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<br>
<b>neurons</b> is a neural network library written from scratch in Rust. It provides a flexible and efficient way to build, train, and evaluate neural networks. The library is designed to be modular, allowing for easy customization of network architectures, activation functions, objective functions, and optimization techniques.
#### Jump to
* [Features](#features)
* [Quickstart](#quickstart)
* [The package](#the-package)
* [Releases](#releases)
* [Progress](#progress)
* [Resources](#resources)
## Features
>
> ### Modular design
> * Ready-to-use dense, convolutional and maxpool layers.
> * Inferred input shapes when adding layers.
> * Easily specify activation functions, biases, and dropout.
> * Customizable objective functions and optimization techniques.
>
> ### Fast
> * Leveraging Rust's performance and parallelization capabilities.
>
> ### <i>Everything</i> built from scratch
> * Only dependencies are `rayon` and `plotters`.<br>
> Where `plotters` only is used through some of the examples (thus optional).
>
> ### Various examples showcasing the capabilities
> * Located in the `examples/` directory.
## The package
The package is divided into separate modules, each containing different parts of the library, everything being connected through the <b>network.rs</b> module.
### Core
>
> #### tensor.rs
> > Describes the custom tensor struct and its operations.<br>
> > A tensor is here divided into four different types:
> > * `Single`: One-dimensional data (`Vec<_>`).
> > * `Double`: Two-dimensional data (`Vec<Vec<_>>`).
> > * `Triple`: Three-dimensional data (`Vec<Vec<Vec<_>>>`).
> > * `Quadruple`: Four-dimensional data (`Vec<Vec<Vec<Vec<_>>>>`).
> >
> > Each shape following the same pattern of operations, but with increasing dimensions.<br>
> > Thus, every tensor contains information about its shape and data.<br>
> > The reason for wrapping the data in this way is to easily allow for dynamic shapes and types in the network.
>
> #### random.rs
> > Functionality for random number generation.<br>
> > Used when initializing the weights of the network.
>
> #### network.rs
> > Describes the network struct and its operations.<br>
> > The network contains a vector of layers, an optimizer, and an objective function.<br>
> > The network is built layer by layer, and then trained using the `learn` function.<br>
> > See [quickstart](#quickstart) or the `examples/` directory for more information.
### Layers
>
> #### dense.rs
> > Describes the dense layer and its operations.
>
> #### convolution.rs
> > Describes the convolutional layer and its operations.<br>
> > If the input is a tensor of shape `Single`, the layer will automatically reshape it into a `Triple` tensor.
>
> #### maxpool.rs
> > Describes the maxpool layer and its operations.<br>
> > If the input is a tensor of shape `Single`, the layer will automatically reshape it into a `Triple` tensor.
### Functions
>
> #### activation.rs
> > Contains all the possible activation functions to be used.
>
> #### objective.rs
> > Contains all the possible objective functions to be used.
>
> #### optimizer.rs
> > Contains all the possible optimization techniques to be used.
## Quickstart
```rust
use neurons::{activation, network, objective, optimizer, tensor};
fn main() {
// New feedforward network with input shape (1, 28, 28)
let mut network = network::Network::new(
tensor::Shape::Triple(1, 28, 28),
feedback::Accumulation::Add
);
// Convolution(filters, kernel, stride, padding, activation, Some(dropout))
network.convolution(5, (3, 3), (1, 1), (1, 1), activation::Activation::ReLU, None);
// Maxpool(kernel, stride)
network.maxpool((2, 2), (2, 2));
// Dense(outputs, activation, bias, Some(dropout))
network.dense(100, activation::Activation::ReLU, false, None);
// Dense(outputs, activation, bias, Some(dropout))
network.dense(10, activation::Activation::Softmax, false, None);
network.set_optimizer(optimizer::RMSprop::create(
0.001, // Learning rate
0.0, // Alpha
1e-8, // Epsilon
Some(0.01), // Decay
Some(0.01), // Momentum
true, // Centered
));
network.set_objective(
objective::Objective::MSE, // Objective function
Some((-1f32, 1f32)) // Gradient clipping
);
println!("{}", network); // Display the network
let (x, y) = { }; // Add your data here
let validation = (
x_val, // Validation data
y_val, // Validation labels
5 // Stop if val loss decreases for 5 epochs
);
let batch = 32; // Minibatch size
let epochs = 100; // Number of epochs
let print = Some(10); // Print every 10th epoch
let (train_loss, val_loss) = network.learn(x, y, validation, batch, epochs, print);
}
```
## Releases
<details>
<summary>v2.2.0 – Selectable scaling wrt. loopbacks.</summary>
Add possibility of selecting the scaling function.
