## torch create tensor: Construct a PyTorch Tensor

torch create tensor - Create an uninitialized PyTorch Tensor and an initialized PyTorch Tensor

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### Transcript:

We import PyTorch.

```
import torch
```

We’re going to print the torch version to see what version we’re using.

```
print(torch.__version__)
```

We’re using 0.2.0_4.

To construct a PyTorch tensor, we define a variable x and set it equal to torch.Tensor(5,1).

```
x = torch.Tensor(5, 1)
```

We can then print that tensor to see that it is a torch.FloatTensor of size 5x1.

```
print(x)
```

It is uninitialized.

By default, the PyTorch tensors are created using floats.

We can create a second tensor, y, using torch.Tensor(1,5).

```
y = torch.Tensor(1, 5)
```

We can print the y tensor and it is a FloatTensor of size 1x5.

```
print(y)
```

It is uninitialized.

Next, we define a tensor z and we set it equal to torch.Tensor(2, 2, 2).

This is going to be a three-dimensional tensor.

```
z = torch.Tensor(2, 2, 2)
```

When we print it, we can see that it is 2x2, 2x2, it is uninitialized, and that there are two of these matrices.

```
print(z)
```

Again, it is torch.FloatTensor of size 2x2x2.

Here, we’re going to construct a random tensor variable and we’re going to use torch.rand(3, 3, 3).

What this does is it creates a tensor based on the arguments we passed.

So it’s going to be 3x3x3.

```
random_tensor = torch.rand(3, 3, 3)
```

And when we print it, you can see that it has numbers that are all floating numbers.

```
print(random_tensor)
```

This rand function in PyTorch gives you a random number pulled from a uniform distribution from 0 to 1.

So you can see that all of the numbers here displayed are between 0 and 1.