Created
April 18, 2018 01:10
-
-
Save llSourcell/32bbec89b54096d1702929d16f8bd9d1 to your computer and use it in GitHub Desktop.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| <html> | |
| <head> | |
| <!-- Load TensorFlow.js --> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.9.0"> </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> |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment