Polyglot Studio: DeepLearning With TensorFlowJS 3

Friday, January 3, 2020

DeepLearning With TensorFlowJS 3 - Fitting The Model

This tutorial is based on the book Deep Learning With JavaScript (TensorFlowJS).

////
// Data Markers
////
const dataTraceTrain = {
  x: trainData.sizeMB,
  y: trainData.timeSec,
  name: 'trainData',
  mode: 'markers',
  type: 'scatter',
  marker: {symbol: "circle", size: 8}
};
const dataTraceTest = {
  x: testData.sizeMB,
  y: testData.timeSec,
  name: 'testData',
  mode: 'markers',
  type: 'scatter',
  marker: {symbol: "triangle-up", size: 10}
};
const dataTrace10Epochs = {
  x: [0, 2],
  y: [0, 0.01],
  name: 'model after N epochs',
  mode: 'lines',
  line: {color: 'blue', width: 1, dash: 'dot'},
};
const dataTrace20Epochs = {
  x: [0, 2],
  y: [0, 0.01],
  name: 'model after N epochs',
  mode: 'lines',
  line: {color: 'green', width: 2, dash: 'dash'}
};
const dataTrace100Epochs = {
  x: [0, 2],
  y: [0, 0.01],
  name: 'model after N epochs',
  mode: 'lines',
  line: {color: 'red', width: 3, dash: 'longdash'}
};
const dataTrace200Epochs = {
  x: [0, 2],
  y: [0, 0.01],
  name: 'model after N epochs',
  mode: 'lines',
  line: {color: 'black', width: 4, dash: 'solid'}
};

////
// Set up plotly plot.
////
Plotly.newPlot('dataSpaceWith4Lines', [dataTraceTrain, dataTraceTest, dataTrace10Epochs, dataTrace20Epochs, dataTrace100Epochs, dataTrace200Epochs], {
  width: 700,
  title: 'Model fit result',
  xaxis: {
     title: 'size (MB)'
   },
  yaxis: {
    title: 'time (sec)'
  }
});

////
// Construct and compile model.
////
const model = tf.sequential();
model.add(tf.layers.dense({
  units: 1,
  inputShape: [1],
}));
// Use a slower learning rate for illustration purposes.
const optimizer = tf.train.sgd(0.0005);
model.compile({optimizer: optimizer, loss: 'meanAbsoluteError'});

// Updates a specified line on the plot
function updateScatterWithLines(dataTrace,k, b, N, traceIndex) {
  dataTrace.x = [0, 10];
  dataTrace.y = [b, b + (k * 10)];
  var update = {
    x: [dataTrace.x],
    y: [dataTrace.y],
    name: 'model after ' + N + ' epochs'
  } 
  Plotly.restyle('dataSpaceWith4Lines', update, traceIndex);
}

// Initialize to k=0, b=0 for pretty illustration purposes.  
// You may want to remove this to see how the model looks with
// random initialization
let k = 0;
let b = 0;
model.setWeights([tf.tensor2d([k], [1, 1]), tf.tensor1d([b])]);

////
// Train the model within an async block
////
(async () => {
  await model.fit(trainTensors.sizeMB, trainTensors.timeSec, {
    epochs: 200,
    // Use callbacks to orchestrate certain functions at certain triggers.
    callbacks: {
      onEpochEnd: async (epoch, logs) => {
        k = model.getWeights()[0].dataSync()[0];
        b = model.getWeights()[1].dataSync()[0];
        // console.log(`epoch ${epoch}`);
        if (epoch === 9) {
          updateScatterWithLines(dataTrace10Epochs, k, b, 10,  2);
          console.log('wrote model 10');
        }        
        if (epoch === 19) {
          updateScatterWithLines(dataTrace20Epochs, k, b, 20,  3);
          console.log('wrote model 20');
        }        
        if (epoch === 99) {
          updateScatterWithLines(dataTrace100Epochs, k, b, 100, 4);
          console.log('wrote model 100');
        }        
        if (epoch === 199) {
          updateScatterWithLines(dataTrace200Epochs, k, b, 200, 5);
          console.log('wrote model 200');
        }        
        await tf.nextFrame();
      }
    }
  });
})();  
  
</script>

https://codepen.io/tfjs-book/pen/VEVMMd

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Friday, January 3, 2020

DeepLearning With TensorFlowJS 3 - Fitting The Model

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