Neural Networks in Rust, without backpropagation. WIP

Last update: Apr 10, 2022

Overview

Deep Thought

As of right now, this crate is far from a usable state. This crate implements feedforward-neural Networks in rust. Unlike the vast majority of NN libraries, like pytorch or tensorflow, deep thought does not use backpropagation to calculate gradients. Instead, it utilizes Dual Numbers, which allow for the calculation of derivatives during the forward pass.

There is still a long way to go before this crate will become usable (if ever).

For more information about Dual Numbers, please take a look at these excellent Blogposts by @Atrix256:

If you want to learn more about the math behind neural networks, take a look at these links:

"How the backpropagation algorithm works" by Michael Nielsen

Nightly Usage

Deep_thought makes use of the negative_impls, auto_traits and array_zip features, which are not available on the stable release channel yet.

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Comments

Roadmap
[x] Add a working implementation for basic dual number operations (Add, AddAssign etc)

[x] Allow interfacing with regular numbers implementing num_traits::Float

[ ] Use standard floating point types for weights/biases, these don't require gradients. see https://github.com/rust-ndarray/ndarray/issues/1067

[ ] Implement SGD using Dual Numbers

[ ] Test Performance + Memory usage. Dual Numbers might be really bad
opened by Wuelle 2

Releases(v0.1.2)

v0.1.2(Jun 15, 2021)
Changes in this release:

renamed NeuralNetworkBuilder to NeuralNetwork

Activation function derivatives return a 3D Array instead of a 2D array. The addition of the Softmax function made this necessary since one input value influences all output values

Additions in this release:

Added the Softmax activation function (stabilized)

Added the tanh activation function

Added the option to create a Dataset without normalizing the data (using Dataset::raw())

Added a new trait called Optimizer. Custom optimizers that implement this trait can be used to optimize the network, which means that SGD is no longer the only available optimizer. This also helps keep the code of NeuralNetwork clean as it was previously kinda mixed together with optimizer code

Source code(tar.gz)
Source code(zip)
v0.1.1(Jun 11, 2021)

This release introduces the serde feature flag, which implements the Serialize and ``Deserialize` trait for all necessary structs/enums. It also adds a lot more documentation, mainly to the crate root and the README
Source code(tar.gz)
Source code(zip)
v0.1.0(Jun 10, 2021)

First version pushed to crates.io. Contains basic feedforward nn implementation.
Source code(tar.gz)
Source code(zip)

Owner

Student

GitHub https://crates.io/crates/deep_thought

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