# TensorFlow Tutorial

TensorFlow.js is a JavaScript framework to define and operate on Tensors

TensorFlow Tensors have 3 properties:

• Type
• Rank
• Shape

## Using TensorFlow

To use TensorFlow.js, add the following script tag to your HTML file(s):

### Example

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@3.6.0/dist/tf.min.js"></script>

### Example 2

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>

## Tensors

The central data unit in TensorFlow.js is the Tensor.

A Tensor is much the same as an multidimensional array.

A Tensor has the following properties:

PropertyDescription
dtypeThe data type
rankThe number of dimensions
shapeThe size of each dimension

## Creating a Tensor

A Tensor can be created from any N-dimensional array:

### Example 1

const tensorA = tf.tensor([[1, 2], [3, 4]]);

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### Example 2

const tensorA = tf.tensor([[1, 2], [3, 4], [5, 6]]);

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## Tensor Shape

A Tensor can also be created from an array and a shape parameter:

### Example1

const shape = [2, 2];
const tensorA = tf.tensor([1, 2, 3, 4], shape);

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### Example2

const tensorA = tf.tensor([1, 2, 3, 4], [2, 2]);

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### Example3

const tensorA = tf.tensor([[1, 2], [3, 4]], [2, 2]);

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## Tensor Data Types

A Tensor can have the following data types:

• bool
• int32
• float32 (default)
• complex64
• string

When you create a tensor, you can specify the data type as the third parameter:

### Example

const tensorA = tf.tensor([1, 2, 3, 4], [2, 2], "int32");
/*
Results:
tensorA.rank = 2
tensorA.shape = 2,2
tensorA.dtype = int32
*/

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## Tensor Square

You can square a tensor using tensor.square():

### Example

const tensorA = tf.tensor([1, 2, 3, 4]);

// Tensor Square
const tensorSquare = tensorA.square();

// Result [ 1, 4, 9, 16 ]

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## Tensor Reshape

The number of elements in a tensor is the product of the sizes in the shape.

Since there can be different shapes with the same size, it is often useful to reshape a tensor to other shapes with the same size.

You can reshape a tensor using tensor.reshape():

### Example

const tensorA = tf.tensor([[1, 2], [3, 4]]);
const tensorB = tensorA.reshape([4, 1]);

// Result: [ , , ,  ]

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## Retrieve Tensor Values

You can get the data behind the tensor using tensor.data():

### Example

const tensorA = tf.tensor([[1, 2], [3, 4]]);
tensorA.data().then(data => display(data));

// Result: 1,2,3,4
function display(data) {
document.getElementById("demo").innerHTML = data;
}

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You can get the array behind the tensor using tensor.array():

### Example

const tensorA = tf.tensor([[1, 2], [3, 4]]);
tensorA.array().then(array => display(array));

// Result: 1,2
function display(array) {
document.getElementById("demo").innerHTML = array;
}

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