| <!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Source of the Rust file `/root/.cargo/git/checkouts/incubator-teaclave-crates-c8106113f74feefc/ede1f68/rusty-machine/src/learning/nnet/net_layer.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>net_layer.rs - source</title><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../SourceSerif4-Regular.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../FiraSans-Regular.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../FiraSans-Medium.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../SourceCodePro-Regular.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../SourceSerif4-Bold.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../../../SourceCodePro-Semibold.ttf.woff2"><link rel="stylesheet" href="../../../../normalize.css"><link rel="stylesheet" href="../../../../rustdoc.css" id="mainThemeStyle"><link rel="stylesheet" href="../../../../ayu.css" disabled><link rel="stylesheet" href="../../../../dark.css" disabled><link rel="stylesheet" href="../../../../light.css" id="themeStyle"><script id="default-settings" ></script><script src="../../../../storage.js"></script><script defer src="../../../../source-script.js"></script><script defer src="../../../../source-files.js"></script><script defer src="../../../../main.js"></script><noscript><link rel="stylesheet" href="../../../../noscript.css"></noscript><link rel="alternate icon" type="image/png" href="../../../../favicon-16x16.png"><link rel="alternate icon" type="image/png" href="../../../../favicon-32x32.png"><link rel="icon" type="image/svg+xml" href="../../../../favicon.svg"></head><body class="rustdoc source"><!--[if lte IE 11]><div class="warning">This old browser is unsupported and will most likely display funky things.</div><![endif]--><nav class="sidebar"><a class="sidebar-logo" href="../../../../rusty_machine/index.html"><div class="logo-container"><img class="rust-logo" src="../../../../rust-logo.svg" alt="logo"></div></a></nav><main><div class="width-limiter"><nav class="sub"><a class="sub-logo-container" href="../../../../rusty_machine/index.html"><img class="rust-logo" src="../../../../rust-logo.svg" alt="logo"></a><form class="search-form"><div class="search-container"><span></span><input class="search-input" name="search" autocomplete="off" spellcheck="false" placeholder="Click or press ‘S’ to search, ‘?’ for more options…" type="search"><div id="help-button" title="help" tabindex="-1"><a href="../../../../help.html">?</a></div><div id="settings-menu" tabindex="-1"><a href="../../../../settings.html" title="settings"><img width="22" height="22" alt="Change settings" src="../../../../wheel.svg"></a></div></div></form></nav><section id="main-content" class="content"><div class="example-wrap"><pre class="src-line-numbers"><span id="1">1</span> |
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| </pre><pre class="rust"><code><span class="doccomment">//! Neural Network Layers |
| |
| </span><span class="kw">use </span>linalg::{Matrix, MatrixSlice, BaseMatrix}; |
| |
| <span class="kw">use </span>learning::LearningResult; |
| <span class="kw">use </span>learning::error::{Error, ErrorKind}; |
| <span class="kw">use </span>learning::toolkit::activ_fn::ActivationFunc; |
| |
| <span class="kw">use </span>rand::thread_rng; |
| <span class="kw">use </span>rand::distributions::Distribution; |
| <span class="kw">use </span>rand_distr::Normal; |
| |
| <span class="kw">use </span>std::fmt::Debug; |
| |
| <span class="doccomment">/// Trait for neural net layers |
| </span><span class="kw">pub trait </span>NetLayer : Debug { |
| <span class="doccomment">/// The result of propogating data forward through this layer |
| </span><span class="kw">fn </span>forward(<span class="kw-2">&</span><span class="self">self</span>, input: <span class="kw-2">&</span>Matrix<f64>, params: MatrixSlice<f64>) -> LearningResult<Matrix<f64>>; |
| |
| <span class="doccomment">/// The gradient of the output of this layer with respect to its input |
| </span><span class="kw">fn </span>back_input(<span class="kw-2">&</span><span class="self">self</span>, out_grad: <span class="kw-2">&</span>Matrix<f64>, input: <span class="kw-2">&</span>Matrix<f64>, output: <span class="kw-2">&</span>Matrix<f64>, params: MatrixSlice<f64>) -> Matrix<f64>; |
| |
| <span class="doccomment">/// The gradient of the output of this layer with respect to its parameters |
| </span><span class="kw">fn </span>back_params(<span class="kw-2">&</span><span class="self">self</span>, out_grad: <span class="kw-2">&</span>Matrix<f64>, input: <span class="kw-2">&</span>Matrix<f64>, output: <span class="kw-2">&</span>Matrix<f64>, params: MatrixSlice<f64>) -> Matrix<f64>; |
| |
| <span class="doccomment">/// The default value of the parameters of this layer before training |
