# Getting Started With TensorFlow.js

# Getting Started With TensorFlow.js

### In this super-simple tutorial, I'll show you a basic ''Hello World'' example that will teach you the scaffolding to get you up and running.

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Join For FreeWith TensorFlow.js, you can not only run machine-learned models in the browser to perform inference, but you can also *train* them. In this super-simple tutorial, I'll show you a basic "Hello World" example that will teach you the scaffolding to get you up and running.

Let's start with the simplest Web Page imaginable:

```
<html>
<head></head>
<body></body>
</html>
```

Once you have that, the first thing you'll need to do is add a reference to TensorFlow.js so that you can use the TensorFlow APIs. The JS file is available on a CDN for your convenience:

Right now, I'm using version 0.11.2, but be sure to check GitHub for the most recent version.

Now that TensorFlow.js is loaded, let's do something interesting with it.

Consider a straight line with the formula Y=2X-1. This will give you a set of points like (-1, -3), (0, -1), (1, 1), (2, 3), (3, 5) and (4, 7). While we *know * that the formula gives us the Y value for a given X, it's a nice exercise in training a model for a computer that is not explicitly programmed with this formula, to see if it can infer values of Y for given values of X when trained on this data.

So how would this work?

Well, first of all, we can create a super-simple neural network to do the inference. As there's only 1 input value and 1 output value, it can be a single node. In JavaScript, I can then create a tf.sequential, and add my layer definition to it. It can't get any more basic than this:

```
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));
```

To finish defining my model, I compile it, specifying my loss type and optimizer. I'll pick the most basic loss type — the meanSquaredError, and my optimizer will be a standard stochastic gradient descent (aka 'sgd'):

```
model.compile({
loss: 'meanSquaredError',
optimizer: 'sgd'
});
```

To *train* the model, I'll need a tensor with my *input *(i.e. 'X') values, and another with my *output* (i.e. 'Y') values. With TensorFlow, I also need to define the *shape* of that given tensor:

```
const xs = tf.tensor2d([-1, 0, 1, 2, 3, 4], [6, 1]);
const ys = tf.tensor2d([-3, -1, 1, 3, 5, 7], [6, 1]);
```

So, my Xs are the values -1,0,1,2,3 and 4, defined as a 6×1 tensor. My Ys are -3, -1, 1, 3, 5, 7 in the same shape. Note that the nth Y entry is the value for the nth X entry when we say that Y=2X-1.

To train the model, we use the *fit* method. To this, we pass the set of X and Y values along with a number of epochs (loops through the data) in which we will train it. Note that this is asynchronous, so we should *await* the return value before proceeding, so all this code needs to be in an *async* function (more on that later):

`await model.fit(xs, ys, {epochs: 500});`

Once that's done, the model is trained, so we can predict a value for a new X. So, for example, if we wanted to figure out the Y for X=10 and write it on the page in a <div>, the code would look like this:

```
document.getElementById('output_field').innerText =
model.predict(tf.tensor2d([10], [1, 1]));
```

Note that the input is a tensor, where we specify that it's a 1×1 tensor containing the value 10.

The result is written on the page in the div, and should look something like this:

Wait, you might ask, "Why isn't it 19?" It's pretty close, but it's not 19! That's because the algorithm has never been given the formula — it simply learns based on the data it was given. With more relevant data, any ML model will give greater accuracy, but this one isn't bad considering it only had 6 pieces of data to learn from!

For your convenience, here's the entire code for the page, including the declaration of all this code as an async function called "learnLinear":

```
<html>
<head>
<!-- Load TensorFlow.js -->
<!-- Get latest version athttps://github.com/tensorflow/tfjs --> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.11.2"> </script> </head> <body> <div id="output_field"></div> </body> <script> async function learnLinear(){ const model = tf.sequential(); model.add(tf.layers.dense({units: 1, inputShape: [1]})); model.compile({ loss: 'meanSquaredError', optimizer: 'sgd' }); const xs = tf.tensor2d([-1, 0, 1, 2, 3, 4], [6, 1]); const ys = tf.tensor2d([-3, -1, 1, 3, 5, 7], [6, 1]); await model.fit(xs, ys, {epochs: 500}); document.getElementById('output_field').innerText = model.predict(tf.tensor2d([10], [1, 1])); } learnLinear(); </script> <html>
```

And that's all it takes to create a very simple Machine Learned model with TensorFlow.js that executes in your browser. From here, you have the foundation to go forward with more advanced concepts.

Have fun with it!

Published at DZone with permission of Laurence Moroney , DZone MVB. See the original article here.

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