https://github.com/salieri/tartarus-deep

Deep learning framework for TypeScript
https://github.com/salieri/tartarus-deep

ai artificial-intelligence deep-learning deeplearning machine-learning ml neural-network neural-networks nn typescript

Last synced: 4 months ago
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Deep learning framework for TypeScript

• Host: GitHub
• URL: https://github.com/salieri/tartarus-deep
• Owner: salieri
• License: mit
• Created: 2018-10-02T17:26:25.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-30T17:34:42.000Z (over 3 years ago)
• Last Synced: 2025-08-04T20:30:33.932Z (10 months ago)
• Topics: ai, artificial-intelligence, deep-learning, deeplearning, machine-learning, ml, neural-network, neural-networks, nn, typescript
• Language: TypeScript
• Homepage:
• Size: 1.9 MB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
# Tartarus Deep Learning Framework

**Deep learning framework for TypeScript.** Run it on a browser, on AWS Lambda, or on anything that runs Node.js!

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## Features

From-the-ground-up implementation for:

*   **Math:** Vector and Matrix operations, seeded randomization

*   **Graph:** Acyclic networks, automated data routing, support for multiple

    input and output layers

*   **Machine Learning:** Forward and back propagation, logistic regression,

    gradient descent, loss (cost) functions, activation functions, optimizers,

    metrics, dense layers, concat layers

## Goals

*   From-the-ground-up implementation for all standard deep learning operations

*   Compatibility with Node.js, modern browsers, and AWS Lambda

## Example

```ts

import { Model, Dense, MemoryInputFeed } from '@tartarus/deep';

/*

 * 1. Define a model 

 *    - 4 input nodes

 *    - hidden layer with 5 nodes and sigmoid activation

 *    - output layer with 3 nodes and softmax activation 

 */

const model = new Model({ optimizer: 'stochastic', loss: 'mean-squared-error' });

model

  .input(4)

  .push(new Dense({ units: 5, activation: 'sigmoid' }))

  .push(new Dense({ units: 3, activation: 'softmax' }));

model.compile()

  .then(

    async () => {

      /* 2. Prepare three samples of training data */

      const feed = new MemoryInputFeed();

      

      feed

        .add([1, 2, 3, 4], [1, 0, 0]) // .add(input, expected output)

        .add([4, 3, 2, 1], [0, 1, 0])

        .add([5, 6, 7, 8], [0, 0, 1]);

      

      

      /* 3. Train model */

      await model.fit(feed, { batchSize: 1, epochs: 100 });

      

      

      /* 4. Predict */

      const result = await model.predict([8, 9, 10, 11]);

      

      console.log(`Prediction: ${result.getDefaultValue().toJSON()}`);

    }

  );

```

## Model

## Layers

### Dense Layer

### Concat Layer

## Supplementary

### Activation Functions

### Loss Functions

### Initializers

### Metrics

### Input