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0.4.0
Constelation Sequence model support, with jupyter notebook example, although based in sensitive data. This release leads up to but does not include the full Fashion-MNIST example with classification networks. This release holds many of the required changes for both forward and backrpop with a slightly modified decryption method as an explicit node.
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0.3.1
Full forward bugfixes Forward pass and certain nodes had some bugfixes when utilised together. For instance Loss functions will now need a signal input OR y_hat + y
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0.3.0
Complete Initial Selection of Nodes Iteration: - Additional neuron nodes such that a complete MNIST graph is now possible - Added unittests for all new and any residual nodes that were not yet tested - Added experimental custom marshmallow field to serialise and deserialise numpy attributes - Expanded on MNIST example and created a new interactive pyvis graph for it - Expanded documentation in several places as well as adding a whole new category for traversers since there will likeley be various algorithms toward stimulating and harvesting the neurons activations.
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0.2.1
CNN as CC operations CNN has been re-added but split as individual operations that can be chained together. This marks the completion of most individual nodes. Now we just need to work on the network traversal to handle some of our newer but more complex cases.
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0.2.0
Overhauled nodes and networks Nodes now take centre stage, in a network abstraction. Inheritance and complexity has been slashed. Now we have a very simple architecture while still having minimal boilerplate/ redundant code. This is completeley incompatible with 0.1.1 but this is allowed for rapid development untill version 1.0
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0.1.1
Dark Docs ========= Documentation is now eye-friendly!
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0.1.0
Original ReArray This current version represents the nodeless implementation where FHE was used in nodeless CNN ANN and activation functions. The FHE implementation is based on inbuilt MS-SEAL, abstracted as rearray. The intention is to in future split FHE backends into seperate modules/ plugins, and from now on all layers, activations etc will be nodes in our custom graphs.