In the PyTorch deep learning model, all of your data inputs, outputs, and learning weights are going to be expressed as tensors. It is a multi-dimensional array that can contain floating-point integer or boolean data.

## Initialize Tensors

In the previous tutorial, we initialize tensors from a Python list and NumPy array. In particular, in this tutorial, we’re going to only cover how to create **empty** PyTorch tensors.

We have the very simplest way to create a tensor is the torch.empty() method. PyTorch torch module has multiple factory methods that will let you create tensors **with and without initial values** and whatever datatype you need.

```
import torch
x=torch.empty(2,3,dtype=torch.float32)
print(type(x)) #<class 'torch.Tensor'>
print(x)
```

###### Output:

```
<class 'torch.Tensor'>
tensor([[-9.8166e-38, 4.3524e-41, -9.8166e-38],
[ 4.3524e-41, 0.0000e+00, 0.0000e+00]])
```

This is the most basic way to allocate tensor. Here it’s going to create a 3×4 tensor and you can see that the object itself is of type `torch.tensor`

.

When you create a tensor using `torch.empty()`

you may see **random-looking values** in the output that’s because `torch.empty()`

just allocates memory and does not write any values to it so whatever happened to be a memory at the time you allocated this tensor is what you’re going to see here.

### Related Post

How to change the PyTorch tensor type?

Concatenates PyTorch tensors using Stack and Cat with Dimension