Deep Learning Classification, Clustering and Regression with Tensorflow
npm install @tensorscript/ts-deeplearning 
Deep Learning Classification and Regression with Tensorflow (Clustering coming soon)
``sh`
$ npm i @tensorscript/ts-deeplearning
#### Classification
Test against the Iris Flower Data Set
`javascript
import { DeepLearningClassification, } from '@tensorscript/ts-deeplearning';
import ms from 'modelscript';
async function main(){
const irisFlowerDataCSV = await ms.csv.loadCSV('./test/mock/data/iris_data.csv');
const DataSet = new ms.DataSet(irisFlowerDataCSV);
/**
* encodedData = [
* { sepal_length_cm: 5.1,
sepal_width_cm: 3.5,
petal_length_cm: 1.4,
petal_width_cm: 0.2,
plant: 'Iris-setosa',
'plant_Iris-setosa': 1,
'plant_Iris-versicolor': 0,
'plant_Iris-virginica': 0 },
...
{ sepal_length_cm: 5.9,
sepal_width_cm: 3,
petal_length_cm: 4.2,
petal_width_cm: 1.5,
plant: 'Iris-versicolor',
'plant_Iris-setosa': 0,
'plant_Iris-versicolor': 1,
'plant_Iris-virginica': 0 },
];
*/
const encodedData = DataSet.fitColumns({
columns: [
{
name: 'plant',
options: {
strategy: 'onehot',
},
},
],
returnData:true,
});
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 x_matrix = DataSet.columnMatrix(independentVariables);
const y_matrix = DataSet.columnMatrix(dependentVariables);
/*
x_matrix = [
[ 5.1, 3.5, 1.4, 0.2 ],
[ 4.9, 3, 1.4, 0.2 ],
[ 4.7, 3.2, 1.3, 0.2 ],
...
];
y_matrix = [
[ 1, 0, 0 ],
[ 1, 0, 0 ],
[ 1, 0, 0 ],
...
]
*/
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 nnClassification = new DeepLearningClassification();
const nnClassificationModel = await nnClassification.train(x_matrix, y_matrix);
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, ],
[ 0, 0, 1, ],
[ 0, 1, 0, ],
[ 1, 0, 0, ],
[ 1, 0, 0, ],
];
*/
}
main();
`
#### Regression
Test against the Boston Housing Data Set
`javascript
import { DeepLearningRegression, } from '@tensorscript/ts-deeplearning';
import ms from 'modelscript';
function scaleColumnMap(columnName) {
return {
name: columnName,
options: {
strategy: 'scale',
scaleOptions: {
strategy:'standard'
}
}
}
}
async function main(){
const housingdataCSV = await ms.csv.loadCSV('./test/mock/data/boston_housing_data.csv');
/*
housingdataCSV = [
{ CRIM: 0.00632, ZN: 18, INDUS: 2.31, CHAS: 0, NOX: 0.538, RM: 6.575, AGE: 65.2, DIS: 4.09, RAD: 1, TAX: 296, PTRATIO: 15.3, B: 396.9, LSTAT: 4.98, MEDV: 24 },
{ CRIM: 0.02731, ZN: 0, INDUS: 7.07, CHAS: 0, NOX: 0.469, RM: 6.421, AGE: 78.9, DIS: 4.9671, RAD: 2, TAX: 242, PTRATIO: 17.8, B: 396.9, LSTAT: 9.14, MEDV: 21.6 },
...
]
*/
const DataSet = new ms.DataSet(housingdataCSV);
const independentVariables = [
'CRIM',
'ZN',
'INDUS',
'CHAS',
'NOX',
'RM',
'AGE',
'DIS',
'RAD',
'TAX',
'PTRATIO',
'B',
'LSTAT',
];
const dependentVariables = [
'MEDV',
];
const columns = independentVariables.concat(dependentVariables);
DataSet.fitColumns({
columns: columns.map(scaleColumnMap),
returnData:false,
});
const x_matrix = DataSet.columnMatrix(independentVariables);
const y_matrix = DataSet.columnMatrix(dependentVariables);
/* x_matrix = [
[ -0.41936692921321594, 0.2845482693404666, -1.2866362317172035, -0.272329067679207, -0.1440748547324509, 0.4132629204530747, -0.119894767215809, 0.1400749839795629, -0.981871187861867, -0.6659491794887338, -1.457557967289609, 0.4406158949991029, -1.074498970343932 ],
[ -0.41692666996409716, -0.4872401872268264, -0.5927943782429392, -0.272329067679207, -0.7395303607434242, 0.1940823874370036, 0.3668034264326209, 0.5566090495704026, -0.8670244885881488, -0.9863533804386945, -0.3027944997494681, 0.4406158949991029, -0.49195252491856634 ]
...
];
y_matrix = [
[ 0.15952778852449556 ],
[ -0.1014239172731213 ],
...
