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/*use crate::activation::Function;
use crate::nn::Sequential;
use crate::Float;
use crate::loss::Loss;
use crate::optim::Optimizer;
pub struct NeuralNetworkBuilder<T: Float> {
layers: Sequential<T>,
optimizer: Box<dyn Optimizer<T>>,
loss: Box<dyn Loss<T>>,
}
impl<T: Float> NeuralNetworkBuilder<T> {
pub fn new(layers: Vec<Box<dyn Function<T>>>,
optimizer: Box<dyn Optimizer<T>>,
loss: Box<dyn Loss<T>>
) -> Self {
Self {
layers: Sequential::new(layers),
optimizer,
loss
}
}
pub fn change_optim(&mut self, new_optim: Box<dyn Optimizer<T>>) {
self.optimizer = new_optim;
}
pub fn change_loss(&mut self, new_loss: Box<dyn Loss<T>>) {
self.loss = new_loss;
}
}
#[cfg(test)]
mod tests {
use crate::{activation::{Function, Sigmoid}, loss::MSE, nn::{Linear, neural_network_builder::NeuralNetworkBuilder}, optim::SGD};
#[test]
fn some() {
let layers: Vec<Box< dyn Function<f32>>> = vec![
Box::new(Linear::new(2, 2, true)),// First layer: Linear transformation
Box::new(Sigmoid::new()),// Activation function
Box::new(Linear::new(2, 1, true)),// Second layer: Linear transformation
Box::new(Sigmoid::new())// Activation function
];
// Create the sequential model
//let mut model = Sequential::new(layers);
let sgd = SGD::new(0.01);
let mse = MSE::new(0.0);
let _a = NeuralNetworkBuilder::new(layers,
Box::new(sgd),
Box::new(mse));
}
}*/