Resolving GATConv Dimension Errors in Graph Neural Networks

Learn how to resolve dimension errors in GATConv networks by correctly reshaping the data in PyTorch and DGL.
---
This video is based on the question https://stackoverflow.com/q/75899425/ asked by the user 'Branco François' ( https://stackoverflow.com/u/14951866/ ) and on the answer https://stackoverflow.com/a/76099630/ provided by the user 'David Li' ( https://stackoverflow.com/u/9610282/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: GATconv dimensions

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/licensing
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Resolving GATConv Dimension Errors in Graph Neural Networks

When building Graph Convolutional Networks using GATConv (Graph Attention Convolution), encountering dimension errors can be a common obstacle for many developers, especially when transitioning into graph-based deep learning. If you're faced with the RuntimeError: mat1 and mat2 shapes cannot be multiplied (7140x16 and 64x64), you’re not alone. This guide will dissect this issue and provide a clear, structured solution to help you get your model up and running.

Understanding the Problem

The error message indicates that there is a mismatch in the dimensions of the matrices being multiplied in the forward pass of the GraphClassifier class. Specifically, the error arises in the line of code:

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

This suggests that the output after the first convolution layer (conv1) is not in the expected format when being passed to conv2.

Why Dimension Errors Occur

Dimension errors typically arise due to inconsistencies in how data is reshaped throughout the layers of the neural network. In GATConv, the output of a convolution operation has dimensions formatted as:

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

This means that the output of conv1 now has outputs separated across multiple heads, which is essential for the attention mechanism, but requires proper reshaping for subsequent layers.

The Solution: Reshaping Your Data

The solution to resolving this dimension error lies in reshaping the tensor h after it is processed by the first convolution layer. Let’s break it down into actionable steps:

Reshape Output from conv1:
After the output h from conv1, we need to flatten the tensor so that the h can be properly fed into conv2.

Modify the Forward Function:
Update the forward method in the GraphClassifier class where the reshaping occurs.

Here’s the updated code snippet:

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

Explanation of the Code Changes

Reshape Function:

h = h.reshape(h.shape[0], -1): This reshapes h from [node_num, num_heads, hidden_size] to [node_num, num_heads * hidden_size]. The -1 automatically calculates the size based on the number of nodes, making it versatile to changes in input size.

Activation and Dropout:

After reshaping, we immediately apply the ELU activation function and dropout to ensure that subsequent operations benefit from normalized outputs and help in preventing overfitting.

Conclusion

By following these straightforward steps, you can effectively resolve dimension errors encountered when using GATConv in your neural network models. Proper reshaping after each convolution is key to ensuring smooth transitions between layers.

If this guide helped you overcome similar issues, feel free to share your experiences or ask questions in the comments below. Happy coding!

Видео Resolving GATConv Dimension Errors in Graph Neural Networks канала vlogize