Solving the Pytorch CNN Input Dimensions Not Matching Error

A detailed guide to fix the input dimension error in your Pytorch CNN model, specifically addressing the CNN architecture and the residual blocks.
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Tackling the Pytorch CNN Input Dimensions Not Matching Error

As developers dive into building convolutional neural networks (CNNs) in PyTorch, it's common to encounter input dimension mismatches, particularly with linear layers in your model. This issue often arises when implementing advanced architectures, such as a CNN for an Alpha Zero game player, which can lead to difficulties in matrix multiplication. In this post, we will take a closer look at the issue and provide a step-by-step solution to fix it.

Understanding the Problem

In your current CNN architecture, you're facing a RuntimeError related to matrix multiplication involving the shapes of your input tensors. Here's a simplified version of the error message you encountered:

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

This error indicates that two matrices are not compatible for multiplication because their dimensions do not align properly. Specifically, it relates to how the output from your residual blocks is being processed by linear layers in your model.

Why This Happens

After passing input through the convolutional layers, you end up with a 4D tensor shaped (N, 128, h, w) where:

N is the batch size,

128 is the number of channels (or the number of features),

h and w are the height and width of the feature maps.

However, the expected input for a linear layer in PyTorch must be reshaped to (B, *, H_in) format, where H_in is the input dimension for the linear layer.

Breaking Down the Solution

In order to resolve the input dimension mismatch, you need to ensure that the output of your residual blocks is properly reshaped before feeding it into your linear layers. Here’s how to do this step-by-step:

Modify the Forward Method:
Add a reshaping step immediately before the output heads (the valueHead and policyHead):

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

Fix Input to Linear Layers:
Make sure that the input size for your linear layers matches the flattened size:

Update the valueHead and policyHead to receive a properly reshaped tensor.
Adjust the first linear layer as follows:

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

Here, replace h and w with the actual height and width of your feature maps if applicable.

Final Considerations

With these adjustments, your model should now correctly reshape the output of your residual blocks before it reaches the linear layers, resolving the input dimension mismatch.

Conclusion

Dealing with input dimension issues in CNNs can be tricky, but understanding how PyTorch manages tensor shapes and the linear layer requirements can help streamline your debugging process. By ensuring the outputs from your architecture are properly reshaped, you will be on your way to building a robust Alpha Zero game player without hitches.

Feel free to reach out if you have further questions or if you're experiencing other issues with your CNN architecture!