//Clone the repo:
git clone https://github.com/kaueDM/tensorflow-lab

//Install dependencies:
npm install

//If you dont have Parcel installed:
npm install -g parcel-bundler

//Run Parcel server:
parcel index.html

Tensor: The main building block of a Tensorflow neural net. Its a generic name for any N-dimensional thing of numbers and it's operations.

Examples of 'thing of numbers':

Scalar: A single number. Just it:

4. Or maybe 5.

Vector: Fancy word for Array:

[1,2,3,4, ...n]

Matrix: A 2-dimensional grid:

//I have no idea how i can represent this, so, this is my best:
| 1 2 3 |
| 4 5 6 |
| 7 8 9 |

Building a Tensor:

tf.tensor(values, shape?, dtype?)

values: Kinda obvious, huh?

shape: Dimension of a Tensor.

It's 2x2 matrix? Easy, [2,2]

Or 3x1 matrix populated with 2x2 matrices? Not a problem: [3,2,2]

dtype: DataType. float32, int32 or boolean

Example

Creating a Tensor with 5x3 (Rows x Columns, always) shape using 15 random numbers between 1 and 100

Input

const values = Array.from({ length: 15 }, _ => Math.floor(Math.random() * 100) + 1)
const shape = [5,3]
tf.tensor(values, shape).print()

Output

[
    [29, 39, 42],
    [35, 11, 79],
    [16, 49, 82],
    [46, 66, 29],
    [53, 47, 76]
]

You understand how to create a Tensor now? Great! Now forget it and use the proper method for each type of tensor. Doing this, you get a more readable code.

• tf.scalar (value, dtype?): Rank-0 tensor (scalar). value is a single number.
• tf.tensor1d (values, dtype?): Rank-1 tensor. values is an array of numbers.
• tf.tensor2d (values, shape?, dtype?): Rank-2 tensor. values is a matrix of numbers.
• tf.tensor2d (values, shape?, dtype?): Rank-3 tensor. values is a matrix of matrices. Exactly what we got in the example above.

Matrices multiplication

tf.matMul (a, b, transposeA?, transposeB?)

• Works with rank-2 tensors only.
• The number of cols in a should match the rows in b. Or TRANSPOSE one of them.
• To transpose a rank-2 tensor, you should assign the new tensor (Tensors are IMMUTABLE!) to a new variable, or you will get a big error.

First of all, Garbage collector is dead in TF.js lands. Why? Because we are working in the GPU. Tf.js needs to manage 'manually' the GPU memory to achieve a good speed in mathematical operations. So, pay attention to memory leaks.

How much tensors do i have?

tf.memory().numTensors will give you the answer.

After you used your tensors, you can manually dispose them using dispose() method.

const values = Array.from({ length: 6 }, _ => Math.floor(Math.random() * 6) + 1)
const shape = [2, 3]
const myFirstTensor = tf.tensor2d(values, shape)
const mySecondTensor = tf.tensor2d(values, shape)

//Do something meaningful with your tensor here...
const transposedTensor = mySecondTensor.transpose()
const multipliedMatrices = myFirstTensor.matMul(transposedTensor)

multipliedMatrices.print()

//...and dispose when they aren't necessary anymore
myFirstTensor.dispose()
mySecondTensor.dispose()
transposedTensor.dispose()

OR

Wrap your code with tf.tidy() and let the memory cleanup happens automagically:

tf.tidy(_ => {
    const values = Array.from({ length: 6 }, _ => Math.floor(Math.random() * 6) + 1)
    const shape = [2, 3]
    const myFirstTensor = tf.tensor2d(values, shape)
    const mySecondTensor = tf.tensor2d(values, shape)
    const transposedTensor = mySecondTensor.transpose()
    const multipliedMatrices = myFirstTensor.matMul(transposedTensor)

    multipliedMatrices.print()
})

If you don't want to dispose a tensor automatically inside the tf.tidy() method, you can call tf.keep() to, guess what, keep it.

tf.tidy(_ => {
    const values = Array.from({ length: 6 }, _ => Math.floor(Math.random() * 6) + 1)
    const shape = [2, 3]
    const myFirstTensor = tf.tensor2d(values, shape)
    const mySecondTensor = tf.tensor2d(values, shape)
    
    tf.keep(myFirstTensor)
    //Only mySecondTensor will be disposed.
})