Скачать или смотреть Solving the Error when checking model input in Siamese Networks with Customized Data Generators

Discover how to effectively modify your data generator to resolve input size errors when working with Siamese networks in Keras.
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This video is based on the question https://stackoverflow.com/q/63089452/ asked by the user 'Luca Vezzani' ( https://stackoverflow.com/u/9596957/ ) and on the answer https://stackoverflow.com/a/63089751/ provided by the user 'Marco Cerliani' ( https://stackoverflow.com/u/10375049/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Introduction

Are you experiencing difficulties while implementing a Siamese network for your image recognition project? Specifically, are you encountering an error that states, "Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected"? If so, you're not alone. Many beginners face similar issues, particularly when customizing their data generators.

In this guide, we’ll dive into the root of this error and provide a comprehensive solution to ensure that your Siamese network functions correctly. Let’s break down the problem and its solution step-by-step.

Understanding the Error

When using a Siamese network, you're typically expected to pass two input images to the model at once, as the architecture is designed to process pairs of images. The error arises because the data generator is returning a structure that does not meet these requirements.

Error Breakdown:

Expected Input: The model expects a list containing two arrays.

Received Input: Instead, your generator returns a single array, leading to the compatibility issue.

The Solution

The key to resolving this error lies in how your data generator returns the input values. Let’s explore how to modify your data generator properly.

1. Modifying the Data Generator

To fix the issue, you need to adjust the return statement of your data generator. Instead of returning a single array, you should return a list containing two arrays that correspond to the two input images for your Siamese network:

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

Explanation of the Code:

X[:, 0, ...]: This fetches all the samples of the first images from the pairs (image_a).

X[:, 1, ...]: This fetches all the samples of the second images from the pairs (image_b).

y: This remains unchanged and is used as the output labels.

2. Ensuring Data Integrity

When working with pairs of images, it’s essential to ensure that both the input constructs (Pair_equal and Pair_diff) and the corresponding labels (y_equal and y_diff) are handled correctly. As seen in your setup, it’s crucial to check the length of your pairs and synchronize them accordingly.

3. Verifying the Shapes

Before concluding, ensure that the shapes of your input arrays conform to your model's requirements. For your setup, you should have:

Pair_equal: shape of (16, 2, 200, 200, 1)

Pair_diff: shape of (16, 2, 200, 200, 1)

y_equal and y_diff: both should have the shape of (16,)

This ensures that you have an aligned array for your inputs and corresponding outputs.

Conclusion

Implementing a Siamese network can be challenging, especially when customizing your data generators. However, by adjusting the return structure of your data generator to return separate arrays for each input, you can effectively solve the Error when checking model input. Always ensure your shapes match the model’s requirements and that the data is correctly structured to facilitate smooth training and evaluation.

If you encounter any further issues, don’t hesitate to share your setup or ask for clarification—there’s a robust community ready to assist you in your machine learning journey.