Pytorch官方教程学习笔记（1）

注：本教程在pytorch官方教程的基础上修改得到，原教程请点击pytorch_tutorial

%matplotlib inline

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.empty(5, 3)
print(x)

tensor([[                       0.0000,                        0.0000,
                                0.0000],
        [                       0.0000,                        0.0000,
                                0.0000],
        [-17052573744263721910272.0000,                        0.0000,
                                0.0000],
        [                       0.0000,                        0.0000,
                                0.0000],
        [-17066354759123475628032.0000,                        0.0000,
                                0.0000]])

Construct a randomly initialized matrix:

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

tensor([[0.6351, 0.4463, 0.1681],
        [0.6738, 0.3523, 0.2223],
        [0.2257, 0.7201, 0.4833],
        [0.6948, 0.3860, 0.3745],
        [0.4248, 0.7636, 0.7570]])

Construct a matrix filled zeros and of dtype long:

x = torch.zeros(5, 3, dtype=torch.long)
print(x)

tensor([[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]])

Construct a tensor directly from data:

x = torch.tensor([5.5, 3])
print(x)

tensor([5.5000, 3.0000])

or create a tensor based on an 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

tensor([[1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.]], dtype=torch.float64)
tensor([[ 0.2069,  1.2877, -0.5166],
        [-1.3450,  0.2592,  1.2161],
        [ 0.4427,  0.8569,  0.1560],
        [ 1.1272, -1.3213,  0.9066],
        [-1.0506,  0.3604, -0.4053]])

Get its size:

print(x.size())

torch.Size([5, 3])

torch.Sizeis 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)

tensor([[ 0.6790,  1.6560, -0.2253],
        [-1.1419,  0.2704,  2.1830],
        [ 0.6909,  1.2795,  0.8529],
        [ 1.5204, -0.3262,  1.2432],
        [-1.0165,  0.7596, -0.1896]])

Addition: syntax 2

print(torch.add(x, y))

tensor([[ 0.6790,  1.6560, -0.2253],
        [-1.1419,  0.2704,  2.1830],
        [ 0.6909,  1.2795,  0.8529],
        [ 1.5204, -0.3262,  1.2432],
        [-1.0165,  0.7596, -0.1896]])

Addition: providing an output tensor as argument

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

tensor([[ 0.6790,  1.6560, -0.2253],
        [-1.1419,  0.2704,  2.1830],
        [ 0.6909,  1.2795,  0.8529],
        [ 1.5204, -0.3262,  1.2432],
        [-1.0165,  0.7596, -0.1896]])

Addition: in-place

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

tensor([[ 0.6790,  1.6560, -0.2253],
        [-1.1419,  0.2704,  2.1830],
        [ 0.6909,  1.2795,  0.8529],
        [ 1.5204, -0.3262,  1.2432],
        [-1.0165,  0.7596, -0.1896]])

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])

tensor([ 1.2877,  0.2592,  0.8569, -1.3213,  0.3604])

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())

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

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())

tensor([-1.2625])
-1.2625398635864258

Read later:

100+ Tensor operations, including transposing, indexing, slicing,
mathematical operations, linear algebra, random numbers, etc.,
are described
here <http://pytorch.org/docs/torch>_.

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)

tensor([1., 1., 1., 1., 1.])

b = a.numpy()
print(b)

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

See how the numpy array changed in value.

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

tensor([2., 2., 2., 2., 2.])
[ 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)

[ 2.  2.  2.  2.  2.]
tensor([2., 2., 2., 2., 2.], dtype=torch.float64)

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

CUDA Tensors

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():
    device = torch.device("cuda")          # a CUDA device object
    y = torch.ones_like(x, device=device)  # directly create a tensor on GPU
    x = x.to(device)                       # or just use strings ``.to("cuda")``
    z = x + y
    print(z)
    print(z.to("cpu", torch.double))       # ``.to`` can also change dtype together!

tensor([-0.2625], device='cuda:0')
tensor([-0.2625], dtype=torch.float64)