trait對(duì)象的輸入和輸出類型之間的Rust不匹配

所以我的問(wèn)題是，我有一個(gè)層特性，輸入和輸出類型如下：

pub trait Layer {
    type Input: Dimension;
    type Output: Dimension;
    
    fn forward(&mut self, input: &ArrayBase<OwnedRepr<f32>, Self::Input>) -> ArrayBase<OwnedRepr<f32>, Self::Output>;
}

使用此正向功能：

impl<A: Activation> Layer for DenseLayer<A> {
    type Input = Ix2;
    type Output = Ix2;

    fn forward(&mut self, input: &Array2<f32>) -> Array2<f32> {
        assert_eq!(input.shape()[1], self.weights.shape()[0], "Input width must match weight height.");
        let z = input.dot(&self.weights) + &self.biases;

        self.activation.activate(&z)
    }
}

我有這些，這樣我的前進(jìn)或后退函數(shù)就可以接收例如一個(gè)二維數(shù)組，但仍然輸出一個(gè)只有一維的數(shù)組。然后，我有一個(gè)這個(gè)層特性的包裝器的實(shí)現(xiàn)，我想在其中轉(zhuǎn)發(fā)所有層：

pub struct NeuralNetwork<'a, L>
where
    L: Layer + 'a,
{
    layers: Vec<L>,
    loss_function: &'a dyn Cost,
}

impl<'a, L> NeuralNetwork<'a, L>
where
    L: Layer + 'a,
{
    pub fn new(layers: Vec<L>, loss_function: &'a dyn Cost) -> Self {
        NeuralNetwork { layers, loss_function }
    }

    pub fn forward(&mut self, input: &ArrayBase<OwnedRepr<f32>, L::Input>) -> ArrayBase<OwnedRepr<f32>, L::Output> {
        let mut output = input.clone();

        // todo fix the layer forward changing input to output
        // causing mismatch in the input and output dimensions of forward
        for layer in &mut self.layers {
            output = layer.forward(&output);
        }

        output
    }
}

現(xiàn)在，因?yàn)樵趂or循環(huán)中，我首先輸入input類型，然后從layer.forward接收輸出。在下一次迭代中，它接受類型輸出，但layer.forward只接受類型輸入。至少，這就是我認(rèn)為正在發(fā)生的事情。這似乎是一個(gè)非常簡(jiǎn)單的問(wèn)題，但我真的不確定如何解決這個(gè)問(wèn)題。

Edit 1:

Reproduceable Example:

use ndarray::{Array, Array2, ArrayBase, Dimension, OwnedRepr};

pub trait Layer {
    type Input: Dimension;
    type Output: Dimension;

    fn forward(&mut self, input: &ArrayBase<OwnedRepr<f32>, Self::Input>) -> ArrayBase<OwnedRepr<f32>, Self::Output>;
}

// A Dense Layer struct
pub struct DenseLayer {
    weights: Array2<f32>,
    biases: Array2<f32>,
}

impl DenseLayer {
    pub fn new(input_size: usize, output_size: usize) -> Self {
        let weights = Array::random((input_size, output_size), rand::distributions::Uniform::new(-0.5, 0.5));
        let biases = Array::zeros((1, output_size));
        DenseLayer { weights, biases }
    }
}

impl Layer for DenseLayer {
    type Input = ndarray::Ix2;  // Two-dimensional input
    type Output = ndarray::Ix2; // Two-dimensional output

    fn forward(&mut self, input: &ArrayBase<OwnedRepr<f32>, Self::Input>) -> ArrayBase<OwnedRepr<f32>, Self::Output> {
        assert_eq!(input.shape()[1], self.weights.shape()[0], "Input width must match weight height.");
        let z = input.dot(&self.weights) + &self.biases;
        z // Return the output directly without activation
    }
}

// Neural Network struct
pub struct NeuralNetwork<'a, L>
where
    L: Layer + 'a,
{
    layers: Vec<L>,
}

impl<'a, L> NeuralNetwork<'a, L>
where
    L: Layer + 'a,
{
    pub fn new(layers: Vec<L>) -> Self {
        NeuralNetwork { layers }
    }

    pub fn forward(&mut self, input: &ArrayBase<OwnedRepr<f32>, L::Input>) -> ArrayBase<OwnedRepr<f32>, L::Output> {
        let mut output = input.clone();

        for layer in &mut self.layers {
            output = layer.forward(&output);
        }

        output
    }
}

fn main() {
    // Create a neural network with one Dense Layer
    let mut dense_layer = DenseLayer::new(3, 2);
    let mut nn = NeuralNetwork::new(vec![dense_layer]);

    // Create an example input (1 batch, 3 features)
    let input = Array::from_shape_vec((1, 3), vec![1.0, 2.0, 3.0]).unwrap();
    
    // Forward pass
    let output = nn.forward(&input);
    println!("Output: {:?}", output);
}