| </span><span class="kw">fn </span>default_params(<span class="kw-2">&</span><span class="self">self</span>) -> Vec<f64>; |
| |
| <span class="doccomment">/// The shape of the parameters used by this layer |
| </span><span class="kw">fn </span>param_shape(<span class="kw-2">&</span><span class="self">self</span>) -> (usize, usize); |
| |
| <span class="doccomment">/// The number of parameters used by this layer |
| </span><span class="kw">fn </span>num_params(<span class="kw-2">&</span><span class="self">self</span>) -> usize { |
| <span class="kw">let </span>shape = <span class="self">self</span>.param_shape(); |
| shape.<span class="number">0 </span>* shape.<span class="number">1 |
| </span>} |
| } |
| |
| <span class="doccomment">/// Linear network layer |
| /// |
| /// Represents a fully connected layer with optional bias term |
| /// |
| /// The parameters are a matrix of weights of size I x N |
| /// where N is the dimensionality of the output and I the dimensionality of the input |
| </span><span class="attribute">#[derive(Debug, Clone, Copy)] |
| </span><span class="kw">pub struct </span>Linear { |
| <span class="doccomment">/// The number of dimensions of the input |
| </span>input_size: usize, |
| <span class="doccomment">/// The number of dimensions of the output |
| </span>output_size: usize, |
| <span class="doccomment">/// Whether or not to include a bias term |
| </span>has_bias: bool, |
| } |
| |
| <span class="kw">impl </span>Linear { |
| <span class="doccomment">/// Construct a new Linear layer |
| </span><span class="kw">pub fn </span>new(input_size: usize, output_size: usize) -> Linear { |
| Linear { |
| input_size: input_size + <span class="number">1</span>, |
| output_size: output_size, |
| has_bias: <span class="bool-val">true |
| </span>} |
| } |
| |
| <span class="doccomment">/// Construct a Linear layer without a bias term |
| </span><span class="kw">pub fn </span>without_bias(input_size: usize, output_size: usize) -> Linear { |
| Linear { |
| input_size: input_size, |
| output_size: output_size, |
| has_bias: <span class="bool-val">false |
| </span>} |
| } |
| } |
| |
| <span class="kw">fn </span>remove_first_col(mat: Matrix<f64>) -> Matrix<f64> |
| { |
| <span class="kw">let </span>rows = mat.rows(); |
| <span class="kw">let </span>cols = mat.cols(); |
| <span class="kw">let </span><span class="kw-2">mut </span>data = mat.into_vec(); |
| |
| <span class="kw">let </span>len = data.len(); |
| <span class="kw">let </span><span class="kw-2">mut </span>del = <span class="number">0</span>; |
| { |
| <span class="kw">let </span>v = <span class="kw-2">&mut *</span>data; |
| |
| <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..len { |
| <span class="kw">if </span>i % cols == <span class="number">0 </span>{ |
| del += <span class="number">1</span>; |
| } <span class="kw">else if </span>del > <span class="number">0 </span>{ |
| v[i - del] = v[i]; |
| } |
| } |
| } |
| <span class="kw">if </span>del > <span class="number">0 </span>{ |
| data.truncate(len - del); |
| } |
| Matrix::new(rows, cols - <span class="number">1</span>, data) |
| } |
| |
| <span class="kw">impl </span>NetLayer <span class="kw">for </span>Linear { |
| <span class="doccomment">/// Computes a matrix product |
| /// |
| /// input should have dimensions N x I |
| /// where N is the number of samples and I is the dimensionality of the input |
| </span><span class="kw">fn </span>forward(<span class="kw-2">&</span><span class="self">self</span>, input: <span class="kw-2">&</span>Matrix<f64>, params: MatrixSlice<f64>) -> LearningResult<Matrix<f64>> { |
| <span class="kw">if </span><span class="self">self</span>.has_bias { |
| <span class="kw">if </span>input.cols()+<span class="number">1 </span>!= params.rows() { |
| <span class="prelude-val">Err</span>(Error::new(ErrorKind::InvalidData, <span class="string">"The input had the wrong number of columns"</span>)) |
| } <span class="kw">else </span>{ |
| <span class="prelude-val">Ok</span>(<span class="kw-2">&</span>Matrix::ones(input.rows(), <span class="number">1</span>).hcat(input) * <span class="kw-2">&</span>params) |
| } |
| } <span class="kw">else </span>{ |
| <span class="kw">if </span>input.cols() != params.rows() { |
| <span class="prelude-val">Err</span>(Error::new(ErrorKind::InvalidData, <span class="string">"The input had the wrong number of columns"</span>)) |
| } <span class="kw">else </span>{ |
| <span class="prelude-val">Ok</span>(input * <span class="kw-2">&</span>params) |
| } |
| } |
| } |
| |
| <span class="kw">fn </span>back_input(<span class="kw-2">&</span><span class="self">self</span>, out_grad: <span class="kw-2">&</span>Matrix<f64>, <span class="kw">_</span>: <span class="kw-2">&</span>Matrix<f64>, <span class="kw">_</span>: <span class="kw-2">&</span>Matrix<f64>, params: MatrixSlice<f64>) -> Matrix<f64> { |
