%matplotlib inline
It’s a Python based scientific computing package targeted at two sets of audiences:
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.empty(5, 3)
print(x)
Construct a randomly initialized matrix:
x = torch.rand(5, 3)
print(x)
Construct a matrix filled zeros and of dtype long:
x = torch.zeros(5, 3, dtype=torch.long)
print(x)
Construct a tensor directly from data:
x = torch.tensor([5.5, 3])
print(x)
or create a tensor basing on existing tensor. These methods will reuse properties of the input tensor, e.g. dtype, unless new values are provided by user
x = x.new_ones(5, 3, dtype=torch.double) # new_* methods take in sizes
print(x)
x = torch.randn_like(x, dtype=torch.float) # override dtype!
print(x) # result has the same size
Get its size:
print(x.size())
``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)
Addition: syntax 2
print(torch.add(x, y))
Addition: providing an output tensor as argument
result = torch.empty(5, 3)
torch.add(x, y, out=result)
print(result)
Addition: in-place
# adds x to y
y.add_(x)
print(y)
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])
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())
If you have a one element tensor, use .item() to get the value as a
Python number
x = torch.randn(1)
print(x)
print(x.item())
Read later:
100+ Tensor operations, including transposing, indexing, slicing,
mathematical operations, linear algebra, random numbers, etc.,
are described
here <http://pytorch.org/docs/torch>_.
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)
b = a.numpy()
print(b)
See how the numpy array changed in value.
a.add_(1)
print(a)
print(b)
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)
All the Tensors on the CPU except a CharTensor support converting to NumPy and back.
Tensors can be moved onto any device using the .to method.
# let us run this cell only if CUDA is available
# We will use ``torch.device`` objects to move tensors in and out of GPU
if torch.cuda.is_available():
y = torch.ones_like(x).cuda()
x = x.cuda() # or just use strings ``.to("cuda")``
z = x + y
print(z)
print(z.cpu()) # ``.to`` can also change dtype together!