neural-network-rust-implementation

1. Burn

Modern deep learning framework written fully in Rust.

Features Tensor operations Neural network modules Autograd GPU support (WGPU, CUDA via backends) Training loops Similar feel to PyTorch Best for Full neural network implementation Research projects Modern Rust-native deep learning Example dependencies [dependencies] burn = "0.15" burn-wgpu = "0.15" Good because Most actively growing Rust DL ecosystem Clean API Strong type safety 2. tch-rs

Rust bindings for the PyTorch C++ backend (LibTorch).

Features Uses actual PyTorch engine GPU acceleration Pretrained models Very mature backend Best for Serious deep learning CNNs, Transformers When you already know PyTorch Example [dependencies] tch = "0.16" Pros Fast Stable Production capable Cons Requires LibTorch installation Not pure Rust 3. Candle

Lightweight ML framework from Hugging Face.

Features Tensor operations Transformer inference/training GPU support Efficient inference Best for LLMs Transformers AI inference tools Dependency [dependencies] candle-core = "0.6" candle-nn = "0.6" Pros Fast growing Excellent for transformer models Lower-Level Tensor Libraries

These are useful if you want to implement neural networks manually.

4. ndarray

Rust equivalent of NumPy arrays.

Features N-dimensional arrays Matrix operations Basic linear algebra Dependency [dependencies] ndarray = "0.15" Usually combined with ndarray-rand ndarray-linalg Best for Learning neural networks from scratch Educational implementations 5. nalgebra

Linear algebra library.

Features Vectors Matrices SIMD optimizations Best for Mathematical NN implementation Small networks Dependency [dependencies] nalgebra = "0.32" GPU / Acceleration Libraries 6. wgpu

GPU abstraction layer.

Best for Custom GPU compute Accelerating tensor operations

Usually used internally by frameworks like Burn.

7. cudarc

CUDA bindings for Rust.

Best for NVIDIA GPU acceleration Custom CUDA kernels Data and Utility Libraries 8. rand

Needed for weight initialization.

rand = "0.8" 9. serde

For model saving/loading.

serde = { version = "1", features = ["derive"] } 10. image

For computer vision projects.

image = "0.25" Recommended Stacks If you are learning neural networks from scratch

Use:

ndarray rand

You manually implement:

forward propagation backpropagation gradient descent

This teaches fundamentals best.

If you want production-grade deep learning

Use:

tch

Especially for:

CNNs Transformers Object detection GPU training If you want modern Rust-native DL

Use:

burn

Good balance of:

Rust ergonomics performance ecosystem growth If you want LLMs/Transformers

Use:

candle Example Minimal Neural Network Stack

For a neural network from scratch:

[dependencies] ndarray = "0.15" ndarray-rand = "0.14" rand = "0.8"

For a PyTorch-style workflow:

[dependencies] tch = "0.16"

For modern Rust DL:

[dependencies] burn = "0.15" burn-wgpu = "0.15" Recommendation for You

Since you already work with:

deep learning object detection custom models GPU systems

I would recommend:

Learning implementation details ndarray + rand Real deep learning projects tch-rs Exploring modern Rust ML ecosystem Burn or Candle

tch-rs is currently the most practical for advanced deep learning in Rust because it leverages PyTorch internally.

High-Level Neural Network Flow Input Data ↓ Initialize Weights & Biases ↓ Forward Propagation ↓ Loss Calculation ↓ Backpropagation ↓ Gradient Computation ↓ Weight Updates ↓ Repeat for Epochs ↓ Prediction Step-by-Step Architecture Flow

1. Create Project cargo new neural_net cd neural_net

Add dependencies:

[dependencies] ndarray = "0.15" ndarray-rand = "0.14" rand = "0.8" 2. Define Data Structures

You first define:

weights biases activations layers

Conceptually:

Layer { weights biases }

Neural network:

Network { hidden layers output layer } 3. Initialize Parameters

Randomly initialize:

weight matrices bias vectors

Example dimensions:

Input Layer: 784 Hidden Layer: 128 Output Layer: 10

Then:

W1 = [128 × 784] B1 = [128 × 1]

W2 = [10 × 128] B2 = [10 × 1]

In Rust this usually uses:

Array2 Array1

from ndarray.

4. Forward Propagation

This is the main computation.

Formula:

Z = W·X + B A = activation(Z)

Flow:

Input ↓ Linear Transformation ↓ Activation Function ↓ Next Layer Example Input → Hidden → Output

Hidden layer:

Z1 = W1·X + B1 A1 = ReLU(Z1)

Output layer:

Z2 = W2·A1 + B2 A2 = Softmax(Z2) 5. Activation Functions

You implement functions manually.

Common ones:

ReLU f(x) = max(0, x)

Used in hidden layers.

