From 6771b6af69c46063a19bfa038dd4dfcc98921866 Mon Sep 17 00:00:00 2001 From: Max Date: Wed, 3 Jul 2024 22:12:43 -0700 Subject: [PATCH] load and save models --- 128.json | 1 + Cargo.lock | 5 ++-- Cargo.toml | 1 + src/main.rs | 38 ++++++++++++++++++++++++------- src/nn/activation.rs | 4 +++- src/nn/layer.rs | 3 ++- src/nn/mod.rs | 53 ++++++++++++++++++++++++++++++++++++++++++- src/nn/node.rs | 3 ++- src/population/mod.rs | 11 +++++++++ 9 files changed, 105 insertions(+), 14 deletions(-) create mode 100644 128.json diff --git a/128.json b/128.json new file mode 100644 index 0000000..f029d8e --- /dev/null +++ b/128.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/Cargo.lock b/Cargo.lock index 5faba77..129fda5 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -753,6 +753,7 @@ dependencies = [ "rand", "rayon", "serde", + "serde_json", "strum", "strum_macros", ] @@ -1379,9 +1380,9 @@ dependencies = [ [[package]] name = "serde_json" -version = "1.0.119" +version = "1.0.120" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e8eddb61f0697cc3989c5d64b452f5488e2b8a60fd7d5076a3045076ffef8cb0" +checksum = "4e0d21c9a8cae1235ad58a00c11cb40d4b1e5c784f1ef2c537876ed6ffd8b7c5" dependencies = [ "itoa", "ryu", diff --git a/Cargo.toml b/Cargo.toml index a3722ea..6656da4 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -14,5 +14,6 @@ leptos_hotkeys = "0.2.1" rand = "0.8.5" rayon = "1.10.0" serde = { version = "1.0.203", features = ["derive"] } +serde_json = "1.0.120" strum = "0.26.3" strum_macros = "0.26.4" diff --git a/src/main.rs b/src/main.rs index 1fd7d80..049c339 100644 --- a/src/main.rs +++ b/src/main.rs @@ -1,7 +1,7 @@ use clap::{Parser, Subcommand}; use leptos::*; use leptos_2048::*; -use nn::activation::ActivationFunction; +use nn::{activation::ActivationFunction, NeuralNetwork}; use population::Population; use ui::RenderGame; @@ -17,12 +17,20 @@ struct Arguments { enum Commands { Web {}, - Cli {}, + Train { + /// Save the model every 10 steps + #[arg(short, long)] + save: Option, + + /// Load the model from file + #[arg(short, long)] + load: Option, + }, } pub const BRAIN_MUTATION_RATE: f64 = 0.1; pub const BRAIN_MUTATION_VARIATION: f64 = 0.1; -pub const AGENTS_KEEP_PROPORTION: f64 = 0.05; +pub const AGENTS_KEEP_PROPORTION: f64 = 0.02; fn main() { let args = Arguments::parse(); @@ -33,16 +41,27 @@ fn main() { } }), - Some(Commands::Cli {}) => { - let layers = &[16, 12, 8, 4]; - let act_funs = &[ActivationFunction::ReLU; 3]; - + Some(Commands::Train { save, load }) => { let rounds = 100; let max_steps = 10000; let evolution_steps = 10000; let n_agents = 1000; - let mut population = Population::new(n_agents, layers, act_funs); + let mut population = match load { + Some(file) => { + let nn = NeuralNetwork::load(&file).expect("Failed to load NN"); + Population::from_nn(n_agents, nn) + } + None => { + let layers = &[16, 128, 64, 4]; + let act_funs = &[ + ActivationFunction::ReLU, + ActivationFunction::ReLU, + ActivationFunction::None, + ]; + Population::new(n_agents, layers, act_funs) + } + }; for _ in 0..evolution_steps { for _ in 0..rounds { @@ -60,6 +79,9 @@ fn main() { best.avg_score(), best.get_highest_tile().expect("Error getting best tile") ); + if let Some(file) = save.clone() { + best.nn.save(&file).expect("Failed to save model"); + } } population.evolve( diff --git a/src/nn/activation.rs b/src/nn/activation.rs index 63224e4..f3dcceb 100644 --- a/src/nn/activation.rs +++ b/src/nn/activation.rs @@ -1,3 +1,5 @@ +use serde::{Deserialize, Serialize}; + /// Computes the sigmoid activation function. pub fn sigmoid(x: f64) -> f64 { 1.0 / (1.0 + (-x).exp()) @@ -18,7 +20,7 @@ pub fn tanh(x: f64) -> f64 { } /// Enumeration of possible activation functions. -#[derive(Clone, Copy)] +#[derive(Clone, Copy, Serialize, Deserialize)] pub enum ActivationFunction { Sigmoid, ReLU, diff --git a/src/nn/layer.rs b/src/nn/layer.rs index bb5ba8b..b562721 100644 --- a/src/nn/layer.rs +++ b/src/nn/layer.rs @@ -1,8 +1,9 @@ use super::activation::ActivationFunction; use super::node::Node; +use serde::{Deserialize, Serialize}; /// A layer in a neural network, consisting of multiple nodes. -#[derive(Clone)] +#[derive(Clone, Serialize, Deserialize)] pub struct Layer { pub nodes: Vec, pub activation_function: ActivationFunction, diff --git a/src/nn/mod.rs b/src/nn/mod.rs index b9ca763..3ce74c4 100644 --- a/src/nn/mod.rs +++ b/src/nn/mod.rs @@ -1,12 +1,15 @@ pub mod activation; mod layer; mod node; +use std::fs::File; +use std::io::{self}; use activation::ActivationFunction; use layer::Layer; +use serde::{Deserialize, Serialize}; /// A neural network consisting of multiple layers. -#[derive(Clone)] +#[derive(Clone, Serialize, Deserialize)] pub struct NeuralNetwork { pub layers: Vec, } @@ -69,12 +72,43 @@ impl NeuralNetwork { layer.update(rate, variation); } } + + /// Saves the neural network to a file in JSON format. + /// + /// # Arguments + /// + /// * `filename` - The name of the file to save the neural network to. + /// + /// # Returns + /// + /// A `Result` indicating success or failure. + pub fn save(&self, filename: &str) -> io::Result<()> { + let file = File::create(filename)?; + serde_json::to_writer(file, &self)?; + Ok(()) + } + + /// Loads a neural network from a file in JSON format. + /// + /// # Arguments + /// + /// * `filename` - The name of the file to load the neural network from. + /// + /// # Returns + /// + /// A `Result` containing the loaded `NeuralNetwork` or an error. + pub fn load(filename: &str) -> io::Result { + let file = File::open(filename)?; + let network = serde_json::from_reader(file)?; + Ok(network) + } } #[cfg(test)] mod tests { use super::activation::ActivationFunction; use super::*; + use std::fs; #[test] fn test_new_neural_network() { @@ -114,4 +148,21 @@ mod tests { assert_eq!(outputs.len(), 1); assert!((outputs[0] - expected_final_output).abs() < 1e-6); } + + #[test] + fn test_save_and_load() { + let layer_sizes = vec![2, 3, 1]; + let activation_functions = vec![ActivationFunction::ReLU, ActivationFunction::Sigmoid]; + let network = NeuralNetwork::new(&layer_sizes, &activation_functions); + + let filename = "test_model.json"; + network.save(filename).expect("Failed to save the network"); + + let loaded_network = NeuralNetwork::load(filename).expect("Failed to load the network"); + + assert_eq!(network.layers.len(), loaded_network.layers.len()); + + // Clean up + fs::remove_file(filename).expect("Failed to remove test file"); + } } diff --git a/src/nn/node.rs b/src/nn/node.rs index ca92364..8f68128 100644 --- a/src/nn/node.rs +++ b/src/nn/node.rs @@ -1,7 +1,8 @@ use rand::Rng; +use serde::{Deserialize, Serialize}; /// A single node (neuron) in a neural network layer. -#[derive(Clone)] +#[derive(Clone, Serialize, Deserialize)] pub struct Node { pub weights: Vec, pub bias: f64, diff --git a/src/population/mod.rs b/src/population/mod.rs index 5038fa8..e005480 100644 --- a/src/population/mod.rs +++ b/src/population/mod.rs @@ -33,6 +33,17 @@ impl Population { } } + pub fn from_nn(n_agents: usize, nn: NeuralNetwork) -> Self { + let mut agents = vec![]; + for _ in 0..n_agents { + agents.push(Agent::new(nn.clone(), Game::new())); + } + Self { + agents, + evolution_step: 0, + } + } + pub fn play(&mut self, max_steps: usize) { let mut v = vec![0usize; 5]; v.par_iter_mut().enumerate().for_each(|(i, x)| *x = i);