* `tensor::Scale`
* `feedback::Accumulation`
See implementations of the above for more information.
</details>
<details>
<summary>v2.1.0 – Maxpool tensor consistency.</summary>
Update maxpool logic to ensure consistency wrt. other layers.
Maxpool layers now return a `tensor::Tensor` (of shape `tensor::Shape::Quintuple`), instead of nested `Vec`s.
This will lead to consistency when implementing maxpool for `feedback` blocks.
</details>
<details>
<summary>v2.0.5 – Bug fixes and renaming.</summary>
Minor bug fixes to feedback connections.
Rename simple feedback connections to `loopback` connections for consistency.
</details>
<details>
<summary>v2.0.4 – Initial feedback block structure.</summary>
Add skeleton for feedback block structure.
Missing correct handling of backward pass.
How should the optimizer be handled (wrt. buffer, etc.)?
</details>
<details>
<summary>v2.0.3 - Improved optimizer creation.</summary>
Before:
```rs
network.set_optimizer(
optimizer::Optimizer::AdamW(
optimizer::AdamW {
learning_rate: 0.001,
beta1: 0.9,
beta2: 0.999,
epsilon: 1e-8,
decay: 0.01,
// To be filled by the network:
momentum: vec![],
velocity: vec![],
}
)
);
```
Now:
```rs
network.set_optimizer(optimizer::RMSprop::create(
0.001, // Learning rate
0.0, // Alpha
1e-8, // Epsilon
Some(0.01), // Decay
Some(0.01), // Momentum
true, // Centered
));
```
</details>
<details>
<summary>v2.0.2 – Improved compatability of differing layers.</summary>
Layers now automatically reshape input tensors to the correct shape.
I.e., your network could be conv->dense->conv etc.
Earlier versions only allowed conv/maxpool->dense connections.
Note: While this is now possible, some testing proved this to be suboptimal in terms of performance.
</details>
<details>
<summary>v2.0.1 – Optimized optimizer step.</summary>
Combines operations to single-loop instead of repeadedly iterating over the `tensor::Tensor`'s.
Benchmarking `examples/example_benchmark.rs` (mnist version):
v2.0.1: 16.504570304s (1.05x speedup)
v2.0.0: 17.268632412s
</details>
<details>
<summary>v2.0.0 – Fix batched weight updates.</summary>
Weight updates are now batched correctly.
See `network::Network::learn` for details.
Benchmarking examples/example_benchmark.rs (mnist version):
batched (128): 17.268632412s (4.82x speedup)
unbatched (1): 83.347593292s
</details>
<details>
<summary>v1.1.0 – Improved optimizer step.</summary>
Optimizer step more intuitive and easy to read.
Using `tensor::Tensor` instead of manually handing vectors.
</details>
<details>
<summary>v1.0.0 – Fully working integrated network.</summary>
Network of convolutional and dense layers works.
</details>
<details>
<summary>v0.3.0 – Batched training; parallelization.</summary>
Batched training (`network::Network::learn`).
Parallelization of batches (`rayon`).
Benchmarking `examples/example_benchmark.rs` (iris version):
v0.3.0: 0.318811179s (6.95x speedup)
v0.2.2: 2.218362758s
</details>
<details>
<summary>v0.2.2 – Convolution.</summary>
Convolutional layer.
Improved documentation.
</details>
<details>
<summary>v0.2.0 – Feedback.</summary>
Initial feedback connection implementation.
</details>
<details>
<summary>v0.1.5 – Improved documentation.</summary>
Improved documentation.
</details>
<details>
<summary>v0.1.1 – Custom tensor struct.</summary>
Custom tensor struct.
Unit tests.
</details>
<details>
<summary>v0.1.0 – Dense.</summary>
Dense feedforward network.
Activation functions.
Objective functions.
Optimization techniques.
