Clone the repo and drop your module in the src directory.
# Install Prerequisites
$ npm install rollup typedoc jest sitedown --g$ npm run build #builds type declarations, created bundled artifacts with rollup and generates documenationThis library is a compilation of model building modules with a consistent API for quickly implementing Tensorflow at edge(browser) or any JavaScript environment (Node JS / GPU).
- Deep Learning Classification:
DeepLearningClassification - Logistic Regression:
LogisticRegression
- Deep Learning Regression:
DeepLearningRegression - Multivariate Linear Regression:
MultipleLinearRegression
- Multi-Layered Perceptrons:
BaseNeuralNetwork
- Long Short Term Memory Time Series:
LSTMTimeSeries - Long Short Term Memory Multivariate Time Series:
LSTMMultivariateTimeSeries
TensorScript is and ECMA Script module designed to be used in an ES2015+ environment, if you need compiled modules for older versions of node use the compiled modules in the bundle folder.
Please read more on tensorflow configuration options, specifying epochs, and using custom layers in configuration.
import * as tf from '@tensorflow/tfjs-node';
import { MultipleLinearRegression, DeepLearningRegression, setBackend } from '@jsonstack/model';
import ms from 'modelscript';
//setup jsonstack model tensorflow
setBackend(tf);
async function main(){
const independentVariables = [ 'sqft', 'bedrooms',];
const dependentVariables = [ 'price', ];
const housingdataCSV = await ms.csv.loadCSV('./test/mock/data/portland_housing_data.csv');
const DataSet = new ms.DataSet(housingdataCSV);
const x_matrix = DataSet.columnMatrix(independentVariables);
const y_matrix = DataSet.columnMatrix(dependentVariables);
const MLR = new MultipleLinearRegression();
await MLR.train(x_matrix, y_matrix);
const DLR = new DeepLearningRegression();
await DLR.train(x_matrix, y_matrix);
//1600 sqft, 3 bedrooms
await MLR.predict([1650,3]); //=>[293081.46]
await DLR.predict([1650,3]); //=>[293081.46]
}
main();import * as tf from '@tensorflow/tfjs';
import { DeepLearningClassification, setBackend } from '@jsonstack/model';
import ms from 'modelscript';
//setup jsonstack model tensorflow
setBackend(tf);
async function main(){
const independentVariables = [
'sepal_length_cm',
'sepal_width_cm',
'petal_length_cm',
'petal_width_cm',
];
const dependentVariables = [
'plant_Iris-setosa',
'plant_Iris-versicolor',
'plant_Iris-virginica',
];
const housingdataCSV = await ms.csv.loadCSV('./test/mock/data/iris_data.csv');
const DataSet = new ms.DataSet(housingdataCSV).fitColumns({ columns: {plant:'onehot'}, });
const x_matrix = DataSet.columnMatrix(independentVariables);
const y_matrix = DataSet.columnMatrix(dependentVariables);
const nnClassification = new DeepLearningClassification();
await nnClassification.train(x_matrix, y_matrix);
const input_x = [
[5.1, 3.5, 1.4, 0.2, ],
[6.3, 3.3, 6.0, 2.5, ],
[5.6, 3.0, 4.5, 1.5, ],
[5.0, 3.2, 1.2, 0.2, ],
[4.5, 2.3, 1.3, 0.3, ],
];
const predictions = await nnClassification.predict(input_x);
const answers = await nnClassification.predict(input_x, { probability:false, });
/*
predictions = [
[ 0.989512026309967, 0.010471616871654987, 0.00001649192017794121, ],
[ 0.0000016141033256644732, 0.054614484310150146, 0.9453839063644409, ],
[ 0.001930746017023921, 0.6456733345985413, 0.3523959517478943, ],
[ 0.9875779747962952, 0.01239941269159317, 0.00002274810685776174, ],
[ 0.9545140862464905, 0.04520365223288536, 0.0002823179238475859, ],
];
answers = [
[ 1, 0, 0, ], //setosa
[ 0, 0, 1, ], //virginica
[ 0, 1, 0, ], //versicolor
[ 1, 0, 0, ], //setosa
[ 1, 0, 0, ], //setosa
];
*/
}
main();import * as tf from '@tensorflow/tfjs';
import { LogisticRegression, setBackend } from '@jsonstack/model';
import ms from 'modelscript';
//setup jsonstack model tensorflow
setBackend(tf);
async function main(){
const independentVariables = [
'Age',
'EstimatedSalary',
];
const dependentVariables = [
'Purchased',
];
const housingdataCSV = await ms.csv.loadCSV('./test/mock/data/social_network_ads.csv');
const DataSet = new ms.DataSet(housingdataCSV).fitColumns({ columns: {Age:['scale','standard'],
EstimatedSalary:['scale','standard'],}, });
const x_matrix = DataSet.columnMatrix(independentVariables);
const y_matrix = DataSet.columnMatrix(dependentVariables);
const LR = new LogisticRegression();
await LR.train(x_matrix, y_matrix);
const input_x = [
[-0.062482849427819266, 0.30083326827486173,], //0
[0.7960601198093905, -1.1069168538010206,], //1
[0.7960601198093905, 0.12486450301537644,], //0
[0.4144854668150751, -0.49102617539282206,], //0
[0.3190918035664962, 0.5061301610775946,], //1
];
const predictions = await LR.predict(input_x); // => [ [ 0 ], [ 0 ], [ 1 ], [ 0 ], [ 1 ] ];
}
main();import * as tf from '@tensorflow/tfjs';
import { LSTMTimeSeries, setBackend } from '@jsonstack/model';
import ms from 'modelscript';
//setup jsonstack model tensorflow
setBackend(tf);
async function main(){
const dependentVariables = [
'Passengers',
];
const airlineCSV = await ms.csv.loadCSV('./test/mock/data/airline-sales.csv');
const DataSet = new ms.DataSet(airlineCSV);
const x_matrix = DataSet.columnMatrix(independentVariables);
const TS = new LSTMTimeSeries();
await TS.train(x_matrix);
const forecastData = TS.getTimeseriesDataSet([ [100 ], [200], [300], ])
await TS.predict(forecastData.x_matrix); //=>[200,300,400]
}
main();MIT