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//use transpose::transpose;
use crate::{multipliers::Multiplier, tools::normalize_vector};
use super::{NeuralNetwork, NeuralNetworkError};
/// A two-hidden-layer neural network with customizable layer sizes.
pub struct NeuralNetwork2L<T: Multiplier> {
sizes: (usize, usize, usize, usize),
multiplier: T,
// hidden
weights0: Vec<f32>,
bias0: Vec<f32>,
// hidden
weights1: Vec<f32>,
bias1: Vec<f32>,
// output
weights2: Vec<f32>,
bias2: Vec<f32>,
}
impl<T: Multiplier> NeuralNetwork2L<T> {
#[inline]
pub fn new(multiplier: T, sizes: (usize, usize, usize, usize)) -> Self {
NeuralNetwork2L {
sizes,
multiplier,
weights0: (0..sizes.0*sizes.1).into_iter().map(|_| rand::random::<f32>() - 0.5).collect(),
bias0: (0..sizes.1).into_iter().map(|_| rand::random::<f32>() - 0.5).collect(),
weights1: (0..sizes.1*sizes.2).into_iter().map(|_| rand::random::<f32>() - 0.5).collect(),
bias1: (0..sizes.2).into_iter().map(|_| rand::random::<f32>() - 0.5).collect(),
weights2: (0..sizes.2*sizes.3).into_iter().map(|_| rand::random::<f32>() - 0.5).collect(),
bias2: (0..sizes.3).into_iter().map(|_| rand::random::<f32>() - 0.5).collect(),
}
}
}
impl<T: Multiplier> NeuralNetwork for NeuralNetwork2L<T> {
/// Forward propagation with the given input
#[inline]
fn forward(&mut self, input: &[f32]) -> Vec<f32> {
let mut output = vec![0.0; self.sizes.3];
let _ = self.forward_buf(input, &mut output); //size is guaranteed to be fine due to previous line
output
}
/// Forward propagation with the given input, outputs to buffers
fn forward_buf(&mut self, input: &[f32], output_buf: &mut [f32]) -> Result<(), NeuralNetworkError> {
// ensure buffer can hold the complete output
if output_buf.len() < self.sizes.3 {
return Err(NeuralNetworkError::BadBufferSize);
}
// first hidden layer
let mut layer0 = vec![0.0; self.sizes.1];
self.multiplier.multiply(&input, &self.weights0, (1, self.sizes.0, self.sizes.1), &mut layer0); //matrix mul
for i in 0..layer0.len() { //apply bias + nonlinearity
layer0[i] = (layer0[i] + self.bias0[i]).tanh();
}
// second hidden layer
let mut layer1 = vec![0.0; self.sizes.2];
self.multiplier.multiply(&layer0, &self.weights1, (1, self.sizes.1, self.sizes.2), &mut layer1); //matrix mul
for i in 0..layer1.len() { //apply bias + nonlinearity
layer1[i] = (layer1[i] + self.bias1[i]).tanh();
}
// output layer
//let mut output = vec![0.0; self.sizes.3];
self.multiplier.multiply(&layer1, &self.weights2, (1, self.sizes.2, self.sizes.3), output_buf); //matrix mul
for i in 0..output_buf.len() { //apply bias + nonlinearity
output_buf[i] = (output_buf[i] + self.bias2[i]).tanh();
}
Ok(())
}
/// Backward propagation with the given information
fn backward(&mut self, input: &[f32], expected: &[f32]) -> (Vec<f32>, Vec<f32>, Vec<f32>, Vec<f32>, Vec<f32>, Vec<f32>, f32) {
// forward first hidden layer
let mut layer0_z = vec![0.0; self.sizes.1];
let mut layer0 = vec![0.0; self.sizes.1];
self.multiplier.multiply(&input, &self.weights0, (1, self.sizes.0, self.sizes.1), &mut layer0); //matrix mul
