Tensorflow.js Model only predicting same values

Question

I have a dataset (which contains both the data and the label) with the size of [299,13], and the model keeps outputting / predicting the same value. This is a binary classification task. How would I make my model predict values which are not constantly the same?

Here is the code (with some dummy data):

var Dataset = tf.tensor([[1,0.491821360184978,9,314,0.504585169147173,542,1231,3213,1,0.267304071302649,3,0.615917680092409,0],
        [0,0.72959029133292,3,758,0.402582737085955,400,1788,4599,0,0.532702887951197,4,0.18630897965037,1],
        [1,0.198764110760428,5,787,0.65507860022684,887,192,4831,1,0.739456077544426,3,0.100068056951143,1],
        [0,0.583574833590476,5,596,0.933996451580092,631,331,811,0,0.258445986493932,7,0.811276729811182,0],
        [1,0.701499878184206,8,854,0.0326334179806069,845,470,4930,1,0.825469683527519,1,0.448086959665654,1],
        [0,0.954482878414911,2,468,0.736300149681564,557,3110,739,0,0.325783042694677,5,0.43488580142501,1],
        [1,0.384845877769,2,662,0.265402742189238,649,384,1158,1,0.484884260891815,2,0.915444292219105,0],
        [1,0.379266474923531,9,551,0.275982850450116,1022,3329,1413,1,0.237295089390298,4,0.817104709627837,1],
        [1,0.691365367558705,8,549,0.479627221800976,796,3381,495,1,0.37129382411555,9,0.332832739155564,1],
        [0,0.433042848178662,5,529,0.545178403950882,842,4768,506,0,0.386370525896832,9,0.189942077251933,0],
        [1,0.611272282663452,4,823,0.737901576655264,839,2724,1787,1,0.365032317656007,6,0.884073622694046,0],
        [0,0.0084315409129881,5,352,0.76858549557176,476,685,4796,0,0.302944943656102,1,0.849655932794213,1],
        [0,0.977380232874908,6,701,0.588833228576897,999,2897,3325,0,0.418024491281536,2,0.631872118440871,1],
        [1,0.419601058571829,10,384,0.0157052616592944,1009,4438,113,1,0.909015627566542,1,0.0297684897733232,0],
        [0,0.739471449044276,4,836,0.0430176780439737,1030,1456,3932,0,0.331426481315121,6,0.734008754824423,0],
        [1,0.00209807072438295,4,352,0.499622407429238,418,1912,4452,1,0.727130871883893,8,0.157427964683612,0],
        [1,0.956533819923862,10,681,0.196708599930969,829,4562,1718,1,0.233193195569506,7,0.60582783922237,0],
        [1,0.504637155233183,8,809,0.608861975627751,717,130,4194,1,0.134197560919101,6,0.375188428842507,0],
        [0,0.747363884375055,1,522,0.868234577182028,849,3529,1192,0,0.0322641640468155,5,0.185973206518818,0],
        [0,0.244142898027225,10,402,0.0280582030746698,315,3576,3882,0,0.724916254371562,8,0.062229775169706,1],
        [0,0.858414851618448,8,459,0.367325906336267,616,930,3892,0,0.177388425930446,10,0.859824526007041,1],
        [1,0.921555604905976,2,863,0.821166873626313,528,1624,1289,1,0.366243396916411,5,0.453840754701258,1],
        [1,0.171321120311715,1,524,0.177251413832862,468,1608,3123,1,0.192861821442111,8,0.122983286410146,0],
        [0,0.539946042901786,6,692,0.817780349862711,392,1053,4891,0,0.409578972921785,3,0.0453862502541893,1],
        [1,0.996848843212564,5,549,0.877740438211017,762,3046,843,1,0.888578696082088,8,0.877971306478434,1],
        [0,0.218116987741582,3,655,0.240496962520226,407,1001,1474,0,0.976212355833712,2,0.936396547703282,1]])
var x = Dataset.slice([0, 0], [-1, 12]) 
var y = Dataset.slice([0, 12], [-1, 1]) y = y.cast('int32').reshape([-1]).oneHot(2) y.print()


const model = tf.sequential({
    layers: [
        tf.layers.dense({ inputShape: [12], units: 12, activation: "relu6" }),
        tf.layers.dense({ units: 56, activation: "tanh" }),
        tf.layers.dense({ units: 28, activation: "tanh" }),
        tf.layers.dense({ units: 14, activation: "sigmoid" }),
        tf.layers.dense({ units: 58, activation: "tanh" }),
        tf.layers.dense({ units: 2, activation: "softmax" })
    ] }) model.summary()

model.compile({
    optimizer: tf.train.adam(),
    loss: 'categoricalCrossentropy',
    metrics: ['accuracy'], });

model.fit(x, y, { batchSize: 3, epochs: 10, shuffle: true }).then(h => {
    console.log("Training Complete")
    var predictions = model.predict(x)
    predictions.print() });

Frightera · Accepted Answer · 2021-02-28 12:01:09Z

1

299 samples with 13 features. That might not be enough for model to generalize. In your hidden layers you use tanh, and sigmoid. I suggest using relu. Also you one-hot-encoding your labels to use softmax, that's understandable but you might want to use sigmoid.

If you use sigmoid without one-hot-encoding, then you will have a chance to set some threshold depending on your business problem.

    tf.layers.dense({ units: 1, activation: "sigmoid" })

Let's say you set 0.5 threshold for predictions, means if your prediction is bigger than 0.5 then it will belong to second class. But you can adjust it to, say 0.4, to see what happens. You can conclude it by interpreting AUC-ROC curve.

Another thing is about features, they are not scaled properly:

[1,0.00209807072438295,4,352,0.499622407429238,418,1912,4452,1,0.727130871883893,8,0.157427964683612,0]

If they are not scaled properly in a range, then model can give more importance to certain features than the others, or some unexpected behaviors can happen.

edited Feb 28, 2021 at 12:01

answered Feb 28, 2021 at 11:06

Frightera

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9 Comments

jr123456jr987654321 Over a year ago

Sorry for wasting your time, but if you test the code the model (at least for me) outputs the same values for every single example. Even with your adaptation.

Frightera Over a year ago

Try adjusting hidden layer neurons, like start with 32, and go on 64,128,256..

jr123456jr987654321 Over a year ago

Sorry I tried everything you said. Nothing works. Maybe update your answer (with all the things you would update)? In case I have misunderstood you.

Frightera Over a year ago

What does it predict? Which value? What are the loss and accuracy values?

jr123456jr987654321 Over a year ago

The value is 0.3203969.

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jwson · Accepted Answer · 2021-09-10 04:16:28Z

0

I had the same problem. Model is trained but it always predicts the same value. I don't know the exact principle, but I first trained model with fake data and then trained with normal data again, and this problem was solved. I think it was initialized by training with fake data at first.

I will add example code.

  var fake_xs = tf.zeros([10, 7, 7, 256]);
  var fake_ys = tf.zeros([10]);
  
  newModel.current.fit(
    fake_xs, 
    fake_ys, {
    epochs: 5,
    callbacks: {
      onEpochEnd: async (epoch, logs) => {
        setLoss(logs.loss.toFixed(5));
        console.log("LOSS: " + logs.loss.toFixed(5));
      },
    },
  });      

  const history = await newModel.fit(
     datasetForTraining.xs,
     datasetForTraining.ys,
     {
        epochs: epochNum,
        batchSize: 16,
        callbacks: {
        onEpochEnd: async (epoch, logs) => {
          setLoss(logs.loss.toFixed(5));
          console.log("LOSS: " + logs.loss.toFixed(5));
        },
      },
   }

edited Sep 10, 2021 at 4:16

answered Sep 7, 2021 at 14:14

jwson

235 bronze badges

2 Comments

Community Over a year ago

Please add further details to expand on your answer, such as working code or documentation citations.

jwson Over a year ago

I added the example code. There is no reference document, but the problem was solved in my case.

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