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This repository has been archived by the owner on Apr 4, 2024. It is now read-only.

Releases: Benny-Nottonson/voodoo

v0.7.9-alpha

04 Feb 00:57
d0b6797
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What's Changed

  • Started the migration to seperate initialization kernels, later to be combined into general kernels. by @Benny-Nottonson in #46
  • Deprecated Layers while API Improved, Initialization Struct by @Benny-Nottonson in #47

Full Changelog: v0.7.8-alpha...v0.7.9-alpha

v0.7.8-alpha

01 Feb 17:08
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Refactor and change to Vector struct, Graph and Node changes, Lifetime and Pointer changes. Memory pool and graph fuse optimized.

Full Changelog: v0.7.7-alpha...v0.7.8-alpha

v0.7.7-alpha

30 Jan 02:55
0b18c24
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Modified Node, Tensor, and Graph struct for simplicity and speed, modified MatMul implementation for speed with backpropagation. Overall bug fixes.

What's Changed

Full Changelog: v0.7.6-alpha...v0.7.7-alpha

v0.7.6-alpha

28 Jan 20:09
5ee765c
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Removed Pointers from Node and Graph where possible, improved memory access and allocation.

What's Changed

  • Rewrote Node, Graph, and Tensor for memory safety and optimization, slightly changed other functions by @Benny-Nottonson in #44

Full Changelog: v0.7.5b-alpha...v0.7.6-alpha

v0.7.5b-alpha

27 Jan 19:45
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Fixed a segfault issue in the previous release.

v0.7.5-alpha

27 Jan 18:50
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Entirely restructured Graph, Node, Tensor, and Vector types to better utilizer Pointers / References (Preparing for Lifetime support from Mojo). Small speedups and bug fixes. Small overall logic improvements and algorithmic changes.

Full Changelog: v0.7.0-alpha...v0.7.5-alpha

v0.7.0-alpha

25 Jan 22:55
5a8fd91
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Updated for Mojo v0.7, from now on release tags will try and follow the Mojo release tags.

v0.6.1-alpha

23 Jan 01:44
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Improvements on Convolution / Pooling layers using tiling. General speed improvements and optimizations with vectorize_unroll and other related functions. Benchmark added to compare speed to similar projects, MNIST dataset / test updated & scaled out.

v0.6.0-alpha

19 Jan 23:53
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Convolution / Pooling (1D and 2D) added and tested with MNIST dataset, best so far 29% loss with 1440 labeled images. Still necessary to optimize convolution and pooling more.

What's Changed

Full Changelog: v0.5.0-alpha...v0.6.0-alpha

v0.5.0-alpha

15 Jan 20:56
4be559b
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Rewrote Matmul resulting in a >300% speedup from earlier versions, general code improvements.