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logistic-regression.js
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logistic-regression.js
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/** Standard imports. */
const tf = require("@tensorflow/tfjs");
const _ = require("lodash");
/** Main Logistic Regression class that exposes different methods in the context of the model.
* @param features: 28*28 pixels images.
* @param labels: labels.
* @param options: model attributes.
*/
class LogisticRegression {
constructor(features, labels, options) {
this.features = this.processFeatures(features);
this.labels = tf.tensor(labels);
/** cost function, scientific referring to cross entropy */
this.costHistory = [];
this.options = Object.assign(
/**default model attributes */
{
learningRate: 0.1,
iterations: 1000,
batchSize: 10,
decisionBoundary: 0.5,
},
options
);
/** Initialize weights. */
this.weights = tf.zeros([this.features.shape[1], this.labels.shape[1]]);
}
gradientDescent(features, labels) {
/**
* Calculate the gradients of the Mean Squared Error (MSE) with respect to weights (including bias).
* Formula: (transpose of Features * ((Features * Weights) - Labels)) / n * 2
*/
const currentGuesses = features.matMul(this.weights).softmax();
const differences = currentGuesses.sub(labels);
const slopes = features
.transpose() // Transpose of the feature matrix
.matMul(differences) // Dot product with prediction errors
.div(features.shape[0]); // Average over the number of data points
return this.weights.sub(slopes.mul(this.options.learningRate));
}
/** Method to train the model. */
train() {
const batchQuantity = Math.floor(
this.features.shape[0] / this.options.batchSize
);
for (let i = 0; i < this.options.iterations; i++) {
/** Implement batch gradient descent using tensorflowjs. */
for (let j = 0; j < batchQuantity; j++) {
const startIndex = j * this.options.batchSize;
const { batchSize } = this.options;
this.weights = tf.tidy(() => {
const featureSlice = this.features.slice(
[startIndex, 0],
[batchSize, -1]
);
const labelSlice = this.labels.slice(
[startIndex, 0],
[batchSize, -1]
);
return this.gradientDescent(featureSlice, labelSlice);
});
}
debugger;
this.recordCost();
this.updateLearningRate();
}
}
/** Method to make a prediction using the trained model.
* @param: A 2D array representing image pixels.
*/
predict(observations) {
/** Standardize observations */
return this.processFeatures(observations)
.matMul(this.weights)
.softmax()
.argMax(1);
}
test(testFeatures, testLabels) {
const predictions = this.predict(testFeatures);
testLabels = tf.tensor(testLabels).argMax(1);
const incorrect = predictions.notEqual(testLabels).sum().arraySync();
/** % correct: (total predictions - Incorrect Predictions)/ total predictions */
return (predictions.shape[0] - incorrect) / predictions.shape[0];
}
processFeatures(features) {
features = tf.tensor(features);
if (this.mean && this.variance) {
features = features.sub(this.mean).div(this.variance.pow(0.5));
} else {
features = this.standardize(features);
}
features = tf.ones([features.shape[0], 1]).concat(features, 1);
return features;
}
standardize(features) {
const { mean, variance } = tf.moments(features, 0);
/** Backfilling variance */
const filler = variance.cast("bool").logicalNot().cast("float32");
this.mean = mean;
this.variance = variance.add(filler);
return features.sub(mean).div(this.variance.pow(0.5));
}
recordCost() {
const cost = tf.tidy(() => {
/** Calculate cross entropy. */
const guesses = this.features.matMul(this.weights).sigmoid();
const termOne = this.labels.transpose().matMul(guesses.add(1e-7).log());
const termTwo = this.labels.mul(-1).add(1).transpose().matMul(
guesses
.mul(-1)
.add(1)
.add(1e-7) // Add a constant to avoid log(0).
.log()
);
return termOne
.add(termTwo)
.div(this.features.shape[0])
.mul(-1)
.arraySync();
});
this.costHistory.unshift(cost);
}
updateLearningRate() {
if (this.costHistory.length < 2) {
return;
}
if (this.costHistory[0] > this.costHistory[1]) {
this.options.learningRate /= 2;
} else {
this.options.learningRate *= 1.05;
}
}
}
module.exports = LogisticRegression;