| <span class="macro">debug_assert_eq!</span>(out_grad.cols(), params.cols()); |
| <span class="kw">let </span>gradient = out_grad * <span class="kw-2">&</span>params.transpose(); |
| <span class="kw">if </span><span class="self">self</span>.has_bias { |
| remove_first_col(gradient) |
| } <span class="kw">else </span>{ |
| gradient |
| } |
| } |
| |
| <span class="kw">fn </span>back_params(<span class="kw-2">&</span><span class="self">self</span>, out_grad: <span class="kw-2">&</span>Matrix<f64>, input: <span class="kw-2">&</span>Matrix<f64>, <span class="kw">_</span>: <span class="kw-2">&</span>Matrix<f64>, <span class="kw">_</span>: MatrixSlice<f64>) -> Matrix<f64> { |
| <span class="macro">debug_assert_eq!</span>(input.rows(), out_grad.rows()); |
| <span class="kw">if </span><span class="self">self</span>.has_bias { |
| <span class="kw-2">&</span>Matrix::ones(input.rows(), <span class="number">1</span>).hcat(input).transpose() * out_grad |
| } <span class="kw">else </span>{ |
| <span class="kw-2">&</span>input.transpose() * out_grad |
| } |
| } |
| |
| <span class="doccomment">/// Initializes weights using Xavier initialization |
| /// |
| /// weights drawn from gaussian distribution with 0 mean and variance 2/(input_size+output_size) |
| </span><span class="kw">fn </span>default_params(<span class="kw-2">&</span><span class="self">self</span>) -> Vec<f64> { |
| <span class="kw">let </span><span class="kw-2">mut </span>distro = Normal::new(<span class="number">0.0</span>, (<span class="number">2.0</span>/(<span class="self">self</span>.input_size+<span class="self">self</span>.output_size) <span class="kw">as </span>f64).sqrt()).unwrap(); |
| <span class="kw">let </span><span class="kw-2">mut </span>rng = thread_rng(); |
| |
| (<span class="number">0</span>..<span class="self">self</span>.input_size<span class="kw-2">*</span><span class="self">self</span>.output_size).map(|<span class="kw">_</span>| distro.sample(<span class="kw-2">&mut </span>rng)) |
| .collect() |
| } |
| |
| <span class="kw">fn </span>param_shape(<span class="kw-2">&</span><span class="self">self</span>) -> (usize, usize) { |
| (<span class="self">self</span>.input_size, <span class="self">self</span>.output_size) |
| } |
| } |
| |
| <span class="kw">impl</span><T: ActivationFunc> NetLayer <span class="kw">for </span>T { |
| <span class="doccomment">/// Applies the activation function to each element of the input |
| </span><span class="kw">fn </span>forward(<span class="kw-2">&</span><span class="self">self</span>, input: <span class="kw-2">&</span>Matrix<f64>, <span class="kw">_</span>: MatrixSlice<f64>) -> LearningResult<Matrix<f64>> { |
| <span class="kw">let </span><span class="kw-2">mut </span>output = Vec::with_capacity(input.rows()<span class="kw-2">*</span>input.cols()); |
| <span class="kw">for </span>val <span class="kw">in </span>input.data() { |
| output.push(T::func(<span class="kw-2">*</span>val)); |
| } |
| <span class="prelude-val">Ok</span>(Matrix::new(input.rows(), input.cols(), output)) |
| } |
| |
| <span class="kw">fn </span>back_input(<span class="kw-2">&</span><span class="self">self</span>, out_grad: <span class="kw-2">&</span>Matrix<f64>, <span class="kw">_</span>: <span class="kw-2">&</span>Matrix<f64>, output: <span class="kw-2">&</span>Matrix<f64>, <span class="kw">_</span>: MatrixSlice<f64>) -> Matrix<f64> { |
| <span class="kw">let </span><span class="kw-2">mut </span>in_grad = Vec::with_capacity(output.rows()<span class="kw-2">*</span>output.cols()); |
| <span class="kw">for </span>(y, g) <span class="kw">in </span>output.data().iter().zip(out_grad.data()) { |
| in_grad.push(T::func_grad_from_output(<span class="kw-2">*</span>y) * g); |
| } |
| Matrix::new(output.rows(), output.cols(), in_grad) |
| } |
| |
| <span class="kw">fn </span>back_params(<span class="kw-2">&</span><span class="self">self</span>, <span class="kw">_</span>: <span class="kw-2">&</span>Matrix<f64>, <span class="kw">_</span>: <span class="kw-2">&</span>Matrix<f64>, <span class="kw">_</span>: <span class="kw-2">&</span>Matrix<f64>, <span class="kw">_</span>: MatrixSlice<f64>) -> Matrix<f64> { |
| Matrix::new(<span class="number">0</span>, <span class="number">0</span>, Vec::new()) |
| } |
| |
| <span class="kw">fn </span>default_params(<span class="kw-2">&</span><span class="self">self</span>) -> Vec<f64> { |
| Vec::new() |
| } |
| |
| <span class="kw">fn </span>param_shape(<span class="kw-2">&</span><span class="self">self</span>) -> (usize, usize) { |
| (<span class="number">0</span>, <span class="number">0</span>) |
| } |
| } |
| </code></pre></div> |
| </section></div></main><div id="rustdoc-vars" data-root-path="../../../../" data-current-crate="rusty_machine" data-themes="ayu,dark,light" data-resource-suffix="" data-rustdoc-version="1.66.0-nightly (5c8bff74b 2022-10-21)" ></div></body></html> |
