Running network from JSON seems inaccurate with hundreds of JSON objects?

I’m utilising the fromJSON option of Brain JS to output my network to a json file and load it back in, however, I’m confused as to how I’m meant to train this…

Am I meant to run one set of data through the network in the .run([]) and then use that one json file for other data running through the data, or, am I meant to train it and keep appending data to the JSON file, either way, the following seems to return mostly the same result all of the time, I’ve stripped most of the training data, but the array of objects would be thousands of objects:

// require packages
const brain = require('brain.js')
const fs = require('fs');
const path = require('path');
const args = require('minimist')(process.argv.slice(2))

// check if we have a trained network already
const checkIfNetworkExists = fs.existsSync('./js/generate/data/trained-network.json')

// Loan & Term: init Training Pattern
const myNetwork = new brain.NeuralNetwork({hiddenLayers: [7, 7]})
if (!checkIfNetworkExists) {
  const trainingData = [
    {input:[451,2200,42,0,650,200,150],output:[0,1,1,0,0,1,1]},{input:[851,1400,52,150,500,50,850],output:[0,1,1,0,0,0,0]}
  ]

  // train the network
  if (args['enableAsyncTraining'] && args['enableAsyncTraining'] === 'true') {
    myNetwork.trainAsync(trainingData, trainingOptions)
  } else {
    myNetwork.train(trainingData, trainingOptions)
  }

  // write trained network to file
  fs.writeFileSync('./js/generate/data/trained-network.json', JSON.stringify(myNetwork.toJSON(), null, '  '));
} else {
  
  // load network if it exists
  myNetwork.fromJSON(JSON.parse(fs.readFileSync('./js/generate/data/trained-network.json', 'utf8')));
}

// run the network
myNetwork.run([100, 2000, 40, 6, 650, 200, 100])

[robertleeplummerjr]

Normalize them to between 0 and 1 for the best results (NOTE: this isn’t a brain.js thing, it is just machine learning in general). Normalize the most negative number (or least number) to 0, and then the highest number to 1, everything else should be within that 0 to 1 range for effective normalization.

[mubaidr]

@sts-ryan-holton Can you please create a playground here, with your sample code: https://runkit.com/home

This will allow us to understand the code and we will be able to update code.