# What is PyTorch?¶

It’s a Python based scientific computing package targeted at two sets of audiences:

• A replacement for NumPy to use the power of GPUs
• a deep learning research platform that provides maximum flexibility and speed

## Getting Started¶

### Tensors¶

Tensors are similar to NumPy’s ndarrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.

from __future__ import print_function
import torch


Construct a 5x3 matrix, uninitialized:

x = torch.Tensor(5, 3)
print(x)


Out:

0.0000e+00  0.0000e+00 -1.4973e+31
4.5722e-41 -1.5089e+31  4.5722e-41
-8.9654e+35  4.5722e-41 -8.9653e+35
4.5722e-41 -1.0677e+36  4.5722e-41
-1.0392e+36  4.5722e-41 -8.9680e+35
[torch.FloatTensor of size 5x3]


Construct a randomly initialized matrix:

x = torch.rand(5, 3)
print(x)


Out:

0.2455  0.1516  0.5319
0.9866  0.9918  0.0626
0.0172  0.6471  0.1756
0.8964  0.7312  0.9922
0.6264  0.0190  0.0041
[torch.FloatTensor of size 5x3]


Get its size:

print(x.size())


Out:

torch.Size([5, 3])


Note

torch.Size is in fact a tuple, so it supports all tuple operations.

### Operations¶

There are multiple syntaxes for operations. In the following example, we will take a look at the addition operation.

Addition: syntax 1

y = torch.rand(5, 3)
print(x + y)


Out:

0.2699  0.4096  1.0308
1.7155  1.0834  0.5545
0.2245  1.5612  0.8485
0.9799  1.1686  1.6989
1.5575  0.4184  0.3967
[torch.FloatTensor of size 5x3]


Addition: syntax 2

print(torch.add(x, y))


Out:

0.2699  0.4096  1.0308
1.7155  1.0834  0.5545
0.2245  1.5612  0.8485
0.9799  1.1686  1.6989
1.5575  0.4184  0.3967
[torch.FloatTensor of size 5x3]


Addition: providing an output tensor as argument

result = torch.Tensor(5, 3)
torch.add(x, y, out=result)
print(result)


Out:

0.2699  0.4096  1.0308
1.7155  1.0834  0.5545
0.2245  1.5612  0.8485
0.9799  1.1686  1.6989
1.5575  0.4184  0.3967
[torch.FloatTensor of size 5x3]


Addition: in-place

# adds x to y
y.add_(x)
print(y)


Out:

0.2699  0.4096  1.0308
1.7155  1.0834  0.5545
0.2245  1.5612  0.8485
0.9799  1.1686  1.6989
1.5575  0.4184  0.3967
[torch.FloatTensor of size 5x3]


Note

Any operation that mutates a tensor in-place is post-fixed with an _. For example: x.copy_(y), x.t_(), will change x.

You can use standard NumPy-like indexing with all bells and whistles!

print(x[:, 1])


Out:

0.1516
0.9918
0.6471
0.7312
0.0190
[torch.FloatTensor of size 5]


Resizing: If you want to resize/reshape tensor, you can use torch.view:

x = torch.randn(4, 4)
y = x.view(16)
z = x.view(-1, 8)  # the size -1 is inferred from other dimensions
print(x.size(), y.size(), z.size())


Out:

torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])


Read later:

100+ Tensor operations, including transposing, indexing, slicing, mathematical operations, linear algebra, random numbers, etc., are described here.

## NumPy Bridge¶

Converting a Torch Tensor to a NumPy array and vice versa is a breeze.

The Torch Tensor and NumPy array will share their underlying memory locations, and changing one will change the other.

### Converting a Torch Tensor to a NumPy Array¶

a = torch.ones(5)
print(a)


Out:

1
1
1
1
1
[torch.FloatTensor of size 5]

b = a.numpy()
print(b)


Out:

[ 1.  1.  1.  1.  1.]


See how the numpy array changed in value.

a.add_(1)
print(a)
print(b)


Out:

2
2
2
2
2
[torch.FloatTensor of size 5]

[ 2.  2.  2.  2.  2.]


### Converting NumPy Array to Torch Tensor¶

See how changing the np array changed the Torch Tensor automatically

import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
np.add(a, 1, out=a)
print(a)
print(b)


Out:

[ 2.  2.  2.  2.  2.]

2
2
2
2
2
[torch.DoubleTensor of size 5]


All the Tensors on the CPU except a CharTensor support converting to NumPy and back.

## CUDA Tensors¶

Tensors can be moved onto GPU using the .cuda method.

# let us run this cell only if CUDA is available
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
x + y


Total running time of the script: ( 0 minutes 1.549 seconds)

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