Resolving the Issue of Fitting a Model with Multiple Inputs in Keras

Discover how to successfully fit a Keras model with multiple inputs using proper data formatting and structure. Avoid common errors and simplify your training process!
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This video is based on the question https://stackoverflow.com/q/65463924/ asked by the user 'Sportalcraft' ( https://stackoverflow.com/u/6471637/ ) and on the answer https://stackoverflow.com/a/65464106/ provided by the user 'amin' ( https://stackoverflow.com/u/5498574/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Understanding the Challenge of Fitting a Model with Multiple Inputs

In the world of machine learning, it’s common to encounter models that require multiple inputs for training. This is particularly true when you are working with data that has various features that contribute to the prediction. However, for beginners (and sometimes even experienced practitioners), fitting a model with multiple inputs can lead to confusion and errors.

Let’s take a look at a specific scenario where someone attempted to create a Keras model with two inputs but ran into problems during the training phase.

The Problem

The model was set up successfully with two inputs, both of which were defined with the following code:

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

However, when they tried to fit the model with inputs, they encountered multiple errors including ValueError. The confusion ultimately stemmed from the way the data was structured when passed to the fit method.

The Solution

To successfully run the fit method for a Keras model with multiple inputs, you need to ensure that the input data is adequately formatted. Below are the steps you should follow:

1. Prepare Your Input Data

Each input must be structured as a two-dimensional NumPy array with the shape (batch_size, number_of_features). In this case, since each input has one feature, the input arrays for both input1 and input2 should have the shape (batch_size, 1).

For example, here’s how you can do it correctly:

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

2. Use the Right Data Format in fit

Once you have your input data prepared, you can fit the model using the following code:

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

3. Common Mistakes to Avoid

Mismatched Sizes: Ensure that all input arrays have the same number of samples. If input_1 has 32 samples and input_2 has 32 samples, then your output array (outs) must also have 32 samples.

Confusion with Input Structure: Remember that for multiple inputs, you must pass a list of inputs to the fit method, rather than a single array or a tuple.

Conclusion

By adhering to proper data shapes and formats, you can successfully fit a Keras model with multiple inputs. Paying close attention to how data is structured not only avoids common errors, but also streamlines the training process.

Now, you can confidently tackle models with multiple inputs and leverage the power they offer in training robust machine learning solutions. Happy modeling!