Pytorch入门初体验(一)

Pytorch是python的一个科学计算包,主要有两方面的好处:

(1)它可以替代NumPy,充分的利用GPU的算力;

(2)是一个深度学习的研究平台,提供最大的灵活性和速度。

下面开始学习记录:

1. tensor:

Example:

import torch   #导入pytorch

#Construct a 5X3 matrix, uninitialized:
x = torch.empty(5, 3)
print(x) 

输出:

tensor([[ 3.2401e+18,  0.0000e+00,  1.3474e-08],
        [ 4.5586e-41,  1.3476e-08,  4.5586e-41],
        [ 1.3476e-08,  4.5586e-41,  1.3474e-08],
        [ 4.5586e-41,  1.3475e-08,  4.5586e-41],
        [ 1.3476e-08,  4.5586e-41,  1.3476e-08]])

Example 2:

import torch

Construct a tensor directly from data:
x = torch.tensor([5.5, 3])
print(x)

输出:

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

Example 3:

import tensor

Construct a tensor directly from data:
x = torch.tensor([5.5, 3])
print(x)

输出:

tensor([ 5.5000,  3.0000])

Example 4:

import torch

#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

输出:

tensor([[ 1.,  1.,  1.],
        [ 1.,  1.,  1.],
        [ 1.,  1.,  1.],
        [ 1.,  1.,  1.],
        [ 1.,  1.,  1.]], dtype=torch.float64)
tensor([[ 0.2641,  0.0149,  0.7355],
        [ 0.6106, -1.2480,  1.0592],
        [ 2.6305,  0.5582,  0.3042],
        [-1.4410,  2.4951, -0.0818],
        [ 0.8605,  0.0001, -0.7220]])

Example 5:

import torch

#Gets its size
print(x.size())

输出

torch.Size([5, 3])

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


2. 操作(Operations):

对于操作存在许多语法,下面的例子主要展现一些额外的操作。

Addition: syntax 1

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

Out:

tensor([[ 0.7355,  0.2798,  0.9392],
        [ 1.0300, -0.6085,  1.7991],
        [ 2.8120,  1.2438,  1.2999],
        [-1.0534,  2.8053,  0.0163],
        [ 1.4088,  0.9000, -0.1172]])

Addition: syntax 2

print(torch.add(x+y))

Out:

tensor([[ 0.7355,  0.2798,  0.9392],
        [ 1.0300, -0.6085,  1.7991],
        [ 2.8120,  1.2438,  1.2999],
        [-1.0534,  2.8053,  0.0163],
        [ 1.4088,  0.9000, -0.1172]])

Addition: syntax 3

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

Out:

tensor([[ 0.7355,  0.2798,  0.9392],
        [ 1.0300, -0.6085,  1.7991],
        [ 2.8120,  1.2438,  1.2999],
        [-1.0534,  2.8053,  0.0163],
        [ 1.4088,  0.9000, -0.1172]])

Addition: 

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

Out:

tensor([[ 0.7355,  0.2798,  0.9392],
        [ 1.0300, -0.6085,  1.7991],
        [ 2.8120,  1.2438,  1.2999],
        [-1.0534,  2.8053,  0.0163],
        [ 1.4088,  0.9000, -0.1172]])
Any operation that mutates a tensor in-place is post-fixed with an _. For example: x.copy_(y)x.t_(), will change x.


print(x[:, 1])   #和numpy的操作一样

Out:

tensor([ 0.0149, -1.2480,  0.5582,  2.4951,  0.0001])

如果想要resize/reshape tensor,可以使用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])

如果是只有一个元素的tensor,可以使用.item()获得它的值:

x = torch.randn(1)
print(x)
print(x.item())

Out:

tensor([ 1.3159])
1.3159412145614624


3. NumPy Bridge

torch Tensor和NumPy的array相互转换,它们公用一个内存地址,改变一个另外一个也会发生改变。

(1)Torch Tensor转换为NumPy array

a = torch.ones(5)
print(a)
Out:
tensor([ 1.,  1.,  1.,  1.,  1.])
b = a.numpy()
print(b)
Out:
[1. 1. 1. 1. 1.]

改变a看b的变化:

a.add_(1)
print(a)
print(b)
Out:
tensor([ 2.,  2.,  2.,  2.,  2.])
[2. 2. 2. 2. 2.]

(2) NumPy array转换为Torch Tensor

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

4. CUDA tensors

tensor可以使用.to移动到任何设备上

# 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!
Out:
tensor([ 2.3159], device='cuda:0')
tensor([ 2.3159], dtype=torch.float64)


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