mohdsanadzakirizvi/basic_regression.js

## basic_regression.js
    const callbacks = {
        onEpochEnd: async (epoch, logs) => {
          console.log("epoch: " + epoch + JSON.stringify(logs))
        }
      };

    // Generate some synthetic data for training.
    const xs = tf.tensor2d([[1], [2], [3], [4]], [4, 1]);
    const ys = tf.tensor2d([[1], [3], [5], [7]], [4, 1]);

    // Build and compile model.
    async function basicRegression(){

      // Build a sequential model
      const model = tf.sequential();
      model.add(tf.layers.dense({units: 1, inputShape: [1]}));
      model.add(tf.layers.dense({units: 1, inputShape: [1]}));
      model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});

      // Train model with fit().
      await model.fit(xs, ys, {epochs: 100, validationSplit: 0.1, callbacks: callbacks});

      // Run inference with predict().
      model.predict(tf.tensor2d([[5]], [1, 1])).print();
  }

  // Create a basic regression model
  basicRegression();

## index.html
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta http-equiv="X-UA-Compatible" content="ie=edge">
  <!-- load Tensorflow.js -->
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js"></script>
</head>
<body>
  <h1>Tensorflow.js Core API</h1>
  <!-- <script src="index.js"></script> -->
  <script type="text/javascript">
  </script>
</body>
</html>

## index_2.html
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta http-equiv="X-UA-Compatible" content="ie=edge">
  <!-- load Tensorflow.js -->
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js"></script>
</head>
<body>
  <h1>Tensorflow.js Core API</h1>
  <!-- <script src="index.js"></script> -->
  <script type="text/javascript">
    const a = tf.tensor([1, 2, 3, 4]);
    const b = tf.tensor([10, 20, 30, 40]);
    const y = a.add(b);  // equivalent to tf.add(a, b)
    const z = a.mul(b);  // equivalent to tf.mul(a, b)
    y.print();
    z.print();
  </script>
</body>
</html>
	const callbacks = {
	onEpochEnd: async (epoch, logs) => {
	console.log("epoch: " + epoch + JSON.stringify(logs))
	}
	};

	// Generate some synthetic data for training.
	const xs = tf.tensor2d([[1], [2], [3], [4]], [4, 1]);
	const ys = tf.tensor2d([[1], [3], [5], [7]], [4, 1]);

	// Build and compile model.
	async function basicRegression(){

	// Build a sequential model
	const model = tf.sequential();
	model.add(tf.layers.dense({units: 1, inputShape: [1]}));
	model.add(tf.layers.dense({units: 1, inputShape: [1]}));
	model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});

	// Train model with fit().
	await model.fit(xs, ys, {epochs: 100, validationSplit: 0.1, callbacks: callbacks});

	// Run inference with predict().
	model.predict(tf.tensor2d([[5]], [1, 1])).print();
	}

	// Create a basic regression model
	basicRegression();
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<meta http-equiv="X-UA-Compatible" content="ie=edge">
	<!-- load Tensorflow.js -->
	<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js"></script>
	</head>
	<body>
	<h1>Tensorflow.js Core API</h1>
	<!-- <script src="index.js"></script> -->
	<script type="text/javascript">
	</script>
	</body>
	</html>