PyTorch Tensors

通过大量实例学习编程应用是最有效的方法。

本篇是PyTorch综合运用，旨在让读者通过一行行代码亲自掌握Pytorch工具包的各种功能，有利于大家部署自己的神经网络人工智能计算工程。

首先，载入torch库。

import torch

我们来看看一些基本的张量操作。首先，只是一些创建张量的方法：

z = torch.zeros(5, 3)
print(z)
print(z.dtype)

输出为：

tensor([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]])
torch.float32

上面，我们创建了一个5x3的矩阵，里面充满了零，并查询它的数据类型来发现零是32位浮点数，这是PyTorch的默认值。

如果你想要整数呢？你总是可以覆盖默认值：

z = torch.zeros(5, 3)
print(z)
print(z.dtype)

输出为：

tensor([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]])
torch.float32

您可以看到，当我们更改默认值时，张量在打印时有用地报告了这一点。

随机初始化学习权重是很常见的，通常会为PRNG设置一个特定的种子，以获得结果的可重复性：

torch.manual_seed(1729)
r1 = torch.rand(2, 2)
print('A random tensor:')
print(r1)

r2 = torch.rand(2, 2)
print('\nA different random tensor:')
print(r2) # new values

torch.manual_seed(1729)
r3 = torch.rand(2, 2)
print('\nShould match r1:')
print(r3) # repeats values of r1 because of re-seed

输出为：

A random tensor:
tensor([[0.3126, 0.3791],
        [0.3087, 0.0736]])

A different random tensor:
tensor([[0.4216, 0.0691],
        [0.2332, 0.4047]])

Should match r1:
tensor([[0.3126, 0.3791],
        [0.3087, 0.0736]])

PyTorch张量直观地执行算术运算。相似形状的张量可以相加、相乘等。标量操作分布在张量上：

ones = torch.ones(2, 3)
print(ones)

twos = torch.ones(2, 3) * 2 # every element is multiplied by 2
print(twos)

threes = ones + twos       # addition allowed because shapes are similar
print(threes)              # tensors are added element-wise
print(threes.shape)        # this has the same dimensions as input tensors

r1 = torch.rand(2, 3)
r2 = torch.rand(3, 2)
# uncomment this line to get a runtime error
# r3 = r1 + r2

输出为：

tensor([[1., 1., 1.],
        [1., 1., 1.]])
tensor([[2., 2., 2.],
        [2., 2., 2.]])
tensor([[3., 3., 3.],
        [3., 3., 3.]])
torch.Size([2, 3])

下面是可用的数学运算的一个小示例：

r = (torch.rand(2, 2) - 0.5) * 2 # values between -1 and 1
print('A random matrix, r:')
print(r)

# Common mathematical operations are supported:
print('\nAbsolute value of r:')
print(torch.abs(r))

# ...as are trigonometric functions:
print('\nInverse sine of r:')
print(torch.asin(r))

# ...and linear algebra operations like determinant and singular value decomposition
print('\nDeterminant of r:')
print(torch.det(r))
print('\nSingular value decomposition of r:')
print(torch.svd(r))

# ...and statistical and aggregate operations:
print('\nAverage and standard deviation of r:')
print