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Current implementation of convolutional 2D layer assumes (1) no paddings (2) no dilations (3) zero biases.
To add these features, we need:
parameters corresponding to those features in the Layer2D definition: https://github.com/cog-imperial/OMLT/blob/main/src/omlt/neuralnet/layer.py
a bias term in the Layer2D constraints: https://github.com/cog-imperial/OMLT/blob/main/src/omlt/neuralnet/layers/full_space.py
By generalizing function kernel_index_with_input_indexes in line 258 of https://github.com/cog-imperial/OMLT/blob/main/src/omlt/neuralnet/layer.py, it's possible to support more pooling and convolutional layers (e.g., 1D, 3D).
The text was updated successfully, but these errors were encountered:
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Current implementation of convolutional 2D layer assumes (1) no paddings (2) no dilations (3) zero biases.
To add these features, we need:
parameters corresponding to those features in the Layer2D definition: https://github.com/cog-imperial/OMLT/blob/main/src/omlt/neuralnet/layer.py
a bias term in the Layer2D constraints: https://github.com/cog-imperial/OMLT/blob/main/src/omlt/neuralnet/layers/full_space.py
By generalizing function kernel_index_with_input_indexes in line 258 of https://github.com/cog-imperial/OMLT/blob/main/src/omlt/neuralnet/layer.py, it's possible to support more pooling and convolutional layers (e.g., 1D, 3D).
The text was updated successfully, but these errors were encountered: