itchml

v1.0.0-alpha.6

Published

21 days ago

TypeScript & Type-Safe Machine Learning Library

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suhaotian

itchml Machine Learning TypeScript Type Safe Tensor Linear ReLU MLP Loss Functions Optimizer Tanh Transformer MNIST XOR

ItchML

ItchML is an TypeScript module for running machine learning in the browser or Node.js environment. It is not intended as a replacement for TensorFlow or PyTorch. Instead, ItchML provides a minimal implementation of core modern machine learning concepts, designed for educational purposes or experimental projects directly in the browser.

What ItchML Is Not

It is not intended for training large models in the browser.
It does not aim to compete with TensorFlow or PyTorch.
It avoids unnecessary machine learning modules or features.

ItchML focuses on simplicity, accessibility, and learning, making it an ideal foundation for students, educators, and developers experimenting with ML in web-based environments.

Install

npm i itchml

Usage

import {
  Tensor,
  tensor,
  makeTensor,
  shapeSize,

  //
  Sequential,
  Linear,
  ReLU,
  Softmax,
  LayerNorm,
  type Layer,

  //
  SGD,
  Adam,
  mseLoss,
  crossEntropyLoss,
  params,

  //
  scaledDotProductAttention,
  multiHeadAttention,

  //
  serialize,
  deserialize,

  //
  Conv2d,
  convOutputSize,
  BatchNorm2d,
  maxPool2d,
  flatten4d,
  relu4d,
} from "itchml";

Tensor

Create type safe tensor:

import { tensor } from "itchml";

const A = tensor([1, 2, 3, 4, 5, 6], [2, 3] as const);
const B = tensor([1, 2, 3, 4, 5, 6], [2, 3] as const);
const C = tensor([
  [1, 2],
  [3, 4],
  [5, 6],
]);
const D = tensor([
  [1, 2],
  [3, 4],
  [5, 6],
] as const);
const E = tensor<3, 2>([
  [1, 2],
  [3, 4],
  [5, 6],
]);

Linear & MLP & XOR

import { Sequential, Linear, ReLU, SGD, tensor, mseLoss } from "itchml";
import * as fs from "fs";

const CHECKPOINT = "./xor-checkpoint.json";

const X = [
  tensor([0, 0], [1, 2] as const),
  tensor([0, 1], [1, 2] as const),
  tensor([1, 0], [1, 2] as const),
  tensor([1, 1], [1, 2] as const),
];
const Y = [0, 1, 1, 0];

const model = new Sequential([
  new Linear(2, 8),
  new ReLU<8>(),
  new Linear(8, 1),
]);
const opt = new SGD(model.parameters(), 0.15);

// Load checkpoint if exists, otherwise train
if (fs.existsSync(CHECKPOINT)) {
  model.load(JSON.parse(fs.readFileSync(CHECKPOINT, "utf-8")));
  console.log("Loaded checkpoint.");
} else {
  for (let epoch = 1; epoch <= 1000; epoch++) {
    let loss = 0;
    for (let s = 0; s < 4; s++) {
      const pred = model.forward(X[s]);
      const l = mseLoss(pred, tensor([Y[s]], [1, 1] as const));
      loss += l.data[0];
      l.backward();
      opt.step();
      opt.zeroGrad();
    }
    if (epoch % 100 === 0)
      console.log(`epoch ${epoch} | loss ${(loss / 4).toFixed(4)}`);
  }
  fs.writeFileSync(CHECKPOINT, JSON.stringify(model.serialize()), "utf-8");
}

// Eval
for (let s = 0; s < 4; s++) {
  const pred = model.forward(X[s]).data[0];
  console.log(`[${Y[s]}] pred=${pred.toFixed(3)} rounded=${Math.round(pred)}`);
}

Browser Usage

<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <title>XOR Training</title>
    <style>
      body {
        font-family: monospace;
        background: #0f0f0f;
        color: #ccc;
        display: flex;
        flex-direction: column;
        align-items: center;
        justify-content: center;
        height: 100vh;
        margin: 0;
        gap: 1rem;
      }
      h1 {
        color: #7effa0;
        font-size: 1.4rem;
        margin: 0;
      }
      button {
        background: #1e1e1e;
        color: #7effa0;
        border: 1px solid #7effa0;
        padding: 0.4rem 1.2rem;
        font-family: monospace;
        cursor: pointer;
        border-radius: 4px;
      }
      button:hover {
        background: #7effa0;
        color: #0f0f0f;
      }
      p {
        color: #555;
        font-size: 0.85rem;
        margin: 0;
      }
    </style>
  </head>
  <body>
    <h1>XOR Training</h1>
    <button
      onclick="localStorage.removeItem('xor-checkpoint'); location.reload()">
      Reset
    </button>
    <p>Open DevTools console (F12) to see training logs and eval output.</p>
    <script type="module">
      import {
        Sequential,
        Linear,
        ReLU,
        SGD,
        tensor,
        mseLoss,
      } from "https://esm.sh/itchml";

      const X = [
        tensor([0, 0], [1, 2]),
        tensor([0, 1], [1, 2]),
        tensor([1, 0], [1, 2]),
        tensor([1, 1], [1, 2]),
      ];
      const Y = [0, 1, 1, 0];

      const model = new Sequential([
        new Linear(2, 8),
        new ReLU(),
        new Linear(8, 1),
      ]);
      const opt = new SGD(model.parameters(), 0.15);

      const saved = localStorage.getItem("xor-checkpoint");
      if (saved) {
        model.load(JSON.parse(saved));
        console.log("Loaded checkpoint.");
      } else {
        for (let epoch = 1; epoch <= 500; epoch++) {
          let loss = 0;
          for (let s = 0; s < 4; s++) {
            const pred = model.forward(X[s]);
            const l = mseLoss(pred, tensor([Y[s]], [1, 1]));
            loss += l.data[0];
            l.backward();
            opt.step();
            opt.zeroGrad();
          }
          if (epoch % 50 === 0)
            console.log(`epoch ${epoch} | loss ${(loss / 4).toFixed(4)}`);
        }
        localStorage.setItem(
          "xor-checkpoint",
          JSON.stringify(model.serialize())
        );
      }

      for (let s = 0; s < 4; s++) {
        const pred = model.forward(X[s]).data[0];
        console.log(
          `[${Y[s]}] pred=${pred.toFixed(3)} rounded=${Math.round(pred)}`
        );
      }
    </script>
  </body>
</html>

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Readme