use std::path::Path;
use newron::dataset::Dataset;
use newron::layers::LayerEnum::*;
use newron::optimizers::sgd::SGD;
use newron::sequential::Sequential;
use newron::loss::{mse::MSE};
use newron::metrics::Metric;
fn main() {
let dataset = Dataset::from_csv(Path::new("datasets/winequality-white.csv"), true).unwrap();
println!("{:?}", dataset);
let mut model = Sequential::new();
model.set_seed(42);
model.add(Dense {
input_units: dataset.get_number_features(),
output_units: 100
});
model.add(ReLU);
model.add(Dense {
input_units: 100,
output_units: dataset.get_number_targets()
});
model.compile(MSE{},
SGD::new(0.0002),
vec![Metric::Accuracy]);
model.fit(&dataset, 200, true);
}