for i in 0..layer0.len() { //apply bias + nonlinearity
layer0_z[i] = layer0[i] + self.bias0[i];
layer0[i] = (layer0[i] + self.bias0[i]).tanh();
}
normalize_vector(&mut layer0_z);
// forward second hidden layer
let mut layer1_z = vec![0.0; self.sizes.2];
let mut layer1 = vec![0.0; self.sizes.2];
self.multiplier.multiply(&layer0, &self.weights1, (1, self.sizes.1, self.sizes.2), &mut layer1); //matrix mul
for i in 0..layer1.len() { //apply bias + nonlinearity
layer1_z[i] = layer1[i] + self.bias1[i];
layer1[i] = (layer1[i] + self.bias1[i]).tanh();
}
normalize_vector(&mut layer1_z);
// forward output layer
let mut output_z = vec![0.0; self.sizes.3];
let mut output = vec![0.0; self.sizes.3];
self.multiplier.multiply(&layer1, &self.weights2, (1, self.sizes.2, self.sizes.3), &mut output); //matrix mul
for i in 0..output.len() { //apply bias + nonlinearity
output_z[i] = output[i] + self.bias2[i];
output[i] = (output[i] + self.bias2[i]).tanh();
}
normalize_vector(&mut output_z);
// delta output layer
let mut delta2 = vec![0.0; self.sizes.3];
let mut diffs = vec![0.0; self.sizes.3];
for i in 0..delta2.len() {
diffs[i] = output[i] - expected[i];
delta2[i] = (output[i] - expected[i]) * (1.0 - output_z[i].tanh().powi(2)); //take difference and mul by invert tanh of z
if delta2[i].is_infinite() || delta2[i].is_nan() {
todo!();
}
}
// delta second hidden layer
let mut delta1 = vec![0.0; self.sizes.2];
self.multiplier.multiply(&self.weights2, &delta2, (self.sizes.2, self.sizes.3, 1), &mut delta1);
for i in 0..delta1.len() {
delta1[i] *= layer1_z[i];
if delta1[i].is_infinite() || delta1[i].is_nan() {
todo!();
}
}
// delta first hidden layer
let mut delta0 = vec![0.0; self.sizes.1];
self.multiplier.multiply(&self.weights1, &delta1, (self.sizes.1, self.sizes.2, 1), &mut delta0);
for i in 0..delta0.len() {
delta0[i] *= layer0_z[i];
if delta0[i].is_infinite() || delta0[i].is_nan() {
println!("{:?}", layer0_z[i]);
todo!();
}
}
// calculate gradients
let mut grads_w0 = vec![0.0; self.weights0.len()];
let mut grads_w1 = vec![0.0; self.weights1.len()];
let mut grads_w2 = vec![0.0; self.weights2.len()];
for n in 0..self.sizes.0 { //weights0 tall
for m in 0..self.sizes.1 { //weights0 wide
grads_w0[m + (n * self.sizes.1)] = input[n] * delta0[m];
if grads_w0[m + (n * self.sizes.1)].is_infinite() || grads_w0[m + (n * self.sizes.1)].is_nan() {
todo!();
}
}
}
for n in 0..self.sizes.1 { //weights1 tall
for m in 0..self.sizes.2 { //weights1 wide
grads_w1[m + (n * self.sizes.2)] = layer0[n] * delta1[m];
if grads_w1[m + (n * self.sizes.2)].is_infinite() || grads_w1[m + (n * self.sizes.2)].is_nan() {
todo!();
}
}
}
for n in 0..self.sizes.2 { //weights2 tall
for m in 0..self.sizes.3 { //weights2 wide
grads_w2[m + (n * self.sizes.3)] = layer1[n] * delta2[m];
if grads_w2[m + (n * self.sizes.3)].is_infinite() || grads_w2[m + (n * self.sizes.3)].is_nan() {
todo!();
}
}
}
// calculate average error
let avg_err = diffs.iter().sum::<f32>() / diffs.len() as f32;
(grads_w0, grads_w1, grads_w2, delta0, delta1, delta2, avg_err) //deltas are equal to bias grads
}
