NatuMyers/gist:396ec699e20f6b8104e534f8b86a2017

## gistfile1.txt
<html>
  <head>
    <!-- Load TensorFlow.js -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.11.2"> </script>

    <!-- Place your code in the script tag below. You can also use an external .js file -->
    <script>
      // Notice there is no 'import' statement. 'tf' is available on the index-page
      // because of the script tag above.

      // Define a model for linear regression.
      const model = tf.sequential();
      model.add(tf.layers.dense({units: 1, inputShape: [1]}));

      // Prepare the model for training: Specify the loss and the optimizer.
      model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

      // 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]);

      // Train the model using the data.
      model.fit(xs, ys).then(() => {
        // Use the model to do inference on a data point the model hasn't seen before:
        // Open the browser devtools to see the output
        model.predict(tf.tensor2d([5], [1, 1])).print();
      });
    </script>
  </head>

  <body>
  </body>
</html>
	<html>
	<head>
	<!-- Load TensorFlow.js -->
	<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.11.2"> </script>

	<!-- Place your code in the script tag below. You can also use an external .js file -->
	<script>
	// Notice there is no 'import' statement. 'tf' is available on the index-page
	// because of the script tag above.

	// Define a model for linear regression.
	const model = tf.sequential();
	model.add(tf.layers.dense({units: 1, inputShape: [1]}));

	// Prepare the model for training: Specify the loss and the optimizer.
	model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

	// 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]);

	// Train the model using the data.
	model.fit(xs, ys).then(() => {
	// Use the model to do inference on a data point the model hasn't seen before:
	// Open the browser devtools to see the output
	model.predict(tf.tensor2d([5], [1, 1])).print();
	});
	</script>
	</head>

	<body>
	</body>
	</html>