https://github.com/lovesaroha/lnn.js
This is a generalized neural network package with clean and transparent API for the javascript.
https://github.com/lovesaroha/lnn.js
gradient-descent javascript javascript-library logistic-regression machine-learning neural-network sequential-models tensor
Last synced: 11 months ago
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This is a generalized neural network package with clean and transparent API for the javascript.
- Host: GitHub
- URL: https://github.com/lovesaroha/lnn.js
- Owner: lovesaroha
- License: gpl-3.0
- Created: 2021-09-19T11:01:37.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-09-19T11:02:28.000Z (over 4 years ago)
- Last Synced: 2025-01-12T19:23:34.828Z (12 months ago)
- Topics: gradient-descent, javascript, javascript-library, logistic-regression, machine-learning, neural-network, sequential-models, tensor
- Language: JavaScript
- Homepage:
- Size: 16.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# lnn.js
This is a generalized neural network package with clean and transparent API for the javascript. Also available for golang [github/lovesaroha/lnn](https://github.com/lovesaroha/lnn)
## Features
- Lightweight and Fast.
- Tensor Operations.
- Sequential Models.
- Support loss functions like (Mean Square Error).
- Opitmization algorithms like (Gradient Descent).
## Installation
```html
```
## Tensor Usage
### Create Tensor
```js
// Create tensor of given shape.
let tensor = lnn.Tensor([3, 4]);
// Print values.
tensor.Print();
```
### Random Tensor
```js
// Create tensor of given shape and (minimum, maximum).
let tensor = lnn.Tensor([3, 4] , -1 , 1);
// Print values.
tensor.Print();
// Scalar tensor.
let stensor = lnn.Tensor([], -1 , 1);
// Print values.
stensor.Print();
```
### Convert Array Or Values Into Tensor
```js
// Array to tensor and print values.
lnn.ToTensor([1, 2, 3]).Print();
// 2d array to tensor and print values.
lnn.ToTensor([[1, 2],[3, 4]]).Print();
// Value to tensor and print values.
lnn.ToTensor(5).Print();
```
### Tensor Element Wise Operations (Add, Subtract, Multiply, Divide)
```js
// Create a random tensor.
let tensor = lnn.Tensor([3, 4] , 10 , 20);
let tensorB = lnn.Tensor([3, 4] , 0 , 10);
// Add and print values.
tensor.Add(tensorB).Print();
// Subtract and print values.
tensor.Sub(tensorB).Print();
// Multiply and print values.
tensor.Mul(tensorB).Print();
// Divide and print values.
tensor.Sub(tensorB).Print();
```
### Tensor Element Wise Operations With Scalar Value (Add, Subtract, Multiply, Divide)
```js
// Create a random tensor.
let tensor = lnn.Tensor([3, 4] , 10 , 20);
let tensorB = lnn.ToTensor(4);
// Add and print values.
tensor.Add(tensorB).Print();
// Subtract and print values.
tensor.Sub(tensorB).Print();
// Multiply and print values.
tensor.Mul(tensorB).Print();
// Divide and print values.
tensor.Sub(tensorB).Print();
```
### Tensor Dot Product
```js
// Create a random tensor.
let tensor = lnn.Tensor([3, 4] , 10 , 20);
let tensorB = lnn.Tensor([4, 3] , 0 , 10);
// Dot product and print values.
tensor.Dot(tensorB).Print();
```
### Tensor Transpose
```js
// Create a random tensor.
let tensor = lnn.Tensor([3, 1] , 10 , 20);
// Print values.
tensor.Print();
// Transpose and print values.
tensor.Transpose().Print();
```
### Add All Column Values
```js
// Create a random tensor.
let tensor = lnn.Tensor([3, 3] , 10 , 20);
// Print values.
tensor.Print();
// Add columns and print values.
tensor.AddCols().Print();
```
### Change Tensor Values (Map)
```js
// Create a random tensor.
let tensor = lnn.Tensor([3, 3] , 0, 10);
// Print values.
tensor.Print();
// Square and print values.
tensor.Map(function(value) {
return value * value
}).Print();
```
## Model Usage
### Create Model
```js
// Create a model.
let model = lnn.Model();
```
### Add Layers In Model
```js
// Add layer to model with 4 units , Input shape (2) and activation function sigmoid.
model.AddLayer({inputShape: [2], units: 4, activation: "sigmoid"});
// Add another layer to model with 1 unit and activation function sigmoid.
model.AddLayer({units: 1});
```
### Model Configuration
```js
// Makes the model with given values of loss function , optimizer and learning rate.
model.Make({loss: "meanSquareError", optimizer: "sgd", learningRate: 0.2});
```
### Train Model
```js
// Trains the model with given configuration.
model.Train(inputs, outputs, {epochs: 1000, batchSize: 4, shuffle: true});
```
### Predict Output
```js
model.Predict(inputs);
```
## Examples
### XOR Gate Model Training
```js
// Create a model.
let model = lnn.Model()
// Add layer to model with 4 units , Input shape (2) and activation function sigmoid.
model.AddLayer({inputShape: [2], units: 4, activation: "sigmoid"});
// Add another layer to model with 1 unit and activation function sigmoid.
model.AddLayer({units: 1});
// Makes the model with given values of loss function , optimizer and learning rate.
model.Make({loss: "meanSquareError", optimizer: "sgd", learningRate: 0.2});
// Inputs and outputs as a tensor object.
let inputs = lnn.ToTensor([[1, 1, 0, 0],[1, 0, 1, 0]]);
let outputs = lnn.ToTensor([[0, 1, 1, 0]]);
// Trains the model with given configuration.
model.Train(inputs, outputs, {epochs: 5000, batchSize: 4, shuffle: true});
// Print values.
model.Predict(inputs).Print();
```
### Logistic Regression (OR Gate) With Tensor
```js
// Learning rate.
let learningRate = lnn.ToTensor(0.2);
let size = lnn.ToTensor(4);
// Inputs and outputs as a tensor object.
let inputs = lnn.ToTensor([[1, 1, 0, 0],[1, 0, 1, 0]]);
let outputs = lnn.ToTensor([[1, 1, 1, 0]]);
// Weights and bias.
let weights = lnn.Tensor([2, 1] , -1, 1);
let bias = lnn.Tensor([], -1, 1);
// Train weights and bias (epochs 1000).
for(let i = 0; i < 1000; i++) {
// Sigmoid(wx + b).
let prediction = weights.Transpose().Dot(inputs).Add(bias).Map(lnn.Sigmoid);
let dZ = prediction.Sub(outputs);
weights = weights.Sub(inputs.Dot(dZ.Transpose()).Divide(size).Mul(learningRate));
bias = bias.Sub(dZ.AddCols().Divide(size).Mul(learningRate));
}
// Show prediction.
weights.Transpose().Dot(inputs).Add(bias).Map(lnn.Sigmoid).Print();
```
