neurons 2.0.4

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.

Jump to

• Features
• Quickstart
• The package
• Releases
• Progress
• 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.

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.

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 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. 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.

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.

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

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, 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

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

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

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
));

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.

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

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

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

Network of convolutional and dense layers works.

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

Convolutional layer.
Improved documentation.

Initial feedback connection implementation.

Improved documentation.

Custom tensor struct.
Unit tests.

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

Progress

• Dense
• Convolutional
  • Forward pass
    • Padding
    • Stride
    • Dilation
  • Backward pass
    • Padding
    • Stride
    • Dilation
  • Max pooling
  • Feedback

• Linear
• Sigmoid
• Tanh
• ReLU
• LeakyReLU
• Softmax

• AE
• MAE
• MSE
• RMSE
• CrossEntropy
• BinaryCrossEntropy
• KLDivergence

• SGD
• SGDM
• Adam
• AdamW
• RMSprop
• Minibatch

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

• Selectable gradient accumulation
• Handle backward pass of feedback block in network::Network

• Dropout
• Early stopping
• Batch normalization

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

• 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)

• XOR
• Iris
  • MLP
  • MLP + Feedback
• Linear regression
  • MLP
  • MLP + Feedback
• Classification TBA.
  • MLP
  • MLP + Feedback
• MNIST
  • MLP
  • MLP + Feedback
  • CNN
  • CNN + Feedback
• CIFAR-10
  • CNN
  • CNN + Feedback

• 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

Resources

Sources

• backpropagation
• softmax
• momentum
• Adam
• AdamW
• RMSprop
• convolution 1
• convolution 2
• convolution 3

Tools used

• GitHub Copilot
• ChatGPT
• Mistral
• Claude

Inspiration

• candle
• rust-simple-nn