pub fn train(&mut self, xs: &Array2<f64>, ys: &Array2<f64>, n: usize) {
        let mut pb = ProgressBar::new(n as u64);
        pb.format("[=>-]");
        for _ in 0..n {
            let activations = self.forward(xs);
            pb.inc();
            self.backward(xs, ys, &activations);
        }
    }
// Train the network with the given input, output n number of times
Train the network with the given input, output n number of times
Train the network with the given input output n number of times
pub fn train(&mut self, xs: &Array2<f64>, ys: &Array2<f64>, n: usize) {
        let mut pb = ProgressBar::new(n as u64);
        pb.format("[=>-]");
        for _ in 0..n {
            let activations = self.forward(xs);
            pb.inc();
            self.backward(xs, ys, &activations);
        }
    }
pub fn train ( & mut self , xs : & Array2 < f64 > , ys : & Array2 < f64 > , n : usize ) { let mut pb = ProgressBar :: new ( n as u64 ) ; pb . format ( " " ) ; for _ in 0 .. n { let activations = self . forward ( xs ) ; pb . inc ( ) ; self . backward ( xs , ys , & activations ) ; } }
Train the network with the given input, output n number of times
Train the network with the given input output n number of times
&mut self
xs
&Array2<f64>
ys
&Array2<f64>
n
usize