data = [
{ input: { r: 0.62, g: 0.72, b: 0.88 }, output: { light: 1 } },
{ input: { r: 0.1, g: 0.84, b: 0.72 }, output: { light: 1 } },
{ input: { r: 0.33, g: 0.24, b: 0.29 }, output: { dark: 1 } },
{ input: { r: 0.74, g: 0.78, b: 0.86 }, output: { light: 1 } },
{ input: { r: 0.31, g: 0.35, b: 0.41 }, output: { dark: 1 } },
{ input: { r: 1, g: 0.99, b: 0 }, output: { light: 1 } },
{ input: { r: 1, g: 0.42, b: 0.52 }, output: { dark: 1 } },
{ input: { r: 0, g: 0.25, b: 1 }, output: { light: 1 } },
{ input: { r: 0, g: 0.45, b: 1 }, output: { light: 1 } },
{ input: { r: 0.5, g: 0.5, b: 0.5 }, output: { inconclusive: 1 } },
{ input: { r: 0.6, g: 0.4, b: 0.4 }, output: { inconclusive: 1 } },
{ input: { r: 0.55, g: 0.45, b: 0.52 }, output: { inconclusive: 1 } }
]
predictColor = d3.color(predict)
testData = ({
r: predictColor.r / 255,
g: predictColor.g / 255,
b: predictColor.b / 255
})
prediction = trainedNetwork.run(testData)
config = ({
// Defaults values --> expected validation
iterations: 10000, // the maximum times to iterate the training data --> number greater than 0
errorThresh: 0.005, // the acceptable error percentage from training data --> number between 0 and 1
log: false, // true to use console.log, when a function is supplied it is used --> Either true or a function
learningRate: 0.12, // scales with delta to effect training rate --> number between 0 and 1
momentum: 0.12, // scales with next layer's change value --> number between 0 and 1
timeout: Infinity // the max number of milliseconds to train for --> number greater than 0
})
trainedNetwork = {
const network = new brain.NeuralNetwork();
//network.train(data,config);
network.train(data);
return network;
}
brain = require("brain.js")
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