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| | license: mit |
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| | # It works BEAUTIFULLY |
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| | HOLY **** this 500k param stack of linear layers hit 79% val. I mean it's just a linear head but still, it works really well and with only a fraction of the active neurons. |
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| | I posted an updated train feed because I never expected it to work so well. |
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| | # Prelim |
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| | This is a prototype linear stack meant to benchmark the newest CantorLinear layer implementation. |
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| | Preliminary MNIST trains show that CantorLinear embedded cantor-fingerprinted direct neuron learning mask with alpha weights; |
| | * increases accuracy by +-4% with commonly +2% over standard linear layers and reduces train time by about 4% worst -4% speed. |
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| | Preliminary MNIST trains show that CantorConv with cantor-fingerprinted learning mask |
| | * Accuracy is nearly identical to traditional +-2% either way depending on noise |
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| | I will be testing cantor resnet by feeding it imagenet orderly features from my repos to see how it fares. |
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| | https://github.com/AbstractEyes/lattice_vocabulary/blob/master/src/geovocab2/train/model/layers/linear.py |
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| | While testing this stack I have a prototype for a second cantor linear layer I'll be testing. |
