프로젝트 전반 코드베이스를 리뷰해요

.eslintrc.json

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+{
+  "env": {
+    "browser": true,
+    "es2021": true
+  },
+  "extends": [
+    "eslint:recommended",
+    "plugin:prettier/recommended"
+  ],
+  "plugins": ["prettier"],
+  "parserOptions": {
+    "ecmaVersion": "latest",
+    "sourceType": "module"
+  },
+  "rules": {
+    "prettier/prettier": "error",
+    "no-unused-vars": "warn",
+    "no-console": "off"
+  }
+}

.github/workflows/deploy.yml

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+name: Deploy perceptron preview
+
+on:
+  push:
+    branches:
+      - feat/perceptron
+
+permissions:
+  contents: write
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: pnpm/action-setup@v3
+        with:
+          version: 9
+
+      - uses: actions/setup-node@v4
+        with:
+          node-version: 18
+
+      - name: Install deps
+        run: pnpm install --frozen-lockfile
+
+      - name: Build
+        env:
+          GITHUB_REF_NAME: ${{ github.ref_name }}
+        run: pnpm build
+
+      - name: Deploy
+        uses: peaceiris/actions-gh-pages@v4
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./dist
+          publish_branch: gh-pages
+          destination_dir: feat/perceptron

.gitignore

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+dev/train/trainData/mnist/mnistTraindata_large.js
+dev/train/trainData/mnist/mnist_train.csv
+node_modules
+dist
+build
+*.min.js

.prettierrc

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+{
+  "printWidth": 100,
+  "tabWidth": 4,
+  "trailingComma": "all",
+  "singleQuote": true,
+  "semi": true
+}

dev/train/Tester.js

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+import { Trainer } from './Trainer.js';
+// data sets
+import { mnistTrainData } from './trainData/mnist/mnistTrainData.js';
+import { mnistTestData } from './trainData/mnist/mnistTestData.js';
+import { mnistTrainData_large } from './trainData/mnist/mnistTraindata_large.js';
+
+// train canvasUtils
+import { normalizeInputs, oneHotEncodeLabels } from './utils/trainDataHandler.js';
+import { saveWeightsAsJson } from './utils/saveWeightsAsJson.js';
+
+// <entry point>
+export const tester = () => {
+    const networkConfig = {
+        inputNodes: 784,
+        hiddenNodes: 200,
+        outputNodes: 10,
+        learningRate: 0.15,
+    };
+    const $NN = new Trainer({
+        inputNodes: networkConfig.inputNodes,
+        hiddenNodes: networkConfig.hiddenNodes,
+        outputNodes: networkConfig.outputNodes,
+        learningRate: networkConfig.learningRate,
+    });
+
+    const inputs = normalizeInputs(mnistTrainData);
+    const targets = oneHotEncodeLabels(mnistTrainData, networkConfig);
+
+    inputs.map((array, idx) => {
+        $NN.train(array, targets[idx]);
+        if (idx % 5000 === 0) console.log(`Training progress: ${idx}/60000`);
+        console.log('✅ training complete!');
+    });
+
+    for (let i = 0; i < 9; i++) {
+        console.log('Query', mnistTestData[i][0]);
+        console.log($NN.query(normalizeInputs(mnistTestData)[i]));
+    }
+
+    saveWeightsAsJson($NN.W_inputToHidden, $NN.W_hiddenToOutput);
+    console.log('✅ Weight Downloaded!');
+};
+
+export default tester;

dev/train/Trainer.js

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+import { NeuralNetworkBase } from '../../src/core/NeuralNetworkBase.ts';
+
+// matrix operations
+import { matrixMultiply, transposeMatrix } from '../../src/core/ops/matrixOps.ts';
+
+export class Trainer extends NeuralNetworkBase {
+    train(inputs, targets) {
+        // CNN operations
+        const { finalOutputs, hiddenOutputs } = this.feedForward(inputs);
+        // calculate errors
+        const output_errors = targets.map((v, idx) => v - finalOutputs[idx]).map((v) => [v]); // 출력 계층의 오차를 목표값 - 출력값으로 지정
+        const hidden_errors = matrixMultiply(transposeMatrix(this.W_hiddenToOutput), output_errors); // 은닉 계층의 오차를 은닉-> 출력 계층의 가중치값과(W_hiddenToOutput.T) 출력 계층의 오차들을 재조합하여 계산
+
+        // activation function derivative
+        const activationDerivative_HtO = finalOutputs
+            .map((v) => 1.0 - v)
+            .map((v, idx) => v * finalOutputs[idx]); // hidden to output derivative
+        const outputGradient = activationDerivative_HtO
+            .map((v, idx) => v * output_errors[idx])
+            .map((v) => [v]);
+        const W_HtO_Update = matrixMultiply(outputGradient, transposeMatrix(hiddenOutputs)).map(
+            (array) => array.map((v) => v * this.learningRate),
+        ); // 오차값을 이용해 은닉 계층과 출력 계층간의 가중치 업데이트
+
+        const activationDerivative_ItH = hiddenOutputs
+            .map((v) => 1.0 - v)
+            .map((v, idx) => v * hiddenOutputs[idx]);
+        const hiddenGradient = activationDerivative_ItH
+            .map((v, idx) => v * hidden_errors[idx])
+            .map((v) => [v]);
+        const W_ItH_update = matrixMultiply(
+            hiddenGradient,
+            transposeMatrix(inputs.map((v) => [v])),
+        ).map((array) => array.map((v) => v * this.learningRate));
+
+        // update weights
+        this.W_hiddenToOutput = this.W_hiddenToOutput.map((row, i) =>
+            row.map((v, j) => v + W_HtO_Update[i][j]),
+        );
+        this.W_inputToHidden = this.W_inputToHidden.map((row, i) =>
+            row.map((v, j) => v + W_ItH_update[i][j]),
+        );
+    }
+}

dev/train/dataConverter/trainDataConverter.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+
+</body>
+</html>