This library provides easy access to the MNIST Handwritten Digits dataset to test and train neural networks in Javascript. It can work directly in browser or through node.js.
The training set can include up to 60,000 digits and the test set up to 10,000. You can also limit the digits included as well (e.g. creating training and test sets with just the numbers 1 and 2 for easier debugging).
for node.js: npm install mnist-javascript --save
for the browser: bower install mnist-javascript --save
To use, construct an instance of the MNIST class. All functionality is accessed through this class.
// All parameters set to default values
const mnist = new MNIST();
// Specifying all parameters to custom values
const mnist = new MNIST({trainCount: 30000, testCount: 5000, includedDigits: [1, 3, 5], batchSize: 10});trainCount [optional]: number
- The number (between 0 and 60,000) of digit samples you want in your training set, chosen randomly.
default = 60,000
testCount [optional]: number
- The number (between 0 and 10,000) of digit samples you want in your test set, chosen randomly.
default = 10,000
includedDigits [optional]: number[]
- The digits you want included in your sets (e.g. [1,3,5,7,9] if you just wanted odd digits to train and test with)
- Note: Specifying a subset of digits means the number of total samples you'll have access to will decrease.
default = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
batchSize [optional]: number
- The number of samples in each training batch (see batches below)
default = 100
Each training and test sample is a basic object with two keys:
input: 784 grayscale pixel values (normalized)output: an array of length 10 with a1in the spot corresponding to the label for the digit
const threeSample = {
input: [0, 0.5, 0.3, ..., 0.8, 1, 0, 0], // 784 values, one for each pixel
output: [0, 0, 0, 1, 0, 0, 0, 0, 0] // since this sample is a three, it has a 1 at that index
};trainSamples: Sample[] All the training samples in random order (ignores batching)
nextBatch() - returns a Sample[] of the next batch, of length batchSize or null if no batches remain.
hasBatch() - returns true if there are still batches to iterate over, false otherwise.
resetBatches() - resets the internal batch index so calling nextBatch() will start over with the first training batch.
testSamples: Sample[] All the test samples in random order
nextTest() - returns the next test as a Sample or null if no tests remain.
hasTest() - returns true if there are still tests to iterate over, false otherwise.
resetTests() - resets the internal test index so calling nextTest() will start over with the first Sample.
- Construct a MNIST instance to test and train your NN using batches
const network = new NeuralNetwork(); // example of a NN class to train and test
const mnist = new mnist();
// Train network by running through all batches
while (mnist.hasBatch()) {
let batch = mnist.nextBatch();
network.train(batch);
}
// Test the network
network.test(mnist.testSamples);- Construct a MNIST instance to test and train your NN without batches
const network = new NeuralNetwork(); // example of a NN class to train and test
const mnist = new mnist({trainCount: 55000});
// Train using all training samples at once
network.train(mnist.trainingSamples);
// Test the network
network.test(mnist.testSamples);- Construct a MNIST instance and iterate over test samples
const network = new NeuralNetwork(); // example of a NN class to train and test
const mnist = new mnist({trainingCount: 55000});
// Train using all training samples at once
network.train(mnist.trainingSamples);
// Test the network
while (mnist.hasTest()) {
network.testSingleSample(mnist.nextTest());
}You can grab a specified number of samples of a particular digit (randomly chosen from the training set)
const mnist = new mnist();
const digit = 5;
const count = 2;
const samples = mnist.getDigit(digit, count);digit: number The digit you want samples of (between 0 and 9)
count [optional]: number The number of samples you want (defaults to 1)
return: An array of count samples.
You can have MNIST render a sample digit to an HTML canvas if you provide the canvas context.
const mnist = new mnist();
const ctx = window.getElementById("myCanvas").getContext('2d'); // grab a canvas element
const sampleThree = mnist.getDigit(3)[0]; // grab a random sample 3 to render
// Draw an image at location (0, 0) at 1x the size
mnist.drawDigit(sampleThree.input, ctx);
const xOffset = 100;
const yOffest = 50;
const scale = 3;
// Draw an image at location (100, 50) at 3x the size
mnist.drawDigit(sampleThree.input, ctx, xOffset, yOffset, scale);pixels: number[] An array of 784 pixel values to render (the input of a sample)
ctx: CanvasRenderingContext2D A canvas rendering context, can get using getContext(2d)
x [optional]: number X offset into the canvas for the image
y [optional]: number Y offset into the canvas for the image
scale [optional]: number A scalar applied to the image. It's set to 1 by default.