prepare stuff for public code dump

COPYING

@@ -0,0 +1,14 @@
+            DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE
+                    Version 2, December 2004
+
+ Copyright (C) 2004 Sam Hocevar <sam@hocevar.net>
+
+ Everyone is permitted to copy and distribute verbatim or modified
+ copies of this license document, and changing it is allowed as long
+ as the name is changed.
+
+            DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE
+   TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
+
+  0. You just DO WHAT THE FUCK YOU WANT TO.
+

README.md

@@ -0,0 +1,54 @@
+bigbrain
+===
+
+An experiment in following
+[neuralnetworksanddeeplearning.com](http://neuralnetworksanddeeplearning.com/)
+in Rust. The end result is a web page that can sometimes recognize hand-written
+digits using a three-layer (one hidden) neural network.
+
+![A digit '3' being written in a JS canvas, with the probability of the output being any of the digits 0-9 displayed on a bar graph underneath it.](https://object.ceph-eu.hswaw.net/q3k-personal/a61716bb765cbe53955829fcdb1d9b6d4e2b5c5a4b87b6312940bfae8a2e3a53.png)
+
+You can see it in action at [https://hackerspace.pl/~q3k/bigbrain/main.html](https://hackerspace.pl/~q3k/bigbrain/main.html). It's really good at figuring out the digit 2, and gets somewhat confused about other digits. Be kind to it.
+
+Goals
+---
+
+1. Be a weekend project
+2. Implement everything specific to ML/DL/NN from scratch
+3. End up with a janky JS/WASM demo
+
+Non-Goals
+---
+
+1. Be fast
+2. Be good
+3. Be clean code
+
+Training
+---
+
+First, acquire the MNIST handwritten digit database (training/test sets, both images and labels) and save them in this repo.
+
+Then, `cargo run --release` to run training, which will generate a `net.pb` containing the trained model.
+
+Building web app
+---
+
+You'll need wasm-pack (`cargo install wasm-pack`). Then:
+
+```
+    cd bigbrainjs
+    wasm-pack build --release --target web
+```
+
+You can then serve files from the `bigbrainjs/web` repository to see the web interface. The simplest way to do that is probably to run `python -m http.server`.
+
+
+License
+===
+
+Copyright © 2021-2023 Serge Bazanski <q3k@q3k.org>
+
+This work is free. You can redistribute it and/or modify it under the
+terms of the Do What The Fuck You Want To Public License, Version 2,
+as published by Sam Hocevar. See the COPYING file for more details.

src/main.rs

@@ -5,8 +5,9 @@ use bigbrain::{mnist,network,proto::network as npb};
 use protobuf::Message;
 
 fn main() {
-    env_logger::init();
+    env_logger::init_from_env( env_logger::Env::default().filter_or(env_logger::DEFAULT_FILTER_ENV, "info"));
 
+    log::info!("Loading training/test images...");
     let now = time::Instant::now();
     let train = mnist::load("train").unwrap();
     let test = mnist::load("t10k").unwrap();
@@ -18,19 +19,19 @@ fn main() {
     log::info!("Split into {} training and {} validation", num_train, num_validation);
     let (train, validation) = train.split(num_train);
 
-    //let now = time::Instant::now();
-    //let size = vec![
-    //    28*28 as usize, 
-    //    30usize,
-    //    10usize,
-    //];
-    //let mut net = network::Network::new(&size);
-    //let elapsed = now.elapsed();
-    //log::info!("Created random network {:?} in {:?}.", size, elapsed);
-    //net.sgd(&train, 30, 32, 3.0, Some(&test));
-    //let proto = net.proto();
-    //let mut f = std::fs::File::create("net.pb").unwrap();
-    //proto.write_to_writer(&mut f).unwrap();
+    let now = time::Instant::now();
+    let size = vec![
+        28*28 as usize, 
+        30usize,
+        10usize,
+    ];
+    let mut net = network::Network::new(&size);
+    let elapsed = now.elapsed();
+    log::info!("Created random network {:?} in {:?}.", size, elapsed);
+    net.sgd(&train, 30, 32, 3.0, Some(&test));
+    let proto = net.proto();
+    let mut f = std::fs::File::create("net.pb").unwrap();
+    proto.write_to_writer(&mut f).unwrap();
 
     let nbytes = std::fs::read("net.pb").unwrap();
     let n = npb::Network::parse_from_bytes(&nbytes).unwrap();