TensorFlow.js MLP Regression Model output not returning as expected

Question

I've created a MLP Regression Model with TensorFlow.js that takes as input a tensor like this:

tf.tensor2d([[1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 8]])

The Model was constructed this way:

function createModel(inputShape) {
  const model = tf.sequential();
  model.add(tf.layers.dense({
    inputShape: inputShape,
    activation: 'sigmoid',
    units: 50,
  }));
  model.add(tf.layers.dense({
    activation: 'sigmoid',
    units: 50,
  }));
  model.add(tf.layers.dense({
    units: 1,
  }));
  model.compile({optimizer: tf.train.sgd(0.01), loss: 'meanSquaredError'});
  return model;
}

But the answer I'm getting is not as expected. No matter the input, I always get something like: 0.3157223165035248 instead of 0s and 1s.

My dataset has values like this example below, where the last column is the label, and should be the output of the training model. (I added the same input layer on first line here just as a demonstration)

1,0,1,1,1,0,1,1,1,1,1,0,8,1
1,1,1,1,0,0,1,1,0,0,1,0,6,0
1,1,0,1,0,0,0,1,0,1,0,1,5,0
1,1,0,1,1,0,1,1,1,1,0,1,8,1
1,1,0,1,0,0,1,1,0,1,1,1,6,0
1,0,0,0,0,0,0,1,0,0,1,0,2,0
1,1,1,1,1,0,1,1,1,1,0,0,9,1
0,1,0,0,0,0,0,1,0,0,1,0,2,0
1,1,1,1,0,0,0,0,1,0,0,0,5,0
1,1,0,0,1,0,0,1,1,1,0,0,6,0
1,1,1,1,0,1,1,1,1,0,0,1,8,1

I was using the TensorFlow Albalone Node example and adapting it to what I needed, so my code is pretty similar to it.

I've tried changing the activation and optimizers methods, changing the learning rate and loss values, reducing or increasing the dataset, epochs and batches, etc. but none helped. The results are always the same or pretty close to that.