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Your model architecture looks fine. If this is a binary classification task, you probably need to reshape the output via |
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Dear PyG community,
I am using GATConv for a regression task on cascade graphs to predict final number of activations in cascade graphs. Cascade graphs are made on the same underlying graph to produce a data point such as
Data(x=[4039, 4], edge_index=[2, 176468], y=[1], cascade_name='0')
. Node features are [seed_activation_flag, centrality 1, centrality 2, centrality 3].seed_activation_flag
determines if the node is a seed in a given cascade. As you can see centrality remains the same for all cascade graphs since they are static features. I use 8 layers as the underlying graph has a diameter of 8. I am trying to use a deep NN to predict final activation count. Is this a correct way to perform a graph level regression task?Also, currently my model outputs in the shape
torch.Size([1, 1])
but thedata.y.shape
istorch.Size([1])
. Should I worry about the shape?Beta Was this translation helpful? Give feedback.
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