# Five Tensor Operations

May 30, 2020

Last week I enrolled in FreeCodeCamp’s ZeroToGANs deep learning with PyTorch course. The motivation behind doing so was mainly because of quarantine and wanting to learn something new while being on lockdown.

As a software engineer mainly focused on app development, who by the way, barely completed college level algebra and trig, I can say in total confidence that I have no clue what the hell I am getting myself into or even if I have the capacity to complete it.

But, the cool thing about this course is that the instructors take a `code-first`

rather than a
`math-first`

approach in teaching the lessons and I think this works well for me because one thing
I can learn to userstand is code.

Now whether the `code-first`

approach is the right way to get introduced into the space of machine
learning is something I won’t get into and of course there are concepts that I would have to
learn in order to contribute anywhere effectively but the way I see it is:

`I have to start somewhere right?`

So with all that said, this is by no means a hardcore introduction to data science. I won’t be going
into things like **gradient descent**, **linear regression**, **neural networks** etc. because there
are plenty of authority figures who are better equipped to do so. Instead, what you’ll find below
are **5 PyTorch methods** I found interesting that I think are a good way to get introduced to the
space starting with code.

## A little intro to PyTorch and torch.tensor

PyTorch is a python library built and maintained by Facebook, full of programs to help facilitate building of deep learning projects.

PyTorch at its core processes multi-dimensional matrix called a
tensor. A tensor can contain ** integers**,

**and**

*floats****booleans**.

To create a tensor in PyTorch, use the `torch.tensor`

API likeso:

```
import torch
torch.tensor([1, 2, 3, 4])
# Output
tensor([1., 2., 3., 4.])
```

In the example above I created a one dimensional tensor with integers valued 1 - 4.

Outside of `torch.tensor`

, the five methods I’ll go over are:

`torch.arange()`

`torch.split()`

`torch.stack()`

`torch.abs()`

`torch.add()`

These are just methods I found that might be a good intro into PyTorch API but of course, if you want to learn more check out the documentation.

## Function #1 - torch.arange

Returns a one dimensional tensor with values from ** start** to

**incremented by**

*end***.**

*steps*```
# Example 1: 1-D tensor with size of 10 with steps of 1
torch.arange(10)
# Output
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
```

Notice that the first value in our tensor starts with 0. If no ** start** parameter is provided,
it defaults to

**0**.

```
# Example 2 - More parameters
torch.arange(1., 2, .1, requires_grad = True)
# Output
tensor([1.0000, 1.1000, 1.2000, 1.3000, 1.4000, 1.5000, 1.6000, 1.7000,
1.8000, 1.9000], requires_grad=True)
```

Notice here the start and steps parameters are **float** points. We can also provide a range of
other parameters such as ** requiresgrad_**,

**,**

*out***,**

*dtype***and**

*layout***.**

*device*```
# Example 3 - breaking (to illustrate when it breaks)
length = 10
width = 5
torch.arange(length * width, step = 1, dtype = torch.get_default_dtype())
# Output
TypeError: arange() received an invalid combination of arguments
```

Be mindeful of how you organize your parameters. Correct way to write this would be like so:

```
torch.arange(start = 0, end = length * width, step = 1,
dtype = torch.get_default_dtype())
```

torch.arange can be useful to create a one dimensional tensor of indices.

## Function #2 - torch.split

Splits the tensor into chunks. Each chunk is a view of the original tensor.

```
# Example 1 - Basic split
input_tensor = torch.tensor([50, 80, 10, 2])
torch.split(input_tensor, split_size_or_sections = 1)
# Output
(tensor([50]), tensor([80]), tensor([10]), tensor([2]))
```

Above we created a input tensor and used the split method to break our tensor data into equal chunks of size 1.

```
# Example 2 - Split a tensor of ones, size 10, into chunks of at most size 2
x = torch.ones(10)
torch.split(x, 2)
# Output
(tensor([1., 1.]),
tensor([1., 1.]),
tensor([1., 1.]),
tensor([1., 1.]),
tensor([1., 1.]))
```

In the example above, first we created a tensor of **1**’s size 10. After which we use the split
method to split the tensor into chucks of seperate tensors with a size of atmost 2.

```
# Example 3 - breaking (to illustrate when it breaks)
y = torch.zeros([3, 3])
torch.split(y, 1, 3)
# Output
IndexError: Dimension out of range (expected to be in range of [-2, 1], but got 3)
```

We get this error ** IndexError: Dimension out of range** when we attempt increase the dim size.
This causes an issue with the shape. Keep in mind the dimensions along which you want to split the tensor.

torch.split is perfect when you need to split a Tensor into equal-size chunks!

