deepnet.js v1.1.2

deepnet.js

deepnet.js is an auto-differentiation library for javascript. it will compute the gradients in both static and dynamic method.

you can see the API-doc here.

:warning: Deepnet.js is reimplemented from ground to support sparse tensor, broadcast and various backends are in development, If you are using the older versions (1.0.2 or below), please upgrade your code accordingly.

Installation

NPM

npm install deepnet.js

CDN

https://unpkg.com/deepnet.js@latest/dist/deepnet-browser.min.js
https://unpkg.com/deepnet.js@latest/dist/deepnet-browser.js

Usage

Node

const deepnet = require("deepnet.js");

deepnet.platforms.cpu().then((backend) => {
    
    let dn = backend;

    const a = dn.tensor([1, 2, 3, 4], [2, 2]);
    const b = dn.tensor([1, 2, 3, 4], [2, 2]);
    
    const result = dn.matmul( a, b );
    result.print();

})

CDN

<script src="https://unpkg.com/deepnet.js@latest/dist/deepnet-browser.min.js"></script>
<script>

    deepnet.platforms.cpu().then((backend) => {
    
        let dn = backend;

        const a = dn.tensor([1, 2, 3, 4], [2, 2]);
        const b = dn.tensor([1, 2, 3, 4], [2, 2]);

        const result = dn.matmul( a, b );
        result.print();

    })

</script>

Support 🔥

If this package helps you, please consider supporting me. Your donation will inspire me to work on this project.

paypal.me

Thanks for considering to donate.

Getting started

Full API-doc

you can see the API-doc here.

Table of Contents

• Autodiff
• Platforms/Backends
• Tensor
• Operations
  • Broadcasting
  • basic operations
  • Matmul two Tensors
• fully connected example

Autodiff

Autodiff (Automatic Differentitation) is a technique which uses a computational graph to compute a derivatives automatically.

In the forward phase, it executes the math operation and constructs the computational graph, and In the backward phase, the dervatives are computed automatically.

Platforms/Backends

Platforms adds support for various environments to run your neural networks.

• cpu(..), (js-environment) pure js implementaion for the browser and nodejs.

deepnet.platforms.cpu().then((dn) => {        
    dn.tensor(..)
    dn.add(..)
    ...    
}); 

Tensor

A tensor is a scalar or a vector or a multidimensional array. it can be created using dn.tensor(..)

deepnet.platforms.cpu().then((dn) => {
    
    let dense = dn.tensor([1, 2, 3, 4], [2, 2], is_sparse = false);    
    dense.print();

    let sparse = dn.tensor([1, 2, 0, 4, 5, 6, 0, 0], [2, 2, 2], is_sparse = true);    
    sparse.print();

});

Contains:

• value Initial value for the tensor.

  • data Array of numbers.

  • shape The shape of the tensor. if it is not defined, it will be automatically found from data.

• grad Stores the derivation of the tensor, it will be filled while backpass().

  • data Array of numbers.

  • shape The shape of the grad. if it is not defined, it will be automatically found from data.

• parents Stores the Parents (vertex[]), from which it is created.

• print() prints the tensor.

• feed() Recalculates the values respectively, it will fills the value.

• back() Calculates the derivation, it will fills the grad.

• is_sparse it is used specify whether the tensor to be created is sparse or dense. default false.

Operations

Broadcasting

deepnet.js supports broadcasting, no copy or temporary variables are created.

basic_operation

deepnet.platforms.cpu().then((dn) => {
    
    // supports broadcasting
    const a = dn.ones([2, 2, 2]);
    const b = dn.zeros([2, 2]);
        
    const res = dn.add(a, b);
    // dn.sub(..), dn.mul(..), dn.div(..) 

    res.print();
    
})

Output:

Tensor
[[[1 1]
  [1 1]]

 [[1 1]
  [1 1]]]

Matmul_two_Tensors

deepnet.platforms.cpu().then((dn) => {
    
    // supports broadcasting
    const a = dn.ones([2, 2, 2]);
    const b = dn.ones([2, 2]);
        
    const res = dn.matmul(a, b);
    res.print();
    
})

Output:

Tensor
[[[2 2]
  [2 2]]

 [[2 2]
  [2 2]]]

fully_connected_example

const deepnet = require("deepnet.js");

(async () => {  

    // importing the "cpu" class
    let dn = await deepnet.platforms.cpu();

    // declaring input
    let a = dn.tensor([1, 2, 3, 4], [2, 2]);

    // declaring weights
    let w = dn.randn([2, 2]);
    let b = dn.randn([2, 2]);

    // single nn layer - (linear layer)
    // returns sigmoid( a @ w + b )
    const feed = (a, w, b) => {
        let mat_res = dn.matmul(a, w);
        let added = dn.add(mat_res, b);
        return dn.sig(added);
    }

    // Stochastic gradient descent optimizer
    let optm = dn.optimizer.SGD([w, b], 0.04);
    
    for (let i = 0; i < 300; i++) {
        
        let result = feed(a, w, b);
        let loss = dn.sub(result, dn.tensor([0, 1, 0, 1], [2, 2]));              
        
        dn.backpass(result, loss); // calculating derivatives
        optm.step(); // updating weights
        dn.grad_zero(result); // reseting the grad

    }

    // Predicting the values
    let pred = feed(a, w, b);
    pred.print();

})();

Output:

Tensor
[[0.08579222069069875 0.905562796140377]
 [0.005928221665889459 0.9933268076751989]]