PyTorch Tensors Explained  Neural Network Programming
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Introducing PyTorch Tensors
Welcome back to this series on neural network programming with PyTorch. In this post, we'll start to dig in deeper with PyTorch itself by exploring PyTorch tensors. Without further ado, let's get started.
PyTorch tensors are the data structures we'll be using when programming neural networks in PyTorch.
When programming neural networks, data preprocessing is often one of the first steps in the overall process, and one goal of data preprocessing is to transform the raw input data into tensor form.
Instances of the
torch.Tensor
class
PyTorch tensors are instances of the torch.Tensor
Python class. We can create a torch.Tensor
object using the class constructor like so:
> t = torch.Tensor()
> type(t)
torch.Tensor
This creates an empty tensor (tensor with no data), but we'll get to adding data in just a moment.
Tensor attributes
First, let's look at a few tensor attributes. Every torch.Tensor
has these attributes:

torch.dtype

torch.device

torch.layout
Looking at our Tensor
t
, we can see the following default attribute values:
> print(t.dtype)
> print(t.device)
> print(t.layout)
torch.float32
cpu
torch.strided
Tensors have a
torch.dtype
The dtype
, which is torch.float32
in our case, specifies the type of the data that is contained within the tensor. Tensors contain uniform (of the same type) numerical data with
one of these types:
Data type  dtype  CPU tensor  GPU tensor 

32bit floating point  torch.float32  torch.FloatTensor  torch.cuda.FloatTensor 
64bit floating point  torch.float64  torch.DoubleTensor  torch.cuda.DoubleTensor 
16bit floating point  torch.float16  torch.HalfTensor  torch.cuda.HalfTensor 
8bit integer (unsigned)  torch.uint8  torch.ByteTensor  torch.cuda.ByteTensor 
8bit integer (signed)  torch.int8  torch.CharTensor  torch.cuda.CharTensor 
16bit integer (signed)  torch.int16  torch.ShortTensor  torch.cuda.ShortTensor 
32bit integer (signed)  torch.int32  torch.IntTensor  torch.cuda.IntTensor 
64bit integer (signed)  torch.int64  torch.LongTensor  torch.cuda.LongTensor 
Notice how each type has a CPU and GPU version. One thing to keep in mind about tensor data types is that tensor operations between tensors must happen between tensors with the same type of data. However, this statement only applies to PyTorch versions
lower than 1.3
. See the section below on PyTorch Tensor Type Promotion for details.
PyTorch Tensor Type Promotion
Arithmetic and comparison operations, as of PyTorch version 1.3
, can perform mixedtype operations that promote to a common dtype
.
The example below was not allowed in version 1.2
. However, in version 1.3
and above, the same code returns a tensor with dtype=torch.float32
.
torch.tensor([1], dtype=torch.int) +
torch.tensor([1], dtype=torch.float32)
See the full documentation for more details.

torch.result_type
Provide function to determine result of mixedtype ops 26012. 
torch.can_cast
Expose casting rules for type promotion 26805. 
torch.promote_types
Expose promotion logic 26655.
Tensors have a
torch.device
The device, cpu
in our case, specifies the device (CPU or GPU) where the tensor's data is allocated. This determines where tensor computations for the given tensor will be performed.
PyTorch supports the use of multiple devices, and they are specified using an index like so:
> device = torch.device('cuda:0')
> device
device(type='cuda', index=0)
If we have a device like above, we can create a tensor on the device by passing the device to the tensor's constructor. One thing to keep in mind about using multiple devices is that tensor operations between tensors must happen between tensors that exists on the same device.
Using multiple devices is typically something we will do as we become more advanced users, so there's no need to worry about that now.
Tensors have a
torch.layout
The layout, strided
in our case, specifies how the tensor is stored in memory. To learn more about stride check
here.
For now, this is all we need to know.
Take away from the tensor attributes
As neural network programmers, we need to be aware of the following:

Tensors contain data of a uniform type (
dtype
). 
Tensor computations between tensors depend on the
dtype
and thedevice
.
Let's look now at the common ways of creating tensors using data in PyTorch.
Creating tensors using data
These are the primary ways of creating tensor objects (instances of the torch.Tensor
class), with data (arraylike) in PyTorch:

torch.Tensor(data)

torch.tensor(data)

torch.as_tensor(data)

torch.from_numpy(data)
Let's look at each of these. They all accept some form of data and give us an instance of the torch.Tensor
class. Sometimes when there are multiple ways to achieve the same result, things
can get confusing, so let's break this down.
We'll begin by just creating a tensor with each of the options and see what we get. We'll start by creating some data.
We can use a Python list, or sequence, but numpy.ndarray
s are going to be the more common option, so we'll go with a numpy.ndarray
like so:
> data = np.array([1,2,3])
> type(data)
numpy.ndarray
This gives us a simple bit of data with a type of numpy.ndarray
.
Now, let's create our tensors with each of these options 14, and have a look at what we get:
> o1 = torch.Tensor(data)
> o2 = torch.tensor(data)
> o3 = torch.as_tensor(data)
> o4 = torch.from_numpy(data)
> print(o1)
> print(o2)
> print(o3)
> print(o4)
tensor([1., 2., 3.])
tensor([1, 2, 3], dtype=torch.int32)
tensor([1, 2, 3], dtype=torch.int32)
tensor([1, 2, 3], dtype=torch.int32)
All of the options (o1
, o2
, o3
, o4
) appear to have produced the same tensors except for the first one. The first option (o1
) has dots after
the number indicating that the numbers are float
s, while the next three options have a type of int32
.
// Python code example of what we mean
> type(2.)
float
> type(2)
int
In the next post, we will look more deeply at this difference as well as a few other important differences that are lurking behind the scenes.
The discussion in the next post will allow us to see which of these options is best for creating tensors. For now, let's see some of the creation options available for creating tensors from scratch without having any data beforehand.
Creation options without data
Here are some other creation options that are available.
We have the torch.eye()
function which returns a 2D tensor with ones on the diagonal and zeros elsewhere. The name eye()
is connected to the idea of an
identity matrix
, which is a square matrix with ones on the main diagonal and zeros everywhere else.
> print(torch.eye(2))
tensor([
[1., 0.],
[0., 1.]
])
We have the torch.zeros()
function that creates a tensor of zeros with the shape of specified shape argument.
> print(torch.zeros([2,2]))
tensor([
[0., 0.],
[0., 0.]
])
Similarly, we have the torch.ones()
function that creates a tensor of ones.
> print(torch.ones([2,2]))
tensor([
[1., 1.],
[1., 1.]
])
We also have the torch.rand()
function that creates a tensor with a shape of the specified argument whose values are random.
> print(torch.rand([2,2]))
tensor([
[0.0465, 0.4557],
[0.6596, 0.0941]
])
This is a small subset of the available creation functions that don't require data. Check with the PyTorch documentation for the full list.
I hope now you have a good understanding of how we can use PyTorch to create tensors from using data as well as the built in functions that don't require data. This task is a breeze if we are using
numpy.ndarray
s, so congratulations if you are already familiar with NumPy.
In the next post, we will look a little more deeply at the creation options that require data, and we'll discover the differences between these options as well as see which options work best. See you in the next one!
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