neurons

32 releases (21 stable)

new 2.6.1	Nov 19, 2024
2.5.4	Oct 25, 2024
1.1.0	Aug 28, 2024
0.3.0	Aug 24, 2024
0.1.5	Jul 3, 2024

#44 in Machine learning

423 downloads per month

GPL-3.0 license

395KB
8K SLoC

neurons 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.

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.

Feedback loops and -blocks for more advanced architectures.

Skip connections with simple accumulation specification.

Much more!

Fast

Leveraging Rust's performance and parallelization capabilities.

Everything built from scratch

Only dependencies are rayon and plotters.
Where plotters only is used through some of the examples (thus optional).

Various examples showcasing the capabilities

Located in the examples/ directory. With subdirectories for various tasks, showcasing the different architectures and techniques.

The package

The package is divided into separate modules, each containing different parts of the library, everything being connected through the network.rs module.

Core

tensor.rs

Describes the custom tensor struct and its operations.
A tensor is here divided into four main 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<_>>>>).

And further into two additional helper-types:

Quintuple: Five-dimensional data (Vec<Vec<Vec<Vec<Vec<(usize, usize)>>>>>). Used to hold maxpool indices.

Nested: A nested tensor (Vec<Tensor>). Used through feedback blocks.

Each shape following the same pattern of operations, but with increasing dimensions.
Thus, every tensor contains information about its shape and data.
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.
Used when initializing the weights of the network.

network.rs

Describes the network struct and its operations.
The network contains a vector of layers, an optimizer, and an objective function.
The network is built layer by layer, and then trained using the learn function.
See 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.
If the input is a tensor of shape Single, the layer will automatically reshape it into a Triple tensor.

deconvolution.rs

Describes the deconvolutional layer and its operations.
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.
If the input is a tensor of shape Single, the layer will automatically reshape it into a Triple tensor.

feedback.rs

Describe the feedback block and its operations.

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.

Examples

plot.rs

Contains the plotting functionality for the examples.

Quickstart

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));

  // Convolution(filters, kernel, stride, padding, dilation, activation, Some(dropout))
  network.convolution(5, (3, 3), (1, 1), (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, val_acc) = network.learn(x, y, validation, batch, epochs, print);
}

Releases

v2.6.1 – Bugs and visualisation.

Fix bugs concerning expanded feedback approach 1.
Improved results visualisation.

v2.6.0 – Feedback approach 1.

Improved implementation of approach 1 feedback connections.
Add possibility of selecting iteration count.
Improve averaging; now takes a vector of tensors.

v2.5.5 – Comparison and timing.

Added timing functionality.
Improve comparison functionality.

v2.5.4 – Bugs and examples.

Fix a typo in Adam and AdamW optimizers.
Updated examples.
Updated comparisons.

v2.5.3 – Architecture comparison.

Added examples comparing the performance off different architectures.

Probes the final network by turning of skips and feedbacks, etc.

examples/compare/*

Corresponding plotting functionality.

documentation/comparison.py

v2.5.2 – Bug extermination and expanded examples.

Fix bug related to skip connections.
Fix bug related to validation forward pass.
Expanded examples.
Improve feedback block.

v2.5.1 – Convolution dilation.

Add dilation to the convolution layer.

v2.5.0 – Deconvolution layer.

Initial implementation of the deconvolution layer.

Created with the good help of the GitHub Copilot.

Validated against corresponding PyTorch implementation;

documentation/validation/deconvolution.py

v2.4.1 – Bug-fixes.

Minor bug-fixes and example expansion.

v2.4.0 – Feedback blocks.

Thorough expansion of the feedback module.

Feedback blocks automatically handle weight coupling and skip connections.

When defining a feedback block in the network's layers, the following syntax is used:

network.feedback(
    vec![feedback::Layer::Convolution(
        1,
        activation::Activation::ReLU,
        (3, 3),
        (1, 1),
        (1, 1),
        None,
    )],
    2,
    true,
    feedback::Accumulation::Mean,
);

v2.3.0 – Skip connection.

Add possibility of skip connections.

Limitations:

Only works between equal shapes.
Backward pass assumes an identity mapping (gradients are simply added).

v2.2.0 – Selectable scaling wrt. loopbacks.

Add possibility of selecting the scaling function.

tensor::Scale
feedback::Accumulation See implementations of the above for more information.

v2.1.0 – Maxpool tensor consistency.

