in samplecode/machine-learning/enclave/src/lib.rs [97:289]
fn iris_sample() {
println!("IRIS classification sample:");
// Set the layer sizes - from input to output
let layers = &[4,10,10,1];
println!("Layers (Input - Hiddeen - Output) {:?}", layers);
const SAMPLES: usize = 10000000;
let inputs: Vec<f64> = vec![5.1, 3.5, 1.4, 0.2,
4.9, 3.0, 1.4, 0.2,
4.7, 3.2, 1.3, 0.2,
4.6, 3.1, 1.5, 0.2,
5.0, 3.6, 1.4, 0.2,
5.4, 3.9, 1.7, 0.4,
4.6, 3.4, 1.4, 0.3,
5.0, 3.4, 1.5, 0.2,
4.4, 2.9, 1.4, 0.2,
4.9, 3.1, 1.5, 0.1,
5.4, 3.7, 1.5, 0.2,
4.8, 3.4, 1.6, 0.2,
4.8, 3.0, 1.4, 0.1,
4.3, 3.0, 1.1, 0.1,
5.8, 4.0, 1.2, 0.2,
5.7, 4.4, 1.5, 0.4,
5.4, 3.9, 1.3, 0.4,
5.1, 3.5, 1.4, 0.3,
5.7, 3.8, 1.7, 0.3,
5.1, 3.8, 1.5, 0.3,
5.4, 3.4, 1.7, 0.2,
5.1, 3.7, 1.5, 0.4,
4.6, 3.6, 1.0, 0.2,
5.1, 3.3, 1.7, 0.5,
4.8, 3.4, 1.9, 0.2,
5.0, 3.0, 1.6, 0.2,
5.0, 3.4, 1.6, 0.4,
5.2, 3.5, 1.5, 0.2,
5.2, 3.4, 1.4, 0.2,
4.7, 3.2, 1.6, 0.2,
4.8, 3.1, 1.6, 0.2,
5.4, 3.4, 1.5, 0.4,
5.2, 4.1, 1.5, 0.1,
5.5, 4.2, 1.4, 0.2,
4.9, 3.1, 1.5, 0.1,
5.0, 3.2, 1.2, 0.2,
5.5, 3.5, 1.3, 0.2,
4.9, 3.1, 1.5, 0.1,
4.4, 3.0, 1.3, 0.2,
5.1, 3.4, 1.5, 0.2,
5.0, 3.5, 1.3, 0.3,
4.5, 2.3, 1.3, 0.3,
4.4, 3.2, 1.3, 0.2,
5.0, 3.5, 1.6, 0.6,
5.1, 3.8, 1.9, 0.4,
4.8, 3.0, 1.4, 0.3,
5.1, 3.8, 1.6, 0.2,
4.6, 3.2, 1.4, 0.2,
5.3, 3.7, 1.5, 0.2,
5.0, 3.3, 1.4, 0.2,
7.0, 3.2, 4.7, 1.4,
6.4, 3.2, 4.5, 1.5,
6.9, 3.1, 4.9, 1.5,
5.5, 2.3, 4.0, 1.3,
6.5, 2.8, 4.6, 1.5,
5.7, 2.8, 4.5, 1.3,
6.3, 3.3, 4.7, 1.6,
4.9, 2.4, 3.3, 1.0,
6.6, 2.9, 4.6, 1.3,
5.2, 2.7, 3.9, 1.4,
5.0, 2.0, 3.5, 1.0,
5.9, 3.0, 4.2, 1.5,
6.0, 2.2, 4.0, 1.0,
6.1, 2.9, 4.7, 1.4,
5.6, 2.9, 3.6, 1.3,
6.7, 3.1, 4.4, 1.4,
5.6, 3.0, 4.5, 1.5,
5.8, 2.7, 4.1, 1.0,
6.2, 2.2, 4.5, 1.5,
5.6, 2.5, 3.9, 1.1,
5.9, 3.2, 4.8, 1.8,
6.1, 2.8, 4.0, 1.3,
6.3, 2.5, 4.9, 1.5,
6.1, 2.8, 4.7, 1.2,
6.4, 2.9, 4.3, 1.3,
6.6, 3.0, 4.4, 1.4,
6.8, 2.8, 4.8, 1.4,
6.7, 3.0, 5.0, 1.7,
6.0, 2.9, 4.5, 1.5,
5.7, 2.6, 3.5, 1.0,
5.5, 2.4, 3.8, 1.1,
5.5, 2.4, 3.7, 1.0,
5.8, 2.7, 3.9, 1.2,
6.0, 2.7, 5.1, 1.6,
5.4, 3.0, 4.5, 1.5,
6.0, 3.4, 4.5, 1.6,
6.7, 3.1, 4.7, 1.5,
6.3, 2.3, 4.4, 1.3,
5.6, 3.0, 4.1, 1.3,
5.5, 2.5, 4.0, 1.3,
