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Previously only saved `W` and `b` properties, silently dropping Conv2D and BatchNorm2d weights.\r\n\r\n\r\n**⚠️ BREAKING CHANGES in v2.0.0:**\r\n- Tokenizer API: `tokenizeBatch()` → `transform()`, `detokenizeBatch()` → `inverseTransform()`\r\n- Tokenizer now uses `<PAD>` at index 0 and `<UNK>` at index 1\r\n- MSELoss gradient scale now matches PyTorch behavior\r\n\r\n---\r\n\r\n# Overview\r\n\r\n**Mini-JSTorch provides a minimal neural network engine implemented entirely in plain JavaScript.**\r\n\r\n*It is intended for:*\r\n\r\n- learning how neural networks work internally\r\n- experimenting with small models\r\n- running simple training loops in the browser\r\n- environments where large frameworks are unnecessary or unavailable\r\n\r\n`mini-jstorch is intentionally designed to be small, readable, and easy to debug.`\r\n\r\n---\r\n\r\n# Key Characteristics\r\n\r\n- Zero dependencies\r\n- Works in Node.js or others enviornments and browser environments\r\n- Explicit, manual forward and backward passes\r\n- Focused on 2D training logic (`[batch][features]`)\r\n- Designed for educational and experimental use\r\n\r\n---\r\n\r\n# Browser Support\r\n\r\nNow, Mini-JSTorch can be used directly in browsers:\r\n\r\n- via ESM imports\r\n- via CDN / `<script>` with a global `JST` object\r\n\r\nThis makes it suitable for:\r\n\r\n- demos\r\n- learning environments\r\n- lightweight frontend experiments\r\n\r\nHere example code to make a simple Model with JSTorch.\r\nIn Browser/Website:\r\n\r\n```html\r\n<!DOCTYPE html>\r\n<html>\r\n<body style=\"font-family:monospace; padding:20px;\">\r\n    <h3>mini-jstorch XOR Demo</h3>\r\n    <div id=\"status\">Initializing...</div>\r\n    <pre id=\"log\" style=\"background:#eee; padding:10px;\"></pre>\r\n    <div id=\"res\"></div>\r\n\r\n    <script type=\"module\">\r\n        import { Sequential, Linear, ReLU, MSELoss, Adam, StepLR, Tanh } from 'https://unpkg.com/mini-jstorch@2.0.4/index.js';\r\n        \r\n        async function train() {\r\n            const statusEl = document.getElementById('status');\r\n            const logEl = document.getElementById('log');\r\n            try {\r\n                const model = new Sequential([\r\n                    new Linear(2, 16), new Tanh(),\r\n                    new Linear(16, 8), new ReLU(),\r\n                    new Linear(8, 1)\r\n                ]);\r\n\r\n                const X = [[0,0], [0,1], [1,0], [1,1]];\r\n                const y = [[0], [1], [1], [0]];\r\n                const criterion = new MSELoss();\r\n                const optimizer = new Adam(model.parameters(), 0.1);\r\n                const scheduler = new StepLR(optimizer, 25, 0.5);\r\n\r\n                for (let epoch = 0; epoch <= 1000; epoch++) {\r\n                    const loss = criterion.forward(model.forward(X), y);\r\n                    model.backward(criterion.backward());\r\n                    optimizer.step();\r\n                    scheduler.step();\r\n                    \r\n                    if (epoch % 200 === 0) {\r\n                        logEl.textContent += `Epoch ${epoch} | Loss: ${loss.toFixed(6)}\\n`;\r\n                        statusEl.textContent = `Training: ${epoch}/1000`;\r\n                        await new Promise(r => setTimeout(r, 1));\r\n                    }\r\n                }\r\n\r\n                statusEl.textContent = 'Done';\r\n                const preds = model.forward(X);\r\n                document.getElementById('res').innerHTML = `<h4>Results:</h4>` + \r\n                    X.map((input, i) => `[${input}] -> <b>${preds[i][0].toFixed(4)}</b> (Target: ${y[i][0]})`).join('<br>');\r\n\r\n            } catch (e) {\r\n                statusEl.textContent = 'Error: ' + e.message;\r\n            }\r\n        }\r\n        train();\r\n    </script>\r\n</body>\r\n</html>\r\n```\r\n\r\n---\r\n\r\n# Core Features\r\n\r\n# Layers\r\n\r\n- Linear\r\n- Flatten\r\n- Conv2D (*experimental*)\r\n\r\n# Activations\r\n\r\n- ReLU\r\n- Sigmoid\r\n- Tanh\r\n- LeakyReLU\r\n- GELU\r\n- Mish\r\n- SiLU\r\n- ELU\r\n\r\n# Loss Functions\r\n\r\n- MSELoss\r\n- CrossEntropyLoss (*legacy*, use **SoftmaxCrossEntropy** instead)\r\n- SoftmaxCrossEntropyLoss (**recommended**)\r\n- BCEWithLogitsLoss (**recommended**)\r\n\r\n# Optimizers\r\n\r\n- SGD\r\n- Adam\r\n- AdamW\r\n- Lion\r\n\r\n# Learning Rate Schedulers\r\n\r\n- StepLR\r\n- LambdaLR\r\n- ReduceLROnPlateau\r\n- Regularization\r\n- Dropout\r\n- BatchNorm2D (*experimental*)\r\n\r\n# Utilities\r\n\r\n- zeros\r\n- randomMatrix\r\n- dot\r\n- addMatrices\r\n- reshape\r\n- stack\r\n- flatten\r\n- concat\r\n- softmax\r\n- crossEntropy\r\n\r\n# Model Container\r\n\r\n- Sequential\r\n\r\n---\r\n\r\n# Installation \r\n\r\n## Node.js\r\n```bash\r\nnpm install mini-jstorch@latest\r\n```\r\nNode.js v18+ or any modern browser with ES module support is recommended.\r\n\r\n## Git\r\n```bash\r\ngit clone https://github.com/Rizal-HID11/mini-jstorch-github\r\n```\r\n\r\n---\r\n\r\n# Quick Start (Recommended Loss)\r\n\r\n## Multi-class Classification (SoftmaxCrossEntropy)\r\n\r\n```javascript\r\nimport {\r\n  Sequential,\r\n  Linear,\r\n  ReLU,\r\n  SoftmaxCrossEntropyLoss,\r\n  Adam\r\n} from \"./src/jstorch.js\";\r\n\r\nconst model = new Sequential([\r\n  new Linear(2, 8),\r\n  new ReLU(),\r\n  new Linear(8, 2)\r\n]);\r\n\r\nconst X = [\r\n  [0,0], [0,1], [1,0], [1,1]\r\n];\r\nconst Y = [\r\n  [1,0], [0,1], [0,1], [1,0]\r\n];\r\n\r\nconst lossFn = new SoftmaxCrossEntropyLoss();\r\nconst optimizer = new Adam(model.parameters(), {lr: 0.1});\r\n\r\nfor (let epoch = 1; epoch <= 300; epoch++) {\r\n  const logits = model.forward(X);\r\n  const loss = lossFn.forward(logits, Y);\r\n  const grad = lossFn.backward();\r\n  model.backward(grad);\r\n  optimizer.step();\r\n  model.zeroGrad();\r\n\r\n  if (epoch % 50 === 0) {\r\n    console.log(`Epoch ${epoch}, Loss: ${loss.toFixed(6)}`);\r\n  }\r\n}\r\n\r\nconsole.log('\\nResults:');\r\nconst logits = model.forward(X);\r\nX.forEach((input, i) => {\r\n  const pred = logits[i][0] > logits[i][1] ? 0 : 1;\r\n  const target = Y[i][0] === 1 ? 0 : 1;\r\n  console.log(`  [${input}] → class ${pred} (target: ${target}) ${pred === target ? 'TRUE' : 'FALSE'}`);\r\n});\r\n```\r\n`Important:` Do not combine `SoftmaxCrossEntropyLoss` with a `Softmax` layer.\r\n\r\n## Binary Classifiaction (BCEWithLogitsLoss)\r\n\r\n```javascript\r\nimport {\r\n  Sequential,\r\n  Linear,\r\n  ReLU,\r\n  BCEWithLogitsLoss,\r\n  Adam\r\n} from \"./src/jstorch.js\";\r\n\r\nconst model = new Sequential([\r\n  new Linear(2, 8),\r\n  new ReLU(),\r\n  new Linear(8, 1) // logit output\r\n]);\r\n\r\nconst X = [\r\n  [0,0], [0,1], [1,0], [1,1]\r\n];\r\nconst Y = [\r\n  [0], [1], [1], [0]\r\n];\r\n\r\nconst lossFn = new BCEWithLogitsLoss();\r\nconst optimizer = new Adam(model.parameters(), {lr: 0.1});\r\n\r\nfor (let epoch = 1; epoch <= 300; epoch++) {\r\n  const logits = model.forward(X);\r\n  const loss = lossFn.forward(logits, Y);\r\n  const grad = lossFn.backward();\r\n  model.backward(grad);\r\n  optimizer.step();\r\n  model.zeroGrad();\r\n  \r\n  if (epoch % 50 === 0) {\r\n    const probs = logits.map(p => 1 / (1 + Math.exp(-p[0])));\r\n    console.log(`Epoch ${epoch} | Loss: ${loss.toFixed(6)}`);\r\n    probs.forEach((prob, i) => {\r\n      const pred = prob > 0.5 ? 1 : 0;\r\n      console.log(`  [${X[i]}] → prob: ${prob.toFixed(4)} (${pred}) | target: ${Y[i][0]}`);\r\n    });\r\n    console.log('');\r\n  }\r\n}\r\n\r\nconsole.log(\"\\nTraining Complete\\n\");\r\nmodel.eval(); \r\n\r\nconst finalLogits = model.forward(X);\r\nconst finalProbs = finalLogits.map(p => 1 / (1 + Math.exp(-p[0])));\r\n\r\nconsole.log(\"Final Results:\");\r\nlet correct = 0;\r\nfinalProbs.forEach((prob, i) => {\r\n  const pred = prob > 0.5 ? 1 : 0;\r\n  const target = Y[i][0];\r\n  const isCorrect = pred === target;\r\n  if (isCorrect) correct++;\r\n  console.log(`  [${X[i]}] → ${prob.toFixed(4)} (${pred}) | target: ${target} ${isCorrect ? '✓' : '✗'}`);\r\n});\r\nconsole.log(`\\nAccuracy: ${(correct / X.length * 100).toFixed(2)}%`);\r\n```\r\n`Important:` Do not combine `BCEWithLogitsLoss` with a `Sigmoid` layer.\r\n\r\n---\r\n\r\n# Save & Load Models \r\n\r\n```javascript\r\nimport { saveModel, loadModel, Sequential } from \"./src/jstorch\";\r\n\r\n// Save trained model \r\nconst json = saveModel(model);\r\n\r\n// Create fresh model with same architecture and load weights \r\nconst model2 = new Sequential([\r\n    new Linear(2, 16), new ReLU(),\r\n    new Linear(16, 1)\r\n]);\r\nloadModel(model2, json);\r\n```\r\n\r\n---\r\n\r\n# Demos\r\n\r\nSee the `demo/` directory for runnable examples!\r\n- `demo/fu_fun.js`\r\n- `demo/MakeModel.js`\r\n- `demo/scheduler.js`\r\n- `demo/xor_classification.js`\r\n- `demo/linear_regression.js`\r\n- `demo/saveAndLoadModel.js`\r\n\r\n```bash\r\nnode demo/<fileNameInDemo>.js\r\n```\r\n**Make sure your directory while run this at root folder!**\r\n\r\n---\r\n\r\n# Design Notes & Limitations \r\n\r\n- Training logic is 2D-first: `[batch][features]`\r\n- Higher-dimensional data is reshaped internally by specific layers (e.g. Conv2D, Flatten)\r\n- No automatic broadcasting or autograd graph\r\n- Some components (Conv2D, BatchNorm2D, Dropout) are educational / experimental\r\n- Not intended for large-scale or production ML workloads\r\n\r\n---\r\n\r\n# License\r\n\r\nMIT License\r\n\r\nCopyright (c) 2024-2025\r\nrizal-editors\r\n\r\n---\r\n","readmeFilename":"README.md"}