</details>
## Progress
<details>
<summary>Layer types</summary>
- [x] Dense
- [x] Convolutional
- [x] Forward pass
- [x] Padding
- [x] Stride
- [ ] Dilation
- [x] Backward pass
- [x] Padding
- [x] Stride
- [ ] Dilation
- [x] Max pooling
- [ ] Feedback
</details>
<details>
<summary>Activation functions</summary>
- [x] Linear
- [x] Sigmoid
- [x] Tanh
- [x] ReLU
- [x] LeakyReLU
- [x] Softmax
</details>
<details>
<summary>Objective functions</summary>
- [x] AE
- [x] MAE
- [x] MSE
- [x] RMSE
- [x] CrossEntropy
- [x] BinaryCrossEntropy
- [x] KLDivergence
</details>
<details>
<summary>Optimization techniques</summary>
- [x] SGD
- [x] SGDM
- [x] Adam
- [x] AdamW
- [x] RMSprop
- [x] Minibatch
</details>
<details>
<summary>Architecture</summary>
- [x] Feedforward (dubbed `Network`)
- [x] Feedback loops
- [ ] Recurrent
- [ ] Skip connections
- [ ] Feedback blocks
</details>
<details>
<summary>Feedback</summary>
- [x] Feedback connection
- [x] Selectable gradient scaling
- [ ] Selectable gradient accumulation
- [ ] Feedback block
</details>
<details>
<summary>Regularization</summary>
- [x] Dropout
- [x] Early stopping
- [ ] Batch normalization
</details>
<details>
<summary>Parallelization</summary>
- [x] Parallelization of batches
- [ ] Other parallelization?
- NOTE: Slowdown when parallelizing _everything_ (commit: 1f94cea56630a46d40755af5da20714bc0357146).
</details>
<details>
<summary>Testing</summary>
- [x] Unit tests
- [x] Thorough testing of activation functions
- [x] Thorough testing of objective functions
- [x] Thorough testing of optimization techniques
- [ ] Thorough testing of feedback blocks
- [x] Integration tests
- [x] Network forward pass
- [x] Network backward pass
- [x] Network training (i.e., weight updates)
</details>
<details>
<summary>Examples</summary>
- [x] XOR
- [x] Iris
- [x] MLP
- [ ] MLP + Looping
- [ ] MLP + Feedback
- [ ] Linear regression
- [ ] MLP
- [ ] MLP + Looping
- [ ] MLP + Feedback
- [ ] Classification TBA.
- [ ] MLP
- [ ] MLP + Looping
- [ ] MLP + Feedback
- [ ] MNIST
- [ ] MLP
- [ ] MLP + Looping
- [ ] MLP + Feedback
- [x] CNN
- [x] CNN + Looping
- [ ] CNN + Feedback
- [ ] CIFAR-10
- [x] CNN
- [ ] CNN + Looping
- [ ] CNN + Feedback
</details>
<details>
<summary>Other</summary>
- [x] Documentation
- [x] Custom random weight initialization
- [x] Custom tensor type
- [x] Plotting
- [x] Data from file
- [ ] General data loading functionality
- [x] Custom icon/image for documentation
- [x] Custom stylesheet for documentation
- [x] Add number of parameters when displaying `Network`
- [ ] Network type specification (e.g. f32, f64)
- [ ] Serialisation (saving and loading)
- [ ] Single layer weights
- [ ] Entire network weights
- [ ] Custom (binary) file format, with header explaining contents
- [ ] Logging
</details>
## Resources
### Sources
* [backpropagation](https://towardsdatascience.com/backpropagation-from-scratch-how-neural-networks-really-work-36ee4af202bf)
* [softmax](https://e2eml.school/softmax)
* [momentum](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html)
* [Adam](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html)
* [AdamW](https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html)
* [RMSprop](https://pytorch.org/docs/stable/generated/torch.optim.RMSprop.html)
* [convolution 1](https://deeplearning.cs.cmu.edu/F21/document/recitation/Recitation5/CNN_Backprop_Recitation_5_F21.pdf)
* [convolution 2](https://www.jefkine.com/general/2016/09/05/backpropagation-in-convolutional-neural-networks/)
* [convolution 3](https://sites.cc.gatech.edu/classes/AY2021/cs7643_spring/assets/L11_CNNs.pdf)
#### Tools used
* [GitHub Copilot](https://github.com/features/copilot)
* [ChatGPT](https://chatgpt.com)
* [Mistral](https://chat.mistral.ai/chat)
* [Claude](https://claude.ai)
### Inspiration
* [candle](https://github.com/huggingface/candle/tree/main)
* [rust-simple-nn](https://github.com/danhper/rust-simple-nn/tree/master)