix the TypeError: cannot unpack non-iterable float object in Your ML Model Evaluation

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Resolving the TypeError: cannot unpack non-iterable float object in ML Models

When working with machine learning models, encountering errors is a common challenge. One such issue is the error message: TypeError: cannot unpack non-iterable float object. This message can be particularly frustrating especially when you are trying to evaluate your model's performance. In this guide, we will dissect the possible causes of this error and guide you through the steps needed to rectify it effectively.

Understanding the Problem

The Error Message

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This usually happens when the evaluation does not return the expected two outputs (loss and accuracy) that can be unpacked into two variables. Instead, it returns only the loss value and no additional metrics.

The Code Context

Here's a snippet of the code where the error might occur during model evaluation:

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The Solution

Check Your Model's Metrics

Investigate Model Metrics: First, you need to check the available metrics for your model with the following line of code:

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If the output is something like: ['loss'], it indicates that there is no accuracy metric defined for your model.

Adding Accuracy Metric: To compute accuracy, you'll need to add it to your model's compile step. This ensures that the model tracks accuracy during training and testing.

Modify Your Code Accordingly

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Adjust Evaluation Code: After updating the compile method, your evaluation code should look like this:

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Conclusion

By following the steps outlined above, you should now be able to avoid the TypeError: cannot unpack non-iterable float object error when evaluating your ML model. Always ensure that you have the correct metrics specified in your model compilation to receive comprehensive evaluation outputs. Happy coding!