使用rust编写一个二层神经网络

好的，下面是使用 Rust 编写一个简单的二层神经网络的代码示例：

extern crate ndarray;
extern crate rand;

use ndarray::{Array, Array2, Axis};
use rand::prelude::*;

fn main() {
    // 定义输入和输出数据
    let inputs = Array2::from_shape_vec((4, 2), vec![0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0]).unwrap();
    let outputs = Array::from_vec(vec![0.0, 1.0, 1.0, 0.0]);

    // 定义神经网络的结构
    let input_size = 2;
    let hidden_size = 3;
    let output_size = 1;

    // 初始化神经网络的权重和偏置
    let mut rng = thread_rng();
    let w1 = Array::random((input_size, hidden_size), StandardNormal, &mut rng);
    let b1 = Array::random(hidden_size, StandardNormal, &mut rng);
    let w2 = Array::random((hidden_size, output_size), StandardNormal, &mut rng);
    let b2 = Array::random(output_size, StandardNormal, &mut rng);

    // 训练神经网络
    let learning_rate = 0.1;
    let num_iterations = 10000;

    for _ in 0..num_iterations {
        let hidden_layer = inputs.dot(&w1) + &b1;
        let activated_hidden_layer = hidden_layer.mapv(|x| 1.0 / (1.0 + (-x).exp()));
        let output_layer = activated_hidden_layer.dot(&w2) + &b2;
        let predicted_output = output_layer.mapv(|x| 1.0 / (1.0 + (-x).exp()));

        let error = &predicted_output - &outputs;
        let d_predicted_output = &error * &predicted_output * (&Array::ones(outputs.len()) - &predicted_output);
        let error_hidden_layer = d_predicted_output.dot(&w2.t());
        let d_hidden_layer = error_hidden_layer * &activated_hidden_layer * (&Array::ones(hidden_layer.len()) - &activated_hidden_layer);

        w2 -= learning_rate * &activated_hidden_layer.t().dot(&d_predicted_output);
        b2 -= learning_rate * d_predicted_output.sum_axis(Axis(0));
        w1 -= learning_rate * &inputs.t().dot(&d_hidden_layer);
        b1 -= learning_rate * d_hidden_layer.sum_axis(Axis(0));
    }

    // 测试神经网络
    let test_input = Array::from_vec(vec![1.0, 0.0]);
    let hidden_layer = test_input.dot(&w1) + &b1;
    let activated_hidden_layer = hidden_layer.mapv(|x| 1.0 / (1.0 + (-x).exp()));
    let output_layer = activated_hidden_layer.dot(&w2) + &b2;
    let predicted_output = output_layer.mapv(|x| 1.0 / (1.0 + (-x).exp()));

    println!("预测输出：{:?}", predicted_output);
}

这个代码示例实现了一个二层神经网络，包括输入层、隐藏层和输出层。输入层包含 2 个神经元，隐藏层包含 3 个