# Tensorflow js simple linear regression not working great

I have this basic code from an example video

(first half of the video does this but with a different data set)

<!DOCTYPE html>
<html lang="en">
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta content='width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=0' name='viewport' />
<title>Website</title>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js"></script>
<style>

</style>
<script>
var linearModel = tf.sequential();
linearModel.add(tf.layers.dense({units: 1, inputShape: [1]}));
linearModel.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

var xs = tf.tensor1d([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100]);
var ys = tf.tensor1d([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100]);

linearModel.fit(xs, ys);

function linearPrediction(val) {
var output = linearModel.predict(tf.tensor2d([val], [1,1]));
var prediction = Array.from(output.dataSync())[0];
console.log(prediction);
}

linearPrediction(50);
</script>
<body>
Welcome to my website.
</body>
</html>


I train it with 100 values where the input is same as output. Then when I try running it with 50 as new input after training, I get results that range from like -50 to 60.

Is this a normal thing? I would expect values close to 50.

Thanks

EDIT: I tried with an array from 1 to 1000, and input 500, and I get outputs from even -600....