Updated neural net implementation

benches/examples/nnet.rs

@@ -5,6 +5,7 @@ use std::vec::Vec;
 
 use rusty_machine::learning::nnet::{NeuralNet, BCECriterion};
 use rusty_machine::learning::toolkit::regularization::Regularization;
+use rusty_machine::learning::toolkit::activ_fn::Sigmoid;
 use rusty_machine::learning::optim::grad_desc::StochasticGD;
 
 use rusty_machine::linalg::Matrix;
@@ -51,7 +52,7 @@ fn nnet_and_gate_train(b: &mut Bencher) {
     let criterion = BCECriterion::new(Regularization::L2(0.));
 
     b.iter(|| {
-        let mut model = black_box(NeuralNet::new(layers, criterion, StochasticGD::default()));
+        let mut model = black_box(NeuralNet::mlp(layers, criterion, StochasticGD::default(), Sigmoid));
         let _ = black_box(model.train(&inputs, &targets).unwrap());
     })
 }
@@ -62,7 +63,7 @@ fn nnet_and_gate_predict(b: &mut Bencher) {
     let layers = &[2, 1];
     let criterion = BCECriterion::new(Regularization::L2(0.));
 
-    let mut model = NeuralNet::new(layers, criterion, StochasticGD::default());
+    let mut model = NeuralNet::mlp(layers, criterion, StochasticGD::default(), Sigmoid);
     let _ = model.train(&inputs, &targets);
 
     b.iter(|| {

examples/nnet-and_gate.rs

@@ -6,6 +6,7 @@ use std::vec::Vec;
 
 use rusty_machine::learning::nnet::{NeuralNet, BCECriterion};
 use rusty_machine::learning::toolkit::regularization::Regularization;
+use rusty_machine::learning::toolkit::activ_fn::Sigmoid;
 use rusty_machine::learning::optim::grad_desc::StochasticGD;
 
 use rusty_machine::linalg::Matrix;
@@ -41,7 +42,9 @@ fn main() {
 
     let layers = &[2, 1];
     let criterion = BCECriterion::new(Regularization::L2(0.));
-    let mut model = NeuralNet::new(layers, criterion, StochasticGD::default());
+    // Create a multilayer perceptron with an input layer of size 2 and output layer of size 1
+    // Uses a Sigmoid activation function and uses Stochastic gradient descent for training
+    let mut model = NeuralNet::mlp(layers, criterion, StochasticGD::default(), Sigmoid);
 
     println!("Training...");
     // Our train function returns a Result<(), E>