5.5, 2.6, 4.4, 1.2,
6.1, 3.0, 4.6, 1.4,
5.8, 2.6, 4.0, 1.2,
5.0, 2.3, 3.3, 1.0,
5.6, 2.7, 4.2, 1.3,
5.7, 3.0, 4.2, 1.2,
5.7, 2.9, 4.2, 1.3,
6.2, 2.9, 4.3, 1.3,
5.1, 2.5, 3.0, 1.1,
5.7, 2.8, 4.1, 1.3,
6.3, 3.3, 6.0, 2.5,
5.8, 2.7, 5.1, 1.9,
7.1, 3.0, 5.9, 2.1,
6.3, 2.9, 5.6, 1.8,
6.5, 3.0, 5.8, 2.2,
7.6, 3.0, 6.6, 2.1,
4.9, 2.5, 4.5, 1.7,
7.3, 2.9, 6.3, 1.8,
6.7, 2.5, 5.8, 1.8,
7.2, 3.6, 6.1, 2.5,
6.5, 3.2, 5.1, 2.0,
6.4, 2.7, 5.3, 1.9,
6.8, 3.0, 5.5, 2.1,
5.7, 2.5, 5.0, 2.0,
5.8, 2.8, 5.1, 2.4,
6.4, 3.2, 5.3, 2.3,
6.5, 3.0, 5.5, 1.8,
7.7, 3.8, 6.7, 2.2,
7.7, 2.6, 6.9, 2.3,
6.0, 2.2, 5.0, 1.5,
6.9, 3.2, 5.7, 2.3,
5.6, 2.8, 4.9, 2.0,
7.7, 2.8, 6.7, 2.0,
6.3, 2.7, 4.9, 1.8,
6.7, 3.3, 5.7, 2.1,
7.2, 3.2, 6.0, 1.8,
6.2, 2.8, 4.8, 1.8,
6.1, 3.0, 4.9, 1.8,
6.4, 2.8, 5.6, 2.1,
7.2, 3.0, 5.8, 1.6,
7.4, 2.8, 6.1, 1.9,
7.9, 3.8, 6.4, 2.0,
6.4, 2.8, 5.6, 2.2,
6.3, 2.8, 5.1, 1.5,
6.1, 2.6, 5.6, 1.4,
7.7, 3.0, 6.1, 2.3,
6.3, 3.4, 5.6, 2.4,
6.4, 3.1, 5.5, 1.8,
6.0, 3.0, 4.8, 1.8,
6.9, 3.1, 5.4, 2.1,
6.7, 3.1, 5.6, 2.4,
6.9, 3.1, 5.1, 2.3,
5.8, 2.7, 5.1, 1.9,
6.8, 3.2, 5.9, 2.3,
6.7, 3.3, 5.7, 2.5,
6.7, 3.0, 5.2, 2.3,
6.3, 2.5, 5.0, 1.9,
6.5, 3.0, 5.2, 2.0,
6.2, 3.4, 5.4, 2.3,
5.9, 3.0, 5.1, 1.8];
let target: Vec<usize> = vec![0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2];
let target: Vec<f64> = target.into_iter().map(|x| x as f64).collect();
let inputs = Matrix::new(150, 4, inputs);
let targets = Matrix::new(150, 1, target);
// Choose the BCE criterion with L2 regularization (`lambda=0.1`).
let criterion = BCECriterion::new(Regularization::L2(0.1));
// We will just use the default stochastic gradient descent.
let mut model = NeuralNet::mlp(layers, criterion, StochasticGD::default(), Sigmoid);
// Train the model!
model.train(&inputs, &targets).unwrap();
let test_cases = vec![ 5.9, 3.0, 5.1, 1.8];
let test_inputs = Matrix::new(test_cases.len() / 4, 4, test_cases);
println!("Infering {} times", SAMPLES);
// start timer
let now = SystemTime::now();
for _ in 0..SAMPLES {
// Predict
let _ = model.predict(&test_inputs);
}
// end timer
println!("Infer {} times: {:?}", SAMPLES, now.elapsed().unwrap());
}