simple image upscaling neural net

train.js

async function train(trainingPath)
{ 
  const training = await getTrainingDataInBatches(trainingPath);

  let model;

  if (training[0].index > 0){
    model = await tf.loadLayersModel(`file://./training/${training[0].index-1}/model.json`);
  }
  else {
    // r= (in/out)^0.25   hl1 = out*(r^3)  hl2 = out*(r^2)  hl3 = out*r
    model = tf.sequential();
    model.add(tf.layers.dense({ inputShape: [25], 
                                units: 20, activation: 'sigmoid' }));
    model.add(tf.layers.dense({ units: 5, activation: 'sigmoid' }));
    model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }));
  }
   
    model.compile({
      optimizer: tf.train.adam(0.001),
      loss: 'meanSquaredError',
      metrics: ['accuracy']
    });
  

  
  console.log('training...');
  for (var i=0; i<training.length; i++){
    const data = await training[i].getData();
    await model.fit(data.x, data.y, {
      batchSize: 256,
      shuffle: true,
      epochs: 100,
      ...training[i].options,
      callbacks: {
        onEpochEnd: async (epoch, logs) => {
          console.log("Epoch " + (epoch+1) + " Loss: " + logs.loss + " Accuracy: " + logs.acc + "\n");
        }
      }
    });
    await model.save(`file://./training/${training[i].index}`);
  }
}

(async () => {
  await train('./training/training.json');
})();