Sigmoid f(x) = 1 / (1 + e^-x)

Binary classification.

Softmax

Converts outputs into probabilities.

6. Loss Function

Measures prediction error.

Examples:

Mean Squared Error Loss = (prediction - target)^2 Cross Entropy

Used in classification.

7. Backpropagation

Most important part.

Goal:

Find how much each weight caused the error.

Flow:

Output Error ↓ Derivative of Loss ↓ Derivative of Activation ↓ Gradient of Weights ↓ Propagate Backward 8. Compute Gradients

You calculate:

dW dB

for every layer.

This uses:

chain rule matrix multiplication transposes

Example:

dW = dZ · Aᵀ 9. Update Parameters

Gradient descent:

W = W - learning_rate × dW B = B - learning_rate × dB

This is the “learning” step.

10. Training Loop

Entire network repeatedly learns.

for epoch in epochs: forward pass compute loss backward pass update weights 11. Prediction

After training:

Input → Forward Pass → Output Class

Example:

Digit image → "7" Suggested File Structure in Rust src/ │ ├── main.rs ├── network.rs ├── layer.rs ├── activation.rs ├── loss.rs ├── optimizer.rs └── dataset.rs Typical Module Responsibilities layer.rs

Contains:

weights biases forward() backward() activation.rs

Contains:

relu() sigmoid() softmax() derivatives loss.rs

Contains:

mse() cross_entropy() optimizer.rs

Contains:

gradient_descent() adam() Minimal Network Flow Example Input X = [1.2, 0.7] Hidden Layer Z1 = W1X + B1 A1 = ReLU(Z1) Output Layer Z2 = W2A1 + B2 A2 = Sigmoid(Z2) Loss Loss(predicted, actual) Backprop Compute gradients Update Adjust weights Evolution Path Stage 1 — Basic NN

Implement:

dense layers sigmoid MSE gradient descent Stage 2 — Better Training

Add:

ReLU softmax mini-batches Adam optimizer Stage 3 — Deep Learning

Add:

CNN layers dropout normalization Stage 4 — GPU

Move tensor operations to:

CUDA WGPU Burn backend Best Learning Route for You

Since you already work on:

deep learning object detection custom architectures

Best sequence:

First

Build manually using:

ndarray

Learn:

matrix math gradients tensor shapes Then

Move to:

tch-rs

Learn:

autograd GPU tensors model training Then

Explore:

Burn Candle

for advanced Rust-native AI systems.

Realistic Learning Milestone

If you build these manually, you will deeply understand:

PyTorch internals gradient systems tensor computation autodiff engines deep learning frameworks

That knowledge becomes extremely valuable for:

ML systems engineering inference optimization AI infrastructure custom model research Rust AI tooling contributions

Recommended Folder Structure

neural_net/ │ ├── Cargo.toml ├── Cargo.lock │ ├── data/ │ ├── train.csv │ ├── test.csv │ └── mnist/ │ ├── models/ │ ├── saved_weights.bin │ └── checkpoints/ │ ├── src/ │ │ │ ├── main.rs │ │ │ ├── network/ │ │ ├── mod.rs │ │ ├── network.rs │ │ ├── layer.rs │ │ ├── sequential.rs │ │ └── parameters.rs │ │ │ ├── tensor/ │ │ ├── mod.rs │ │ ├── tensor.rs │ │ ├── matrix_ops.rs │ │ └── shape.rs │ │ │ ├── activations/ │ │ ├── mod.rs │ │ ├── relu.rs │ │ ├── sigmoid.rs │ │ ├── softmax.rs │ │ └── tanh.rs │ │ │ ├── loss/ │ │ ├── mod.rs │ │ ├── mse.rs │ │ └── cross_entropy.rs │ │ │ ├── optimizers/ │ │ ├── mod.rs │ │ ├── sgd.rs │ │ └── adam.rs │ │ │ ├── training/ │ │ ├── mod.rs │ │ ├── trainer.rs │ │ ├── backprop.rs │ │ └── metrics.rs │ │ │ ├── dataset/ │ │ ├── mod.rs │ │ ├── loader.rs │ │ ├── preprocessing.rs │ │ └── batching.rs │ │ │ ├── utils/ │ │ ├── mod.rs │ │ ├── random.rs │ │ ├── serialization.rs │ │ └── math.rs │ │ │ └── config/ │ ├── mod.rs │ └── hyperparameters.rs │ ├── tests/ │ ├── tensor_tests.rs │ ├── activation_tests.rs │ ├── gradient_tests.rs │ └── network_tests.rs │ └── examples/ ├── xor.rs ├── mnist.rs └── binary_classifier.rs

Brief Purpose of Each File in the Neural Network Project

---

Root Files

---

Cargo.toml

Project dependencies and metadata.

