PyTorch入门——什么是Pytorch？_pytorch是一个包吗

一、什么是PyTorch

        Pytorch是以一个基于Python的科学计算包，其目标有两个：

1. 替代Numpy使用GPU来处理数据；
2. 提供最大灵活性和速度的深度学习研究平台。

二、如何使用Pytorch处理数据

        Pytorch以Tensor（张量）来组织数据，张量类似于数组，不同的是张量可以使用GPU来加速计算。

        下面从五个方面介绍Tensor的基本用法：

import torch

#Part 1 创建Tensor
###############################################################
# 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 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
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#Part 2 Tensor的运算
###############################################################
# Get its size:
print(x.size())

# .. 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)

# 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)

# .. note::
#     Any operation that mutates a tensor in-place is post-fixed with an ``_``.
#     For example: ``x.copy_(y)``, ``x.t_()``, will change ``x``.
#
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#Part 3 Tensor的索引和resize
###############################################################
# 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())

#   `here <https://pytorch.org/docs/torch>`_.
#
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#Part 4 Tensor与Numpy的转换
###############################################################
# 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)
#
b = a.numpy()
print(b)


# See how the numpy array changed in value.

a.add_(1)	#只会改变a的值
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)
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#Part 5 使用GPU计算Tensor
###############################################################
# 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!

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