## Function #3 - torch.stack

Concatenates sequence of tensors along a new dimension.

```
# Example 1 - Basic example
a = torch.ones([3, 3])
b = torch.zeros([3, 3])
torch.stack([a, b])
# Output
tensor([[[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]],
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]])
```

In the above example we create two seperated tensors using ** ones** and

**. After which was concatenated into one tensor using the**

*zeros***method.**

*stack*```
# Example 2 - Stack two tensor on one dimension
c = torch.ones(5)
d = torch.zeros(5)
torch.stack([c, d], 1)
# Output
tensor([[1., 0.],
[1., 0.],
[1., 0.],
[1., 0.],
[1., 0.]])
```

In the above example we created another two tensors with the ** ones** and

**method. This time when we concatenated them using the**

*zeros***method we added an extra parameter for our dimension we want to**

*stack***along.**

*stack*```
# Example 3 - breaking (to illustrate when it breaks)
e = torch.ones(3)
f = torch.zeros(4)
torch.stack([e, f])
# Output
RuntimeError: stack expects each tensor to be equal size, but got
[3] at entry 0 and [4] at entry 1
```

We get the error above ** stack expects each tensor to be equal size** because the shapes of
tensor

**and**

*e***are not the same.**

*f*torch.stack function is perfect when you need to concatenate two tensors of equal size.

## Function #4 - torch.abs

Computes the element-wise absolute value of the given input tensor.

```
# Example 1 - Basic example
ex_tensor = torch.tensor([-1, -2, -3])
torch.abs(ex_tensor)
# Output
tensor([1, 2, 3])
```

In the above example, first we created a tensor with all negative integers and passed that tensor
through torches ***abs** which turned every element to a positive integer.

```
# Example 2
m = torch.tensor([1])
n = torch.tensor([2])
print('negative:', m - n)
torch.abs(m - n)
# Output
negative: tensor([-1])
tensor([1])
```

Similar to the first example, here we create two tensors using ** tensor** method, after which we
run some arithmitic on both tensors which returns a negative integer but as you can see when we
pass that same expression to

**method it returns as positive.**

*abs*```
# Example 3 - breaking (to illustrate when it breaks)
o = numpy.array([1, [-2]])
torch.abs(o)
# Output
TypeError: abs(): argument 'input' (position 1) must be Tensor, not numpy.ndarray
```

Make sure you pass a tensor as a argument to torch.abs unless it will throw the above error:
** abs(): argument ‘input’ (position 1) must be Tensor, not numpy.ndarray**.

torch.abs is great when you want to work with all positive element in your tensor!

## Function #5 - torch.add

Adds the scalar other to each element of the input and returns a new resulting tensor.

```
# Example 1 - Basic example
p = torch.ones(10)
torch.add(p, 9)
# Output
tensor([10., 10., 10., 10., 10., 10., 10., 10., 10., 10.])
```

So in the above example we create a tensor of ones with size of 10 using ** ones** method. Next we
pass our tensor of ones to the

**method with an accumulator of 9 which takes each element of our tensor and add 9 to it.**

*add*```
# Example 2
q = torch.randn(4)
r = torch.randn(1)
torch.add(q, r, alpha = 10)
# Output
tensor([17.4783, 17.0044, 17.4107, 19.7463])
```

In this example we create a tensor of random floats with size of 4. Next we create a tensor of a
random integer with size of one. Finally we add all the elements of ** q** with

**with a scalar multiplier of**

*r***.**

*10*```
# Example 3 - breaking (to illustrate when it breaks)
s = complex(1, 1)
t = torch.zeros(1)
torch.add(t, s)
# Output
RuntimeError: Complex dtype not supported.
```

In the above example we create a complex number using the ** complex** method. Then we create a
tensor of

**’s with size of one and then attempt to add the element of our tensor to our complex number. We get the error:**

*0***because only**

*Complex dtype not supported.***and**

*integers***are supported in the**

*floats***method.**

*add*This function is great when you need to accumulate each element in your tensor by the same value.

## And that’s about it

If you enjoyed this little intro I recommend checking out the rest of the documentation and also the course. I’ll link them below.

If you want to play around with the code, I’ve created a Jupyter notebook. There you can run the examples I’ve written above. You can also fork it and play around with the API yourself if you so please.

## Reference Links

**A blog by Ruben Profit**