From d9ba8cc07998a72ce605fb2508962a582d0e3ab7 Mon Sep 17 00:00:00 2001
From: Sander Hautvast <shautvast@gmail.com>
Date: Tue, 14 Feb 2023 17:42:02 +0100
Subject: [PATCH] not working

---
 .gitignore        |   3 +
 Cargo.lock        | 320 ++++++++++++++++++++++++++++++++++++++++++++++
 Cargo.toml        |  13 ++
 src/dataloader.rs |  60 +++++++++
 src/lib.rs        |   2 +
 src/main.rs       |  15 +++
 src/net.rs        | 209 ++++++++++++++++++++++++++++++
 7 files changed, 622 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 Cargo.lock
 create mode 100644 Cargo.toml
 create mode 100644 src/dataloader.rs
 create mode 100644 src/lib.rs
 create mode 100644 src/main.rs
 create mode 100644 src/net.rs

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..8b7a06b
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+/target
+*.iml
+.idea
\ No newline at end of file
diff --git a/Cargo.lock b/Cargo.lock
new file mode 100644
index 0000000..131fe6a
--- /dev/null
+++ b/Cargo.lock
@@ -0,0 +1,320 @@
+# This file is automatically @generated by Cargo.
+# It is not intended for manual editing.
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+
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+
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+ "rand_distr",
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+ "serde_json",
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+ "num-complex",
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+ "typenum",
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+ "paste",
+ "wide",
+]
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+ "unicode-ident",
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+dependencies = [
+ "bytemuck",
+ "safe_arch",
+]
diff --git a/Cargo.toml b/Cargo.toml
new file mode 100644
index 0000000..d87fb52
--- /dev/null
+++ b/Cargo.toml
@@ -0,0 +1,13 @@
+[package]
+name = "mnist-rs"
+version = "0.1.0"
+edition = "2021"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+rand = "0.8"
+rand_distr = "0.4"
+nalgebra = "0.32"
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
\ No newline at end of file
diff --git a/src/dataloader.rs b/src/dataloader.rs
new file mode 100644
index 0000000..7fae50b
--- /dev/null
+++ b/src/dataloader.rs
@@ -0,0 +1,60 @@
+use std::iter::zip;
+
+use rand::prelude::*;
+use serde::Deserialize;
+
+pub fn load_data() -> Data<f32, u8> {
+    /// the mnist data is structured as
+    /// x: [[[pixels]],[[pixels]], etc],
+    /// y: [label1, label2, etc]
+    /// this is transformed to:
+    /// Data : Vec<DataLine>
+    /// DataLine {inputs: Vec<pixels as f32>, label: f32}
+    let raw_data: RawData = serde_json::from_slice(include_bytes!("data/unittest.json")).unwrap();
+    let mut vec = Vec::new();
+    for (x, y) in zip(raw_data.x, raw_data.y) {
+        vec.push(DataLine { inputs: x, label: y});
+    }
+
+    Data(vec)
+}
+
+#[derive(Deserialize)]
+struct RawData {
+    x: Vec<Vec<f32>>,
+    y: Vec<u8>,
+}
+
+/// X is type of input
+/// Y is type of output
+pub struct DataLine<X,Y> {
+    pub inputs: Vec<X>,
+    pub label: Y,
+}
+
+pub struct Data<X,Y>(pub Vec<DataLine<X,Y>>);
+
+
+impl<X,Y> Data<X,Y> {
+    pub fn shuffle(&mut self) {
+        let mut rng = rand::thread_rng();
+        self.0.shuffle(&mut rng);
+    }
+
+    pub fn len(&self) -> usize {
+        self.0.len()
+    }
+
+    pub fn as_batches(&self, batch_size: usize) -> Vec<&[DataLine<X,Y>]> {
+        let mut batches = Vec::with_capacity(self.0.len() / batch_size + 1);
+        let mut offset = 0;
+        for _ in 0..self.0.len() / batch_size {
+            batches.push(&self.0[offset..offset + batch_size]);
+            offset += batch_size;
+        }
+        batches.push(&self.0[offset..self.0.len()]);
+        batches
+    }
+
+
+}
diff --git a/src/lib.rs b/src/lib.rs
new file mode 100644
index 0000000..f154dc1
--- /dev/null
+++ b/src/lib.rs
@@ -0,0 +1,2 @@
+pub mod net;
+pub mod dataloader;
\ No newline at end of file
diff --git a/src/main.rs b/src/main.rs
new file mode 100644
index 0000000..ebd727d
--- /dev/null
+++ b/src/main.rs
@@ -0,0 +1,15 @@
+use mnist_rs::dataloader::load_data;
+
+fn main() {
+    let mut net = mnist_rs::net::Network::from(vec![784, 30, 10]);
+    for w in net.weights.iter() {
+        println!("{}, {}", w.shape().0, w.shape().1);
+    }
+    println!();
+    for b in net.biases.iter() {
+        println!("{:?}", b.shape());
+    }
+    let training_data = load_data();
+
+    net.sgd(training_data, 30, 10, 3.0, &None);
+}
\ No newline at end of file
diff --git a/src/net.rs b/src/net.rs
new file mode 100644
index 0000000..49504ef
--- /dev/null
+++ b/src/net.rs
@@ -0,0 +1,209 @@
+use std::convert::identity;
+use std::iter::zip;
+use std::ops::{Add, Sub};
+
+use nalgebra::{DMatrix, Matrix, OMatrix};
+use rand::prelude::*;
+use rand_distr::Normal;
+
+use crate::dataloader::{Data, DataLine};
+
+#[derive(Debug)]
+pub struct Network {
+    _sizes: Vec<usize>,
+    _num_layers: usize,
+    pub biases: Vec<DMatrix<f32>>,
+    pub weights: Vec<DMatrix<f32>>,
+}
+
+impl Network {
+    /// The list `sizes` contains the number of neurons in the
+    /// respective layers of the network.  For example, if the list
+    /// was [2, 3, 1] then it would be a three-layer network, with the
+    /// first layer containing 2 neurons, the second layer 3 neurons,
+    /// and the third layer 1 neuron. The biases and weights for the
+    /// network are initialized randomly, using a Gaussian
+    /// distribution with mean 0, and variance 1.  Note that the first
+    /// layer is assumed to be an input layer, and by convention we
+    /// won't set any biases for those neurons, since biases are only
+    /// ever used in computing the outputs from later layers.
+    pub fn from(sizes: Vec<usize>) -> Self {
+        Self {
+            _sizes: sizes.clone(),
+            _num_layers: sizes.len(),
+            biases: biases(sizes[1..].to_vec()),
+            weights: weights(zip(sizes[..sizes.len() - 1].to_vec(), sizes[1..].to_vec()).collect()),
+        }
+    }
+
+    fn feed_forward(&self, input: Vec<f32>) -> Vec<f32> {
+        let mut a = DMatrix::from_vec(input.len(), 1, input);
+        for (b, w) in zip(&self.biases, &self.weights) {
+            a = b.add_scalar(w.dot(&a));
+            a.apply(sigmoid_inplace);
+        }
+        a.column(1).iter().map(|v| *v).collect()
+    }
+
+    pub fn sgd(&mut self, mut training_data: Data<f32, u8>, epochs: usize, minibatch_size: usize, eta: f32, test_data: &Option<Data<f32, u8>>) {
+        for j in 0..epochs {
+            training_data.shuffle();
+            let mini_batches = training_data.as_batches(minibatch_size);
+            for mini_batch in mini_batches {
+                self.update_mini_batch(mini_batch, eta);
+            }
+
+            if let Some(test_data) = test_data {
+                println!("Epoch {}: {} / {}", j, self.evaluate(test_data), test_data.len());
+            } else {
+                println!("Epoch {} complete", j);
+            }
+        }
+    }
+
+    /// Update the network's weights and biases by applying
+    /// gradient descent using backpropagation to a single mini batch.
+    /// The ``mini_batch`` is a list of tuples ``(x, y)``, and ``eta``
+    /// is the learning rate.
+    fn update_mini_batch(&mut self, mini_batch: &[DataLine<f32, u8>], eta: f32) {
+        let mut nabla_b: Vec<DMatrix<f32>> = self.biases.iter()
+            .map(|b| b.shape())
+            .map(|s| DMatrix::zeros(s.0, s.1))
+            .collect();
+        let mut nabla_w: Vec<DMatrix<f32>> = self.weights.iter()
+            .map(|w| w.shape())
+            .map(|s| DMatrix::zeros(s.0, s.1))
+            .collect();
+        for line in mini_batch.iter() {
+            let (delta_nabla_b, delta_nabla_w) = self.backprop(line.inputs.to_vec(), line.label);
+
+            nabla_b = zip(&nabla_b, &delta_nabla_b).map(|(nb, dnb)| nb.add(dnb)).collect();
+            nabla_w = zip(&nabla_w, &delta_nabla_w).map(|(nw, dnw)| nw.add(dnw)).collect();
+        }
+
+        self.weights = zip(&self.weights, &nabla_w)
+            .map(|(w, nw)| w.add_scalar(-eta / mini_batch.len() as f32)).collect();
+        self.biases = zip(&self.biases, &nabla_b)
+            .map(|(b, nb)| b.add_scalar(-eta / mini_batch.len() as f32)).collect();
+    }
+
+    /// Return the number of test inputs for which the neural
+    /// network outputs the correct result. Note that the neural
+    /// network's output is assumed to be the index of whichever
+    /// neuron in the final layer has the highest activation.
