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| -- CAUTION: If you use this UDF to many images, you may lose many-many money. | |
| CREATE TEMPORARY FUNCTION predictMnist(test ARRAY<FLOAT64>) | |
| RETURNS FLOAT64 | |
| LANGUAGE js AS """ | |
| var nj = globalNumjs; | |
| const ntest = nj.array(test); | |
| const nw1 = nj.array(w1); | |
| const nw2 = nj.array(w2); | |
| const nw3 = nj.array(w3); | |
| const nb1 = nj.array(b1); | |
| const nb2 = nj.array(b2); | |
| const nb3 = nj.array(b3); | |
| function predict(x) { | |
| const a1 = nj.add(nj.dot(x, nw1), nb1) | |
| const z1 = nj.sigmoid(a1) | |
| const a2 = nj.add(nj.dot(z1, nw2), nb2) | |
| const z2 = nj.sigmoid(a2) | |
| const a3 = nj.add(nj.dot(z2, nw3), nb3) | |
| const y = nj.softmax(a3) | |
| return y; | |
| } | |
| const p = predict(ntest) | |
| const pa = p.tolist() | |
| return pa.indexOf(Math.max.apply(null, pa)); | |
| """ | |
| OPTIONS ( | |
| library=[ | |
| "gs://your_bucket/w1.js", -- trained weight(784, 50) | |
| "gs://your_bucket/w2.js", -- trained weight(50, 100) | |
| "gs://your_bucket/w3.js", -- trained weight(100, 10) | |
| "gs://your_bucket/b1.js", -- trained bias(50) | |
| "gs://your_bucket/b2.js", -- trained bias(100) | |
| "gs://your_bucket/b3.js", -- trained bias(10) | |
| "gs://your_bucket/bundle.js" -- numjs(webpacked and globaled to 'var globalNumjs') | |
| ] | |
| ) | |
| ; | |
| SELECT predictMnist([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3294117748737335, 0.7254902124404907, 0.6235294342041016, 0.5921568870544434, 0.23529411852359772, 0.1411764770746231, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8705882430076599, 0.9960784316062927, 0.9960784316062927, 0.9960784316062927, 0.9960784316062927, 0.9450980424880981, 0.7764706015586853, 0.7764706015586853, 0.7764706015586853, 0.7764706015586853, 0.7764706015586853, 0.7764706015586853, 0.7764706015586853, 0.7764706015586853, 0.6666666865348816, 0.20392157137393951, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.26274511218070984, 0.4470588266849518, 0.2823529541492462, 0.4470588266849518, 0.6392157077789307, 0.8901960849761963, 0.9960784316062927, 0.8823529481887817, 0.9960784316062927, 0.9960784316062927, 0.9960784316062927, 0.9803921580314636, 0.8980392217636108, 0.9960784316062927, 0.9960784316062927, 0.5490196347236633, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.06666667014360428, 0.25882354378700256, 0.054901961237192154, 0.26274511218070984, 0.26274511218070984, 0.26274511218070984, 0.23137255012989044, 0.08235294371843338, 0.9254902005195618, 0.9960784316062927, 0.4156862795352936, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.32549020648002625, 0.9921568632125854, 0.8196078538894653, 0.07058823853731155, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08627451211214066, 0.9137254953384399, 1.0, 0.32549020648002625, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5058823823928833, 0.9960784316062927, 0.9333333373069763, 0.1725490242242813, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.23137255012989044, 0.9764705896377563, 0.9960784316062927, 0.24313725531101227, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5215686559677124, 0.9960784316062927, 0.7333333492279053, 0.019607843831181526, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03529411926865578, 0.8039215803146362, 0.9725490212440491, 0.22745098173618317, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4941176474094391, 0.9960784316062927, 0.7137255072593689, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.29411765933036804, 0.9843137264251709, 0.9411764740943909, 0.2235294133424759, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07450980693101883, 0.8666666746139526, 0.9960784316062927, 0.6509804129600525, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0117647061124444, 0.7960784435272217, 0.9960784316062927, 0.8588235378265381, 0.13725490868091583, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.14901961386203766, 0.9960784316062927, 0.9960784316062927, 0.3019607961177826, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12156862765550613, 0.8784313797950745, 0.9960784316062927, 0.45098039507865906, 0.003921568859368563, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5215686559677124, 0.9960784316062927, 0.9960784316062927, 0.20392157137393951, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.239215686917305, 0.9490196108818054, 0.9960784316062927, 0.9960784316062927, 0.20392157137393951, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4745098054409027, 0.9960784316062927, 0.9960784316062927, 0.8588235378265381, 0.1568627506494522, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4745098054409027, 0.9960784316062927, 0.8117647171020508, 0.07058823853731155, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]); |
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About bundle.js
$ yarn $ yarn add numjs webpack $ vi webpack.config.js --- module.exports = { entry: './app.js', output: { filename: 'bundle.js' } } --- $ vi app.js --- import numjs from 'numjs'; globalNumjs = numjs; --- $ ./node_modules/webpack/bin/webpack.js # create bundle.js $ vi bundle # Add "var globalNumjs;" to top of file --- var globalNumjs; ---