/// Train the model on the provided batch and learning rate
fn train(&mut self, batch: &[(Vec<f32>, Vec<f32>)], eta: f32) -> Vec<f32> {
let mut grad_w0_sum = vec![0.0; self.weights0.len()];
let mut grad_w1_sum = vec![0.0; self.weights1.len()];
let mut grad_w2_sum = vec![0.0; self.weights2.len()];
let mut grad_b0_sum = vec![0.0; self.bias0.len()];
let mut grad_b1_sum = vec![0.0; self.bias1.len()];
let mut grad_b2_sum = vec![0.0; self.bias2.len()];
let mut errors = Vec::with_capacity(batch.len());
// iterate through the batch
for (x, y) in batch {
let (grad_w0, grad_w1, grad_w2, grad_b0, grad_b1, grad_b2, avg_err) = self.backward(x, y);
// sum all the gradients to the accumulator
for i in 0..grad_w0_sum.len() {
grad_w0_sum[i] += grad_w0[i];
}
for i in 0..grad_w1_sum.len() {
grad_w1_sum[i] += grad_w1[i];
}
for i in 0..grad_w2_sum.len() {
grad_w2_sum[i] += grad_w2[i];
}
for i in 0..grad_b0_sum.len() {
grad_b0_sum[i] += grad_b0[i];
}
for i in 0..grad_b1_sum.len() {
grad_b1_sum[i] += grad_b1[i];
}
for i in 0..grad_b2_sum.len() {
grad_b2_sum[i] += grad_b2[i];
}
errors.push(avg_err);
}
// apply the updates to the weights and biases
let frac = eta / batch.len() as f32;
for i in 0..grad_w0_sum.len() {
self.weights0[i] -= frac * grad_w0_sum[i];
}
for i in 0..grad_w1_sum.len() {
self.weights1[i] -= frac * grad_w1_sum[i];
}
for i in 0..grad_w2_sum.len() {
self.weights2[i] -= frac * grad_w2_sum[i];
}
for i in 0..grad_b0_sum.len() {
self.bias0[i] -= frac * grad_b0_sum[i];
}
for i in 0..grad_b1_sum.len() {
self.bias1[i] -= frac * grad_b1_sum[i];
}
for i in 0..grad_b2_sum.len() {
self.bias2[i] -= frac * grad_b2_sum[i];
}
errors
}
///// Backward propagation with the given information, outputs to buffers
//pub fn backward_buf(&mut self, input: &[f32], expected: &[f32], grad_bufs: &mut [Vec<f32>], delta_bufs: &mut [Vec<f32>]) -> Result<f32, NeuralNetworkError> {
//
// // ensure all buffers are big enough to hold all return values
// if grad_bufs.len() < 3 || grad_bufs[0].len() < self.weights0.len() || grad_bufs[1].len() < self.weights1.len() || grad_bufs[2].len() < self.weights2.len() {
// return Err(NeuralNetworkError::BadBufferSize);
// }
// if delta_bufs.len() < 3 || delta_bufs[0].len() < self.sizes.1 || delta_bufs[1].len() < self.sizes.2 || delta_bufs[2].len() < self.sizes.3 {
// return Err(NeuralNetworkError::BadBufferSize);
// }
// // forward first hidden layer
// let mut layer0_z = vec![0.0; self.sizes.1];
// let mut layer0 = vec![0.0; self.sizes.1];
// self.multiplier.multiply(&input, &self.weights0, (1, self.sizes.0, self.sizes.1), &mut layer0); //matrix mul
// for i in 0..layer0.len() { //apply bias + nonlinearity
// layer0_z[i] = layer0[i] + self.bias0[i];
// layer0[i] = (layer0[i] + self.bias0[i]).tanh();
// }
// normalize_vector(&mut layer0_z);
//
// // forward second hidden layer
// let mut layer1_z = vec![0.0; self.sizes.2];
// let mut layer1 = vec![0.0; self.sizes.2];
// self.multiplier.multiply(&layer0, &self.weights1, (1, self.sizes.1, self.sizes.2), &mut layer1); //matrix mul
// for i in 0..layer1.len() { //apply bias + nonlinearity
// layer1_z[i] = layer1[i] + self.bias1[i];
// layer1[i] = (layer1[i] + self.bias1[i]).tanh();
// }
// normalize_vector(&mut layer1_z);
// // forward output layer
// let mut output_z = vec![0.0; self.sizes.3];
// let mut output = vec![0.0; self.sizes.3];
// self.multiplier.multiply(&layer1, &self.weights2, (1, self.sizes.2, self.sizes.3), &mut output); //matrix mul
// for i in 0..output.len() { //apply bias + nonlinearity
// output_z[i] = output[i] + self.bias2[i];
// output[i] = (output[i] + self.bias2[i]).tanh();
// }
// normalize_vector(&mut output_z);
// // delta output layer
// //let mut delta2 = vec![0.0; self.sizes.3];
// let mut diffs = vec![0.0; self.sizes.3];
// for i in 0..delta_bufs[2].len() {
// diffs[i] = output[i] - expected[i];
// delta_bufs[2][i] = (output[i] - expected[i]) * (1.0 - output_z[i].tanh().powi(2)); //take difference and mul by invert tanh of z
// if delta_bufs[2][i].is_infinite() || delta_bufs[2][i].is_nan() {
// todo!();
// }
// }
// // delta second hidden layer
// //let mut delta1 = vec![0.0; self.sizes.2];
// let x = &delta_bufs[2];
// let y = &mut delta_bufs[1];
// self.multiplier.multiply(&self.weights2, x, (self.sizes.2, self.sizes.3, 1), delta_bufs[1].as_mut());
// for i in 0..delta_bufs[1].len() {
// delta_bufs[1][i] *= layer1_z[i];
// if delta_bufs[1][i].is_infinite() || delta_bufs[1][i].is_nan() {
// todo!();
// }
// }
// // delta first hidden layer
// //let mut delta0 = vec![0.0; self.sizes.1];
// self.multiplier.multiply(&self.weights1, &delta_bufs[1], (self.sizes.1, self.sizes.2, 1), &mut delta_bufs[0]);
// for i in 0..delta_bufs[0].len() {
// delta_bufs[0][i] *= layer0_z[i];
// if delta_bufs[0][i].is_infinite() || delta_bufs[0][i].is_nan() {
// println!("{:?}", layer0_z[i]);
// todo!();
// }
// }
// // calculate gradients
// //let mut grads_w0 = vec![0.0; self.weights0.len()];
// //let mut grads_w1 = vec![0.0; self.weights1.len()];
// //let mut grads_w2 = vec![0.0; self.weights2.len()];
// for n in 0..self.sizes.0 { //weights0 tall
// for m in 0..self.sizes.1 { //weights0 wide
// grad_bufs[0][m + (n * self.sizes.1)] = input[n] * delta_bufs[0][m];
// if grad_bufs[0][m + (n * self.sizes.1)].is_infinite() || grad_bufs[0][m + (n * self.sizes.1)].is_nan() {
// todo!();
// }
// }
// }
// for n in 0..self.sizes.1 { //weights1 tall
// for m in 0..self.sizes.2 { //weights1 wide
// grad_bufs[1][m + (n * self.sizes.2)] = layer0[n] * delta_bufs[1][m];
// if grad_bufs[1][m + (n * self.sizes.2)].is_infinite() || grad_bufs[1][m + (n * self.sizes.2)].is_nan() {
// todo!();
// }
// }
// }
// for n in 0..self.sizes.2 { //weights2 tall
// for m in 0..self.sizes.3 { //weights2 wide
// grad_bufs[2][m + (n * self.sizes.3)] = layer1[n] * delta_bufs[2][m];
// if grad_bufs[2][m + (n * self.sizes.3)].is_infinite() || grad_bufs[2][m + (n * self.sizes.3)].is_nan() {
// todo!();
// }
// }
// }
// // calculate average error
// let avg_err = diffs.iter().sum::<f32>() / diffs.len() as f32;
// //(grads_w0, grads_w1, grads_w2, delta0, delta1, delta2, avg_err) //deltas are equal to bias grads
// Ok(avg_err)
//}
}