+    fn evaluate(&self, test_data: &Data<f32, u8>) -> usize {
+        let test_results: Vec<(usize, u8)> = test_data.0.iter()
+            .map(|line| (argmax(self.feed_forward(line.inputs.clone())), line.label))
+            .collect();
+        test_results.into_iter().filter(|(x, y)| *x == *y as usize).count()
+    }
+
+    /// Return a tuple `(nabla_b, nabla_w)` representing the
+    /// gradient for the cost function C_x.  `nabla_b` and
+    /// `nabla_w` are layer-by-layer lists of matrices, similar
+    /// to `self.biases` and `self.weights`.
+    fn backprop(&self, x: Vec<f32>, y: u8) -> (Vec<DMatrix<f32>>, Vec<DMatrix<f32>>) {
+        // zero_grad ie. set gradient to zero
+        let mut nabla_b: Vec<DMatrix<f32>> = self.biases.iter()
+            .map(|b| b.shape())
+            .map(|s| DMatrix::zeros(s.0, s.1))
+            .collect();
+        let mut nabla_w: Vec<DMatrix<f32>> = self.weights.iter()
+            .map(|w| w.shape())
+            .map(|s| DMatrix::zeros(s.0, s.1))
+            .collect();
+
+        // feedforward
+        let mut activation = DMatrix::from_vec(x.len(), 1, x);
+        let mut activations = vec![activation.clone()];
+        let mut zs = vec![];
+
+        for (b, w) in zip(&self.biases, &self.weights) {
+            // println!("{:?}", w.shape());
+            // println!("{:?}", activation.shape());
+            // println!("{:?}", b.shape());
+
+            let mut z: DMatrix<f32> = w * &activation + b;
+            zs.push(z.clone());
+            activation = z.map(sigmoid);
+            activations.push(activation.clone());
+        }
+
+        // backward pass
+        let delta: DMatrix<f32> = self.cost_derivative(
+            &activations[activations.len() - 1],
+            y as f32);
+        println!("delta {:?}", delta.shape());
+        println!("z {:?}", &zs[zs.len() - 1].transpose().shape());
+        let delta = delta * (&zs[zs.len() - 1].transpose().map(sigmoid_prime));
+        println!("delta {:?}", delta.shape());
+        let index = nabla_b.len() - 1;
+        nabla_b[index] = delta.clone();
+
+        println!("delta {:?}", delta.shape());
+        println!("activation {:?}", activations[activations.len() - 2].shape());
+        let index = nabla_w.len() - 1;
+        nabla_w[index] = delta * &activations[activations.len() - 2];
+
+
+        (nabla_b, nabla_w)
+    }
+
+    fn cost_derivative(&self, output_activations: &DMatrix<f32>, y: f32) -> DMatrix<f32> {
+        output_activations.add_scalar(-y)
+    }
+}
+
+fn argmax(val: Vec<f32>) -> usize {
+    let mut max = 0.0;
+    let mut index = 0;
+    for (i, x) in val.iter().enumerate() {
+        if *x > max {
+            index = i;
+            max = *x;
+        }
+    }
+    index
+}
+
+fn biases(sizes: Vec<usize>) -> Vec<DMatrix<f32>> {
+    sizes.iter().map(|size| random_matrix(*size, 1)).collect()
+}
+
+fn weights(sizes: Vec<(usize, usize)>) -> Vec<DMatrix<f32>> {
+    println!("{:?}", sizes);
+    sizes.iter().map(|size| random_matrix(size.1, size.0)).collect()
+}
+
+fn random_matrix(rows: usize, cols: usize) -> DMatrix<f32> {
+    let normal: Normal<f32> = Normal::new(0.0, 1.0).unwrap();
+
+    DMatrix::from_fn(rows, cols, |_, _| normal.sample(&mut thread_rng()))
+}
+
+fn sigmoid_inplace(val: &mut f32) {
+    *val = sigmoid(*val);
+}
+
+fn sigmoid(val: f32) -> f32 {
+    1.0 / (1.0 + (-val).exp())
+}
+
+/// Derivative of the sigmoid function.
+fn sigmoid_prime(val: f32) -> f32 {
+    sigmoid(val) * (1.0 - sigmoid(val))
+}
+
+#[cfg(test)]
+mod test {
+    use nalgebra::DMatrix;
+
+    use super::*;
+
+    #[test]
+    fn test_sigmoid() {
+        let mut mat: DMatrix<f32> = DMatrix::from_vec(1, 1, vec![0.0]);
+        mat.apply(sigmoid_inplace);
+        assert_eq!(mat.get(0), Some(&0.5));
+    }
+}
\ No newline at end of file