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use brain.js ( neural network in js ) to learn contrast of background image of element

I'm interested in using brain.js: https://github.com/harthur/brain

There is a provided demo that shows how to train the neural network to recognise colour contrast, but how can I set it up so that it can learn, from input, which colour text ( white or black ) looks better on different background images?

Here is that demo: http://harthur.github.io/brain/

I have a set of background images that I want to train against. And then I want to save that training data and be able to add it to a site that uses those background images and that way be able to determine which colour text to use on top of them.

How to properly set up brain.js Neural Network

I am using the Auto MPG training set from http://archive.ics.uci.edu/ml/datasets/Auto+MPG

My code is:

'use strict';
var brain, fs, normalizeData, trainNetwork, _;

_ = require('lodash');

brain = require('brain');

fs = require('fs');

trainNetwork = function(trainNetworkCb) {
  var net;
  net = new brain.NeuralNetwork();
  return fs.readFile('./data/autodata.csv', function(err, fileData) {
    var fileString, lines, trainingData;
    if (err) {
      return trainNetworkCb(err);
    }
    fileString = fileData.toString();
    lines = fileString.split('\n');
    trainingData = lines.splice(0, lines.length / 2);
    trainingData = _.map(trainingData, function(dataPoint) {
      var normalizedData, obj;
      normalizedData = normalizeData(dataPoint);
      obj = {
        input: normalizedData,
        output: {
          continuous: normalizedData.continuous
        }
      };
      delete obj.input.continuous;
      return obj;
    });
    net.train(trainingData, {
      log: true,
      logPeriod: 100,
      errorThresh: 0.00005
    });
    return trainNetworkCb(null, net);
  });
};

trainNetwork(function(err, net) {
  if (err) {
    throw err;
  }
  return fs.readFile('./data/autodata.csv', function(err, fileData) {
    var fileString, lines, testData;
    if (err) {
      return trainNetworkCb(err);
    }
    fileString = fileData.toString();
    lines = fileString.split('\n');
    testData = lines.splice(lines.length / 2);
    testData = _.filter(testData, function(point) {
      return point !== '';
    });
    testData = _.map(testData, function(dataPoint) {
      var normalizedData, obj;
      normalizedData = normalizeData(dataPoint);
      obj = {
        output: {
          continuous: normalizedData.continuous
        },
        input: normalizedData
      };
      delete obj.input.continuous;
      return obj;
    });
    return _.each(testData, function(dataPoint) {
      var output;
      output = net.run(dataPoint.input);
      console.log(output);
      console.log(dataPoint);
      return console.log('');
    });
  });
});

normalizeData = function(dataRow) {
  var cylinders, dataSet, model_years, origins, row;
  dataSet = dataRow.split(',');
  dataSet = _.map(dataSet, function(point) {
    return Number(point);
  });
  row = {};
  cylinders = [5, 3, 6, 4, 8];
  _.each(cylinders, function(cylinder) {
    row["cylinder" + cylinder] = cylinder === dataSet[0] ? 1 : 0;
  });
  row.displacement = dataSet[1] / 500;
  row.horsepower = dataSet[2] / 500;
  row.weight = dataSet[3] / 10000;
  row.acceleration = dataSet[4] / 100;
  model_years = [82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70];
  _.each(model_years, function(model_year) {
    row["model_year" + model_year] = model_year === dataSet[5] ? 1 : 0;
  });
  origins = [2, 3, 1];
  _.each(origins, function(origin) {
    row["origin" + origin] = origin === dataSet[6] ? 1 : 0;
  });
  row.continuous = dataSet[7] / 100;
  return row;
};

I believe I am normalizing everything correctly. I am using half the data for training and the other half for testing. The data is not ordered, as far as I can tell, so which half is used for which shouldn't matter.

My errors are pretty large however when testing. Usually by 10MPG or so (30% error). What am I doing incorrectly?

Thanks