Update maxpool logic to ensure consistency wrt. other layers.

Maxpool layers now return a tensor::Tensor (of shape tensor::Shape::Quintuple), instead of nested Vecs.

This will lead to consistency when implementing maxpool for feedback blocks.

v2.0.5 – Bug fixes and renaming.

Minor bug fixes to feedback connections. Rename simple feedback connections to loopback connections for consistency.

v2.0.4 – Initial feedback block structure.

Add skeleton for feedback block structure. Missing correct handling of backward pass.

How should the optimizer be handled (wrt. buffer, etc.)?

v2.0.3 - Improved optimizer creation.

Before:

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:

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
));

v2.0.2 – Improved compatability of differing layers.

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.

v2.0.1 – Optimized optimizer step.

Combines operations to single-loop instead of repeadedly iterating over the tensor::Tensor's.

Benchmarking benches/benchmark.rs (mnist version):

v2.0.1: 16.504570304s (1.05x speedup) v2.0.0: 17.268632412s

v2.0.0 – Fix batched weight updates.

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

v1.1.0 – Improved optimizer step.

Optimizer step more intuitive and easy to read. Using tensor::Tensor instead of manually handing vectors.

v1.0.0 – Fully working integrated network.

Network of convolutional and dense layers works.

v0.3.0 – Batched training; parallelization.

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

v0.2.2 – Convolution.

Convolutional layer. Improved documentation.

v0.2.0 – Feedback.

Initial feedback connection implementation.

v0.1.5 – Improved documentation.

Improved documentation.

v0.1.1 – Custom tensor struct.

Custom tensor struct. Unit tests.

v0.1.0 – Dense.

Dense feedforward network. Activation functions. Objective functions. Optimization techniques.

Progress

Layer types

Dense
Convolution
- Forward pass
  - Padding
  - Stride
  - Dilation
- Backward pass
  - Padding
  - Stride
  - Dilation
Deconvolution (#22)
- Forward pass
  - Padding
  - Stride
  - Dilation
- Backward pass
  - Padding
  - Stride
  - Dilation
Max pooling
Feedback

Activation functions

Linear
Sigmoid
Tanh
ReLU
LeakyReLU
Softmax

Objective functions

AE
MAE
MSE
RMSE
CrossEntropy
BinaryCrossEntropy
KLDivergence

Optimization techniques

SGD
SGDM
Adam
AdamW
RMSprop
Minibatch

Architecture

Feedforward (dubbed Network)
Feedback loops
Skip connections
Feedback blocks
Recurrent

Feedback

Feedback connection
Selectable gradient scaling
Selectable gradient accumulation
Feedback block
- Selectable weight coupling

Regularization

Dropout
Early stopping
Batch normalization

Parallelization

Parallelization of batches
Other parallelization?
- NOTE: Slowdown when parallelizing everything (commit: 1f94cea56630a46d40755af5da20714bc0357146).

Testing

Unit tests
- Thorough testing of activation functions
- Thorough testing of objective functions
- Thorough testing of optimization techniques
- Thorough testing of feedback blocks
Integration tests
- Network forward pass
- Network backward pass
- Network training (i.e., weight updates)

Examples

XOR
Iris
FTIR
- MLP
  - Plain
  - Skip
  - Looping
  - Feedback
- CNN
  - Plain
  - Skip
  - Looping
  - Feedback
MNIST
- CNN
- CNN + Skip
- CNN + Looping
- CNN + Feedback
Fashion-MNIST
- CNN
- CNN + Skip
- CNN + Looping
- CNN + Feedback
CIFAR-10
- CNN
- CNN + Skip
- CNN + Looping
- CNN + Feedback

Other

Documentation
Custom random weight initialization
Custom tensor type
Plotting
Data from file
- General data loading functionality
Custom icon/image for documentation
Custom stylesheet for documentation
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

neurons

32 releases (21 stable)

Jump to

Features

Modular design

Fast

Everything built from scratch

Various examples showcasing the capabilities

The package

Core

tensor.rs

random.rs

network.rs

Layers

dense.rs

convolution.rs

deconvolution.rs

maxpool.rs

feedback.rs

Functions

activation.rs

objective.rs

optimizer.rs

Examples

plot.rs

Quickstart

Releases

Progress

Resources

Sources

Tools used

Inspiration

Dependencies