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justadudewhohacksjustadudewhohacks
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implemented faster normalize to reduce prediction time
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8 files changed

+27
-25
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8 files changed

+27
-25
lines changed

‎dist/face-recognition.min.js

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‎dist/faceRecognitionNet/index.js

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‎dist/faceRecognitionNet/index.js.map

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‎dist/normalize.d.ts

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export declare function normalize(arr: number[]): number[];
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import * as tf from '@tensorflow/tfjs-core';
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export declare function normalize(input: number[]): tf.Tensor4D;

‎dist/normalize.js

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‎dist/normalize.js.map

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‎src/faceRecognitionNet/index.ts

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@@ -12,9 +12,7 @@ export function faceRecognitionNet(weights: Float32Array) {
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return tf.tidy(() => {
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const norm = normalize(input)
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const x = tf.tensor4d(norm, [1, 150, 150, 3])
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const x = normalize(input)
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let out = convDown(x, params.conv32_down)
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out = tf.maxPool(out, 3, 2, 'valid')

‎src/normalize.ts

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export function normalize(arr: number[]) {
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const avg_r = 122.782;
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const avg_g = 117.001;
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const avg_b = 104.298;
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const avgs = [avg_r, avg_g, avg_b]
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return arr.map((val, i) => {
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const avg = avgs[i % 3]
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return (val - avg) / 256
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import * as tf from '@tensorflow/tfjs-core';
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export function normalize(input: number[]): tf.Tensor4D {
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return tf.tidy(() => {
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const avg_r = tf.fill([1, 150, 150, 1], 122.782);
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const avg_g = tf.fill([1, 150, 150, 1], 117.001);
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const avg_b = tf.fill([1, 150, 150, 1], 104.298);
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const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)
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const x = tf.tensor4d(input, [1, 150, 150, 3])
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return tf.div(tf.sub(x, avg_rgb), tf.fill(x.shape, 256))
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})
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}

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