TensorFlow Training

Training Function     Try it Yourself »
Model training allows a working model that can then be validated, tested and deployed. The model’s performance during training will eventually determine how well it will work when it is eventually put into an application for the end-users. 

async function trainModel( model, inputs, labels, surface ) {
  const batchSize = 25;
  const epochs = 100;
  const callbacks = tfvis.show.fitCallbacks( surface, ['loss'], {
    callbacks: ['onEpochEnd'] } )
  return await model.fit( inputs, labels,
    { batchSize, epochs, shuffle:true, callbacks:callbacks }
  );
}
epochs
It defines how many iterations (loops) the model will do.

model.fit
It is the function that runs the loops. The await keyword makes the function pause the execution and wait for a resolved promise before it continues.

callbacks
It defines the callback function to call when the model wants to redraw the graphics. A callback function is any reference to executable code that is passed as an argument to another piece of code; that code is expected to call back (execute) the callback function as part of its job.
http://undcemcs01.und.edu/~wen.chen.hu/course/525/13/car.html 
<html>
 <head>
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis"></script>
  <title>Car TensorFlow.js</title>
 </head>
 <body>
  <form>
   Horsepower:
   <input name="hp" id="hp" size="7" value="85" />
   <input type="button" onClick="runTF( )" value="Train and test" />
   <input type="reset" />
   The predicted MPG is <span id="answer">0.00</span> miles per gallon.
   <div id="plot1"></div>
   <div id="plot2"></div>
  </form>

  <script>
   // Extracting the required data 
   function extractData( obj ) {
    return { x:obj.Horsepower, y:obj.Miles_per_Gallon };
   }
   function removeErrors( obj ) {
    return ( ( obj.x != null ) && ( obj.y != null ) );
   }

   // Plotting the extracted data
   function tfPlot( values, surface ) {
    tfvis.render.scatterplot( surface,
     { values:values, series:[ 'Original', 'Predicted' ] },
     { xLabel:'Horsepower', yLabel:'MPG' } );
   }

   // Main function
   async function runTF( ) {
    const jsonData = await fetch( "carsData.json" );
    let values = await jsonData.json( );
    values = values.map( extractData ).filter( removeErrors );

    // Plotting the Data
    const surface1 = document.getElementById( "plot1" );
    const surface2 = document.getElementById( "plot2" );
    tfPlot( values, surface1 );

    // Converting the input to Tensors
    const inputs = values.map( obj => obj.x );
    const labels = values.map( obj => obj.y );
    const inputTensor = tf.tensor2d( inputs, [inputs.length, 1] );
    const labelTensor = tf.tensor2d( labels, [labels.length, 1] );
    const inputMin = inputTensor.min( );  
    const inputMax = inputTensor.max( );
    const labelMin = labelTensor.min( );
    const labelMax = labelTensor.max( );
    const nmInputs = inputTensor.sub(inputMin).div( inputMax.sub(inputMin) );
    const nmLabels = labelTensor.sub(labelMin).div( labelMax.sub(labelMin) );

    // Creating a Tensorflow model
    const model = tf.sequential( ); 
    model.add( tf.layers.dense( { inputShape:[1], units:1, useBias:true } ) );
    model.add( tf.layers.dense( { units: 1, useBias: true } ) );
    model.compile( { loss:'meanSquaredError', optimizer:'sgd' } );

    // Starting training
    await trainModel( model, nmInputs, nmLabels, surface2 );

    // Un-normalizing the data
    let unX = tf.linspace( 0, 1, 100 );      
    let unY = model.predict( unX.reshape( [100, 1] ) );      
    const unNormunX = unX
     .mul( inputMax.sub( inputMin ) )
     .add( inputMin );
    const unNormunY = unY
     .mul( labelMax.sub( labelMin ) )
     .add( labelMin );
    unX = unNormunX.dataSync( );
    unY = unNormunY.dataSync( );

    // Testing the model
    const predicted = Array.from(unX).map( (val, i) => {
     return { x: val, y: unY[i] }
    } );
    tfPlot( [values, predicted], surface1 );

    // Finding the MPG of the input horsepower
    var hp = parseInt( document.getElementById( "hp" ).value );
    unX.sort( ( a, b ) => a[0] - b[0] );
    let x1 = unX[0];
    for ( let i = 1; i < unX.length-1; i++ ) {
     let x2 = unX[i];
     if ( ( x1 <= hp ) && ( hp < x2 ) ) {
      document.getElementById("answer").innerHTML = Math.round( unY[i-1] );
      break;
     }
     x1 = x2;
    }
   }     // End of the main function runTF( )

   // Asyncronous function to train the model
   async function trainModel( model, inputs, labels, surface ) {
    const batchSize = 25;
    const epochs = 50;
    const callbacks = tfvis.show.fitCallbacks(
     surface, ['loss'], { callbacks:['onEpochEnd'] } );
    return await model.fit( inputs, labels,
     { batchSize, epochs, shuffle:true, callbacks:callbacks } );
   }     // End of trainModel
  </script>
 </body>
</html>




      Those two fight like cat and dog (continually arguing with each other),    
      so please don’t put them together on the project.