Awesome

Convolution

This is a simple implementation of a convolutional neural network (CNN) library in Noir. The library provides basic building blocks for creating and working with CNN layers, including Convolutional layers, Pooling layers, and Linear (fully connected) layers.

Features

• Convolutional Layer: The library includes a Conv2D struct for creating convolutional layers. You can specify the weight, bias, stride, and padding parameters when creating a convolutional layer. The layer supports both forward and padding operations.

• Pooling Layer: The library includes a Pool2D struct for creating pooling layers. You can specify the pooling type (max or average), stride, padding, and kernel size when creating a pooling layer. The layer supports both max pooling and average pooling operations.

• Linear (Fully Connected) Layer: The library includes a Linear struct for creating linear layers. You can specify the weight and bias when creating a linear layer. The layer supports forward operations.

Usage

Here's a basic example of how to use the library to create a simple CNN:

use dep::convolution::{conv::{Conv2D, Pool2D, Linear}, tensor::Tensor};

global in_len = 784;
global w1_len = 45;
global b1_len = 5;
global w2_len = 90;
global b2_len = 10;
global w3_len = 490;
global b3_len = 10;
global zero_point = 100000;
global scale = 10000;


fn main(inputs: [Field; in_len], w1 : [Field; w1_len], b1 : [Field; b1_len], w2 : [Field; w2_len], b2 : [Field; b2_len], w3 : [Field; w3_len], b3 : [Field; b3_len]) {

    let weight1 = Tensor::new_quantized(w1, [5, 1, 3, 3], zero_point, scale);
    let bias1 = Tensor::new_quantized(b1, [5], zero_point, scale);
    let weight2 = Tensor::new_quantized(w2, [10, 1, 3, 3], zero_point, scale);
    let bias2 = Tensor::new_quantized(b2, [10], zero_point, scale);
    let weight3 = Tensor::new_quantized(w3, [49, 10], zero_point, scale);
    let bias3 = Tensor::new_quantized(b3, [10], zero_point, scale);

    // Create a convolutional layer
    let conv_layer1 = Conv2D::new(weight1, bias1, 1, 1);
    let conv_layer2 = Conv2D::new(weight2, bias2, 1, 1);

    // Create a pooling layer
    let pooling_layer = Pool2D {
        stride: 2,
        padding: 0,
        kernel_size: 2,
    };

    // Create a linear layer
    let linear_layer = Linear::new(weight3, bias3);

    // Perform forward pass
    let input = Tensor::new_quantized(inputs, [1, 28, 28], zero_point, scale);
    let mut conv_output1 = conv_layer1.forward(input);
    conv_output1.values = conv_output1.relu();
    let pooled_output1 = pooling_layer.max_pooling(conv_output1);
    let mut conv_output2 = conv_layer2.forward(pooled_output1);
    conv_output2.values = conv_output2.relu();
    let pooled_output2 = pooling_layer.max_pooling(conv_output2);
    // Fixed the side with 1 to have only one column
    let reshape_output = pooled_output2.reshape([1, weight3.shape[0]]);
    let linear_output = linear_layer.forward(reshape_output);
}

#[test]
fn test_main() {
    let inputs = [0; in_len];
    let w1 = [0; w1_len];
    let b1 = [0; b1_len];
    let w2 = [0; w2_len];
    let b2 = [0; b2_len];
    let w3 = [0; w3_len];
    let b3 = [0; b3_len];
    main(inputs, w1, b1, w2, b2, w3, b3);
}

Dependencies

This library utilizes quantized_arithmetic for quantized arithmetic operations and employs matrix_operations for matrix operations.

License

This project is provided under the Apache License, Version 2.0.

Acknowledgments

This library is a simple implementation and may not cover all the features and optimizations required for a production-level CNN library. It serves as a starting point for building more complex neural networks in Noir.

If you have any questions, suggestions, or contributions, please feel free to contact the project maintainers.