];
const y_vector = ms.util.pivotVector(y_matrix)[ 0 ];// not used but just illustrative
y_vector = [ 0.15952778852449556, -0.1014239172731213, ... ]
*/
const input_x = [
[-0.41936692921321594, 0.2845482693404666, -1.2866362317172035, -0.272329067679207, -0.1440748547324509, 0.4132629204530747, -0.119894767215809, 0.1400749839795629, -0.981871187861867, -0.6659491794887338, -1.457557967289609, 0.4406158949991029, -1.074498970343932,],
[-0.41692666996409716, -0.4872401872268264, -0.5927943782429392, -0.272329067679207, -0.7395303607434242, 0.1940823874370036, 0.3668034264326209, 0.5566090495704026, -0.8670244885881488, -0.9863533804386945, -0.3027944997494681, 0.4406158949991029, -0.49195252491856634,],
];
const nnRegression = new DeepLearningRegression();
const model = await nnRegression.train(x_matrix, y_matrix);
const predictions = await nnRegressionWide.predict(input_x); // [ [ 0.43396109342575073 ], [ 0.12437985092401505 ] ]
const predictions_unscaled = predictions.map(pred=>DataSet.scalers.get('MEDV').descale(pred[0])); //[ 26.523991670220486, 23.67674075943165 ]
}
main();
`
#### Multiple Linear Regression
Test against the Portland housing price dataset
`javascript
import { MultipleLinearRegression, } from '@tensorscript/ts-deeplearning';
import ms from 'modelscript';
function scaleColumnMap(columnName) {
return {
name: columnName,
options: {
strategy: 'scale',
scaleOptions: {
strategy:'standard'
}
}
}
}
async function main(){
const housingdataCSV = await ms.csv.loadCSV('./test/mock/data/portland_housing_data.csv');
/*
housingdataCSV = [
{ sqft: 2104, bedrooms: 3, price: 399900 },
{ sqft: 1600, bedrooms: 3, price: 329900 },
...
{ sqft: 1203, bedrooms: 3, price: 239500 }
]
*/
const DataSet = new ms.DataSet(housingdataCSV);
DataSet.fitColumns({
columns: [
'sqft',
'bedrooms',
'price',
].map(scaleColumnMap),
returnData:true,
});
const independentVariables = [ 'sqft', 'bedrooms',];
const dependentVariables = [ 'price', ];
const x_matrix = DataSet.columnMatrix(independentVariables);
const y_matrix = DataSet.columnMatrix(dependentVariables);
/* x_matrix = [
[2014, 3],
[1600, 3],
];
y_matrix = [
[399900],
[329900],
];
const y_vector = ms.util.pivotVector(y_matrix)[ 0 ];// not used but just illustrative
// y_vector = [ 399900, 329900]
*/
const testSqft = DataSet.scalers.get('sqft').scale(1650);
const testBedrooms = DataSet.scalers.get('bedrooms').scale(3);
const input_x = [
testSqft,
testBedrooms,
]; // input_x: [ -0.4412732005944351, -0.2236751871685913 ]
const tfMLR = new MultipleLinearRegression();
const model = await tfMLR.train(x_matrix, y_matrix);
const scaledPrediction = await tfMLR.predict(input_x); // [ -0.3785287367962629 ]
const prediction = DataSet.scalers.get('price').descale(scaledPrediction); // prediction: 293081.4643348962
}
main();
`
#### Logistic Regression
Test against the Social Media Ads
`javascript
import { LogisticRegression, } from '@tensorscript/ts-deeplearning';
import ms from 'modelscript';
function scaleColumnMap(columnName) {
return {
name: columnName,
options: {
strategy: 'scale',
scaleOptions: {
strategy:'standard'
}
}
}
}
async function main(){
const CSVData = await ms.csv.loadCSV('./test/mock/data/social_network_ads.csv');
const DataSet = new ms.DataSet(CSVData);
const scaledData = DataSet.fitColumns({
columns: independentVariables.map(scaleColumnMap),
returnData:true,
});
/*
scaledData = [
{ 'User ID': 15624510,
Gender: 'Male',
Age: -1.7795687879022388,
EstimatedSalary: -1.4881825118632386,
Purchased: 0 },
{ 'User ID': 15810944,
Gender: 'Male',
Age: -0.253270175924977,
EstimatedSalary: -1.458854384319991,
Purchased: 0 },
...
];
*/
const independentVariables = [
'Age',
'EstimatedSalary',
];
const dependentVariables = [
'Purchased',
];
const x_matrix = DataSet.columnMatrix(independentVariables);
const y_matrix = DataSet.columnMatrix(dependentVariables);
/*
x_matrix = [
[ -1.7795687879022388, -1.4881825118632386 ],
[ -0.253270175924977, -1.458854384319991 ],
...
];
y_matrix = [
[ 0 ],
[ 0 ],
...
];
*/
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 tfLR = new LogisticRegression();
const model = await tfLR.train(x_matrix, y_matrix);
const prediction = await tfLR.predict(input_x); // => [ [ 0 ], [ 0 ], [ 1 ], [ 0 ], [ 1 ] ],
}
main();
`
`sh`
$ npm i
$ npm test
Fork, write tests and create a pull request!
As of Node 8, ES modules are still used behind a flag, when running natively as an ES module
`sh``
$ node --experimental-modules my-machine-learning-script.mjsAlso there are native bindings that require Python 2.x, make sure if you're using Andaconda, you build with your Python 2.x bin
$ npm i --python=/usr/bin/python
License
----
MIT