I am pletely new to Machine learning and also to tensorflow.js, I am trying to predict the values of the next set but it is giving me "NaN" in result. What am I doing wrong ?

Following this Github example

 async function myFirstTfjs(arr) {
    // Create a simple model.
    const model = tf.sequential();
    model.add(tf.layers.dense({units: 1, inputShape: [2]}));

    // Prepare the model for training: Specify the loss and the optimizer.
    modelpile({
      loss: 'meanSquaredError',
      optimizer: 'sgd'
    });
    const xs = tf.tensor([[1,6],
        [2,0],
        [3,1],
        [4,2],
        [5,3],
        [6,4],
        [7,5],
        [8,6],
        [9,0],
        [10,1],
        [11,2],
        [12,3],
        [13,4],
        [14,5],
        [15,6],
        [16,0],
        [17,1],
        [18,2],
        [19,3],
        [20,4],
        [21,5],
        [22,6],
        [23,0],
        [24,1],
        [25,2],
        [26,3]]);
    const ys = tf.tensor([104780,30280,21605,42415,32710,30385,35230,97795,31985,34570,35180,30095,36175,57300,104140,30735,28715,36035,34515,42355,38355,110080,26745,35315,40365,30655], [26, 1]);
    // Train the model using the data.
    await model.fit(xs, ys, {epochs: 500});
    // Use the model to do inference on a data point the model hasn't seen.
  model.predict(tf.tensor(arr, [1, 2])).print();
  }
  myFirstTfjs([28,5]);

I am pletely new to Machine learning and also to tensorflow.js, I am trying to predict the values of the next set but it is giving me "NaN" in result. What am I doing wrong ?

Following this Github example

 async function myFirstTfjs(arr) {
    // Create a simple model.
    const model = tf.sequential();
    model.add(tf.layers.dense({units: 1, inputShape: [2]}));

    // Prepare the model for training: Specify the loss and the optimizer.
    model.pile({
      loss: 'meanSquaredError',
      optimizer: 'sgd'
    });
    const xs = tf.tensor([[1,6],
        [2,0],
        [3,1],
        [4,2],
        [5,3],
        [6,4],
        [7,5],
        [8,6],
        [9,0],
        [10,1],
        [11,2],
        [12,3],
        [13,4],
        [14,5],
        [15,6],
        [16,0],
        [17,1],
        [18,2],
        [19,3],
        [20,4],
        [21,5],
        [22,6],
        [23,0],
        [24,1],
        [25,2],
        [26,3]]);
    const ys = tf.tensor([104780,30280,21605,42415,32710,30385,35230,97795,31985,34570,35180,30095,36175,57300,104140,30735,28715,36035,34515,42355,38355,110080,26745,35315,40365,30655], [26, 1]);
    // Train the model using the data.
    await model.fit(xs, ys, {epochs: 500});
    // Use the model to do inference on a data point the model hasn't seen.
  model.predict(tf.tensor(arr, [1, 2])).print();
  }
  myFirstTfjs([28,5]);

• javascript
• tensorflow
• machine-learning
• deep-learning
• tensorflow.js

Share Improve this question Follow edited May 2, 2018 at 5:29 Pratik Khadtale asked May 1, 2018 at 20:04 Pratik KhadtalePratik Khadtale 30555 silver badges1111 bronze badges

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2 Answers 2

Sorted by: Reset to default 4

What's happening is that the large values in ys are leading to a very large error. That large error, in bination with the (default) learning rate, are causing the model to overcorrect and be unstable. The model will converge if you lower the learning rate.

const learningRate = 0.0001;
const optimizer = tf.train.sgd(learningRate);

model.pile({
  loss: 'meanSquaredError',
  optimizer: optimizer,      
});

Try convert your output to more readable and change your optimizer

var pred = model.predict(tf.tensor(arr, [1, 2]));
    var readable_output = pred.dataSync();
    console.log(readable_output);