A quick introduction to TensorFlow.js

a set of tutorials by The Coding Train on Youtube

The basics of TensorFlow.js

First of all, you should know that all the documentation is on TF’s website, under the API Reference section.

But wait, why is it called TensorFlow? What even is a tensor?

In TF, tensors are differentiated because of their rank, or, in other words, the number of dimensions they have.

These are the most common ones:

Scalar (rank-0)

Just a number.
This is how you can create and console-log one:

→ tf.scalar(4.5).print();

And the output is the following:

Tensor

  4.5

Tensor1d, tensor2d, tensor3d and tensor4d (rank- 1, 2, 3 and 4 respectively)

These are higher dimensional tensors.
If you wanted to create a rank-1 tensor, for instance, you could simply do:

→ tf.tensor1d([3, 7, 8]).print();

Which would output:

Tensor

  [3, 7, 8]

Tensor (rank-n)

If you don’t know the dimensions of your tensor, you can simply create one with the following function (notice how the above two examples work just as well with this other method):

→ tf.tensor(4.5).print();

tf.tensor([3, 7, 8]).print();

This outputs exactly the same as before.

You can, additionally, pass a couple more parameters to these functions.

→ tf.tensor(values, shape?, dtype?)

Let’s look at values first.
This is the only compulsory parameter, and the only one we’ve been passing on in the previous examples.
You can either pass a flat array of values (or even a single number in scalars) and specify the shape yourself, or you can pass a nested array.

Now you might be wondering what shape is.
So, let’s say you want to output the following tensor:

[[1, 5],

 [4, 7]]

That is, you guessed right, a 2x2 matrix.
You can either create this tensor by passing a flat array and specifying the shape as the second parameter of the function

tf.tensor([1, 5, 4, 7], [2, 2]).print();

or by passing a nested array

tf.tensor([[1, 5], [4, 7]]).print();

Lastly, we have dtype.
This specifies the data type.
As for now, int32, float32 and bool are the three supported types.

Operations

But, what can you do with these tensors? Well, among other things, you can perform mathematical computation on them, such as arithmetic operations:

Note: the following operations are performed element-wise, which means that every term of the first tensor involved is associated with the term in its same place in the other tensor.

const a = tf.tensor1d([4, 7, 2, 1]);

const b = tf.tensor1d([20, 30, 40, 50]);

There are two ways these two can be added:

→ a.add(b).print();

or,

→ tf.add(a, b);

Both output:

Tensor

  [24, 37, 42, 51]

Here’s how they work for subtraction,

→ tf.sub(a, b).print();

Tensor //output

  [-16, -23, -38, -49]

multiplication,

→ tf.mul(a, b).print();

Tensor //output

  [80, 210, 80, 50]

and division:

→ tf.div(a, b).print();

Tensor //output

  [0.2, 0.2333333, 0.05, 0.02]

It’s pretty simple and straightforward.