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linear regression with tensorflow
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.11.7"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.7.2/Chart.bundle.min.js"></script>
<div style="width:400px;">
<canvas id="myChart" width="400" height="400"></canvas>
</div>
<button onclick="train()">Train the model 1 step</button>
<script>
const trainX = [
3.3,
4.4,
5.5,
6.71,
6.93,
4.168,
9.779,
6.182,
7.59,
2.167,
7.042,
10.791,
5.313,
7.997,
5.654,
9.27,
3.1
];
const trainY = [
1.7,
2.76,
2.09,
3.19,
1.694,
1.573,
3.366,
2.596,
2.53,
1.221,
2.827,
3.465,
1.65,
2.904,
2.42,
2.94,
1.3
];
const m = tf.variable(tf.scalar(Math.random()));
const b = tf.variable(tf.scalar(Math.random()));
function predict(x) {
return tf.tidy(function() {
return m.mul(x).add(b);
});
}
function loss(prediction, labels) {
//subtracts the two arrays & squares each element of the tensor then finds the mean.
const error = prediction.sub(labels).square().mean();
return error;
}
function train() {
const learningRate = 0.01;
const optimizer = tf.train.sgd(learningRate);
optimizer.minimize(function() {
const predsYs = predict(tf.tensor1d(trainX));
console.log(predsYs);
stepLoss = loss(predsYs, tf.tensor1d(trainY))
console.log(stepLoss.dataSync()[0])
return stepLoss;
});
plot();
}
const predictionsBefore = predict(tf.tensor1d(trainX));
async function plot() {
let plotData = [];
for (let i = 0; i < trainY.length; i++) {
plotData.push({ x: trainX[i], y: trainY[i] });
}
var ctx = document.getElementById("myChart").getContext("2d");
var scatterChart = new Chart(ctx, {
type: "line",
data: {
datasets: [
{
label: "Training Data",
showLine: false,
data: plotData,
fill: false
},
{
label: "Y = "+m.dataSync()[0]+"X + " + b.dataSync()[0],
data: [
{
x: 0,
y: b.dataSync()[0]
},
{
x: 11,
y: 11 * m.dataSync()[0] + b.dataSync()[0]
}
],
// Changes this dataset to become a line
type: "line",
borderColor: "red",
fill: false
}
]
},
options: {
scales: {
xAxes: [
{
type: "linear",
position: "bottom"
}
]
}
}
});
}
plot();
</script>
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