Contains:

ndarray
rand
serde

---

Cargo.lock

Locks exact dependency versions.

Auto-generated by Cargo.

---

data/

Stores datasets.

---

train.csv

Training data.

---

test.csv

Testing/validation data.

---

mnist/

MNIST digit dataset files/images.

---

models/

Stores trained models.

---

saved_weights.bin

Serialized weights/biases.

---

checkpoints/

Intermediate saved models during training.

Useful if training crashes.

---

src/main.rs

Program entry point.

Responsible for:

• loading dataset
• creating model
• training
• evaluation

Typical flow:

Load Data
→ Build Network
→ Train
→ Test
→ Save Model

---

src/network/

Core neural network structure.

---

network/mod.rs

Exports network modules.

Example:

pub mod layer;
pub mod network;

---

network/network.rs

Defines full neural network.

Contains:

• layers vector
• forward propagation
• prediction logic

Example:

Network {
    layers
}

---

network/layer.rs

Implements dense/fully connected layer.

Responsible for:

• weights
• biases
• layer forward pass
• layer backward pass

Core math:

Z = WX + B

---

network/sequential.rs

Manages ordered layer execution.

Like:

Input → Hidden → Output

---

network/parameters.rs

Stores and updates:

• weights
• biases
• gradients

---

src/tensor/

Low-level tensor/matrix engine.

---

tensor/mod.rs

Exports tensor modules.

---

tensor/tensor.rs

Defines tensor structure.

Could wrap:

Array2<f32>

from ndarray.

---

tensor/matrix_ops.rs

Matrix operations:

• dot product
• transpose
• addition
• multiplication

Most important math file.

---

tensor/shape.rs

Checks tensor dimensions.

Prevents errors like:

128×64 cannot multiply 32×10

---

src/activations/

Activation functions.

---

activations/mod.rs

Exports activations.

---

relu.rs

Implements:

max(0, x)

and derivative.

---

sigmoid.rs

Implements:

1 / (1 + e^-x)

---

softmax.rs

Converts outputs to probabilities.

Used in classification.

---

tanh.rs

Implements tanh activation.

---

src/loss/

Loss functions.

---

loss/mod.rs

Exports loss functions.

---

mse.rs

Mean Squared Error.

Used in regression.

---

cross_entropy.rs

Cross entropy loss.

Used in classification.

---

src/optimizers/

Weight update algorithms.

---

optimizers/mod.rs

Exports optimizers.

---

sgd.rs

Stochastic Gradient Descent.

Updates:

W = W - lr × gradient

---

adam.rs

Advanced optimizer.

Maintains:

• momentum
• adaptive learning rates

---

src/training/

Training engine.

---

training/mod.rs

Exports training modules.

---

trainer.rs

Main training loop.

Handles:

• epochs
• batches
• logging

Core loop:

forward
→ loss
→ backward
→ update

---

backprop.rs

Backpropagation implementation.

Calculates gradients using:

• derivatives
• chain rule

Most mathematically complex file.

---

metrics.rs

Tracks:

• accuracy
• loss
• precision

Used for evaluation.

---

src/dataset/

Data management.

---

dataset/mod.rs

Exports dataset modules.

---

loader.rs

Loads datasets from:

• CSV
• images
• binary files

---

preprocessing.rs

Data cleaning/normalization.

Example:

pixel = pixel / 255.0

---

batching.rs

Creates mini-batches.

Example:

Batch size = 32

---

src/utils/

Shared utilities.

---

utils/mod.rs

Exports utility modules.

---

random.rs

Random initialization.

Implements:

• Xavier initialization
• He initialization

---

serialization.rs

Save/load model weights.

Typically uses:

serde
bincode

---

math.rs

General helper math functions.

Example:

• clipping
• normalization
• stable softmax

---

src/config/

Configuration files.

---

config/mod.rs

Exports config modules.

---

hyperparameters.rs

Stores constants:

LEARNING_RATE
EPOCHS
BATCH_SIZE

---

tests/

Unit/integration tests.

---

tensor_tests.rs

Tests matrix operations.

---

activation_tests.rs

Tests activations.

---

gradient_tests.rs

Checks backprop correctness.

Very important.

---

network_tests.rs

Tests full network behavior.

---

examples/

Runnable demonstrations.

---

xor.rs

Simple XOR neural network.

Best beginner project.

---

mnist.rs

Digit classifier example.

---

binary_classifier.rs

Binary classification demo.

Example:

spam vs not spam

---

Overall Execution Flow

main.rs
   ↓
dataset loader
   ↓
network initialization
   ↓
forward propagation
   ↓
loss calculation
   ↓
backpropagation
   ↓
optimizer update
   ↓
repeat training loop
   ↓
metrics/evaluation
   ↓
save model