JavaScript – TensorFlow.js . – free-tutorials.org news free tutorials source code development scripts

Many people have heard about TensorFlow.
I tried to use it with another programming language, but installation was difficult.
Today I saw that it was implemented on javascript.
I am not very advanced in javascript (thanks to multiple implementations) but I will try to write a simple tutorial.
TensorFlow.js is an open source WebGL-accelerated JavaScript library for machine intelligence. It brings highly performant machine learning building blocks to your fingertips, allowing you to train neural networks in a browser or run pre-trained models in inference mode.
You can use it with script tag or NPM
See the demos webpage.
The default example from GITHUB works well:

<html>
  <head>
    <!-- Load TensorFlow.js -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </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();
		alert(model.predict(tf.tensor2d([5], [1, 1])))
      });
    </script>
  </head>
  <body>
  </body>
</html>

<html>

<head>

// 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();

alert(model.predict(tf.tensor2d([5], [1, 1])))

});

</script>

</head>

<body>

</body>

</html>

The results for Tensor vary, for example:[[1.9764308],]

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