| Task: | Säähavainnot |
| Sender: | ToukoP |
| Submission time: | 2023-11-10 01:16:37 +0200 |
| Language: | Rust |
| Status: | COMPILE ERROR |
Compiler report
error[E0583]: file not found for module `submission`
--> input/code.rs:1:1
|
1 | mod submission;
| ^^^^^^^^^^^^^^^
|
= help: to create the module `submission`, create file "input/submission.rs" or "input/submission/mod.rs"
error[E0433]: failed to resolve: use of undeclared type `NeuralNetwork`
--> input/code.rs:29:39
|
29 | let mut changes = NeuralNetwork::new();
| ^^^^^^^^^^^^^ use of undeclared type `NeuralNetwork`
error[E0433]: failed to resolve: use of undeclared type `Layer`
--> input/code.rs:840:34
|
840 | let mut new: Layer<I2, O2> = Layer::random();
| ^^^^^ use of undeclared type `Layer`
error[E0433]: failed to resolve: use of undeclared type `Layer`
--> input/code.rs:877:34
|
877 | let mut new_hidden_layers = [Layer::<H2, H2>::random(); L2];
| ^^^^^ use of undeclared type `Layer`
error[E0412]: cannot find...Code
mod submission;
use std::{
sync::{Arc, RwLock},
thread,
time::Duration,
};
use submission::*;
const THREADS: usize = 8;
pub fn main() {
let (data, output_data) = read_training_data();
let neural_net: Arc<RwLock<NeuralNetwork<INPUT_COUNT, OUTPUT_COUNT, 18, 0>>> =
Arc::new(RwLock::new(get_neural_network()));
for thread in 0..THREADS {
let output = output_data.clone();
let input = data.clone();
let nn = neural_net.clone();
thread::spawn(move || {
let mut last_save_loss = f64::MAX;
loop {
let mut last_loss = 0.;
let mut loss = 0.;
for _ in 0..40 {
let mut changes = NeuralNetwork::new();
loss = 0.;
//let thread_batch_size = input.len() / THREADS;
for i in 0..input.len() {
//let i = (pseudo_rand() * input.len() as f64).floor() as usize;
loss += nn.read().unwrap().train(
&mut changes,
&input[i],
&output[i],
input.len(),
);
}
nn.write().unwrap().apply_changes(&changes, input.len());
println!(
"Thread: {}, Loss: {}, Change {}",
thread,
loss / input.len() as f64,
(last_loss / loss)
);
last_loss = loss;
}
if thread == 0 {
if last_save_loss * 0.999 > loss {
std::fs::write(
format!("./checkpoints/checkpoint-{:.10}.ron", loss),
format!("{:#?}", nn.read().unwrap()),
)
.expect("Can't save network");
last_save_loss = loss;
println!("Checkpoint created!");
}
std::fs::write("./neuralnet.ron", format!("{:#?}", nn.read().unwrap()))
.expect("Can't save network");
println!("saved!");
}
}
});
}
loop {
thread::sleep(Duration::from_secs(100));
}
}
// TRAIN ERROR DETECTION MODEL
pub fn main2() {}
// MODEL EXTENTION STUFF
const EXTENTION_RANDOMNESS: f64 = 1.;
pub fn main_ext() {
let extended: NeuralNetwork<INPUT_COUNT, OUTPUT_COUNT, 24, 0> = extend_network(NeuralNetwork {
input_layer: Layer {
weights: [
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],
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],
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biases: [
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],
},
hidden_layers: [],
output_layer: Layer {
weights: [
[
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-0.03921600011050003,
0.0370939521274188,
-0.0793951508696832,
0.026148745848090012,
],
[
-0.05784469705460472,
-0.0911978,
-0.0203198,
0.04314522651781204,
-0.0667508,
0.015940200000000005,
0.0986312,
-0.05012790002432129,
0.1011235576277164,
0.005210271552491358,
0.08695040000000001,
-0.10734890890977591,
0.040411007643487894,
0.08667700659294068,
0.003490331585907222,
0.050552568192751106,
-0.08956296860469341,
0.08460347832138619,
],
[
0.06130318143340829,
-0.0640232,
0.01866779999999999,
-0.10528751801100177,
-0.0277632,
0.05492779999999999,
0.00698700000000001,
0.05921636390269124,
0.07459058563894118,
0.04236483247814882,
-0.085875,
-0.10633127459407658,
0.06755202417287051,
0.07687365421248915,
-0.09155449352496135,
-0.010688660970600726,
0.06934762870717552,
-0.038809066494141495,
],
[
0.08785580999116811,
-0.0250356,
0.04584239999999999,
0.006993285367626222,
0.0924056,
-0.0367164,
0.0459746,
-0.10762083218141824,
0.07104722556282012,
0.0697344793692867,
-0.0468874,
-0.16960909754507597,
-0.012267110436804741,
0.07859718684549076,
-0.05208907992725116,
0.018023234093684833,
0.11086063223529301,
0.0017069715622517166,
],
[
0.0009267403284441151,
0.0951332,
-0.0339888,
0.0269955548943452,
-0.0804198,
0.0022712000000000066,
0.0731492,
-0.047185344900259885,
0.08665367383529304,
-0.009748430926091865,
0.061468600000000005,
-0.17642604794388536,
0.014857112794169012,
0.06902555854977288,
-0.025225915730265624,
0.06737291263559271,
0.08546101628595873,
0.10696135350502953,
],
],
biases: [
0.15564504222217423,
0.1473202333557324,
0.14150325747102618,
0.1417972607008875,
0.12915401979983357,
0.11102123566726314,
0.10943827460128827,
0.11337626442372599,
0.11272235937363072,
0.08431237434673645,
0.10892382608690006,
0.10589741994315555,
],
},
});
println!("{:#?}", extended);
}
fn extend_layer<const I1: usize, const I2: usize, const O1: usize, const O2: usize>(
layer: Layer<I1, O1>,
) -> Layer<I2, O2> {
let mut new: Layer<I2, O2> = Layer::random();
for i in 0..O2 {
if i < O1 {
new.biases[i] = layer.biases[i];
} else {
new.biases[i] /= EXTENTION_RANDOMNESS;
}
for j in 0..I2 {
if i < O1 && j < I1 {
new.weights[i][j] = layer.weights[i][j];
} else {
new.weights[i][j] /= EXTENTION_RANDOMNESS;
}
}
}
new
}
fn extend_network<
const I1: usize,
const I2: usize,
const O1: usize,
const O2: usize,
const H1: usize,
const H2: usize,
const L1: usize,
const L2: usize,
>(
network: NeuralNetwork<I1, O1, H1, L1>,
) -> NeuralNetwork<I2, O2, H2, L2> {
let old_hidden_layers = network
.hidden_layers
.map(|layer| extend_layer::<H1, H2, H1, H2>(layer));
let mut new_hidden_layers = [Layer::<H2, H2>::random(); L2];
for i in 0..L2 {
if i < L1 {
new_hidden_layers[i] = old_hidden_layers[i];
} else {
for k in 0..H2 {
new_hidden_layers[i].biases[k] /= EXTENTION_RANDOMNESS;
for j in 0..H2 {
new_hidden_layers[i].weights[k][j] /= EXTENTION_RANDOMNESS;
}
}
for j in 0..H2 {
new_hidden_layers[i].weights[j][j] = 1.;
}
}
}
NeuralNetwork {
input_layer: extend_layer::<I1, I2, H1, H2>(network.input_layer),
output_layer: extend_layer::<H1, H2, O1, O2>(network.output_layer),
hidden_layers: new_hidden_layers,
}
}