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Hi! I've been working with your GCN implementation and noticed a few issues that could affect the model's performance:
Global Mean Pooling issue:
In the current implementation, the tensor is flattened before applying tg.nn.global_mean_pool (in the forward pass). Flattening the tensor collapses the dimensions to: [batch size, resolution(num of regions)*features] so the pooling will be ineffective.
This can be seen also in the input dimension of the first linear layer when creating the gcn model. In your example: (fc1): Linear(in_features=6750, out_features=256, bias=True), where 6750 = 675 regions * 10 features, so no reduction in data is applied between the last Chebconv and the first Linear, so I guess no pooling?
In the fully connected layers (fc1, fc2, fc3), there is no non-linearity introduced between the layers. This reduces the effectiveness of using three layers, as each layer is simply performing a linear transformation without any non-linear interaction. Addition of ReLU between each fully connected layer could make the model more expressive and improve performance.
Hi! I've been working with your GCN implementation and noticed a few issues that could affect the model's performance:
In the current implementation, the tensor is flattened before applying tg.nn.global_mean_pool (in the forward pass). Flattening the tensor collapses the dimensions to: [batch size, resolution(num of regions)*features] so the pooling will be ineffective.
This can be seen also in the input dimension of the first linear layer when creating the gcn model. In your example: (fc1): Linear(in_features=6750, out_features=256, bias=True), where 6750 = 675 regions * 10 features, so no reduction in data is applied between the last Chebconv and the first Linear, so I guess no pooling?
Would love to hear your thoughts and see if these can be addressed. Thanks for the great tutorial!
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