Pytorch 基礎

摘要： Pytorch 1.0.0 學習筆記： Pytorch 的學習可以參考：Welcome to PyTorch Tutorials Deep Learning with PyTorch: A 60 Minute Blitz 快速上手 Pytorch! Tensor...

Pytorch 1.0.0 學習筆記：

Pytorch 的學習可以參考：Welcome to PyTorch Tutorials

Deep Learning with PyTorch: A 60 Minute Blitz

快速上手 Pytorch!

Tensors(張量)

from __future__ import print_function
import torch

建立一個沒有初始化的\(5\times 3\) 矩陣：

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

tensor([[0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00],
[0.0000e+00, 1.9730e-42, 0.0000e+00],
[0.0000e+00, 7.3909e+22, 0.0000e+00]])

建立一個已經初始化的\(5\times 3\) 的隨機矩陣：

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

tensor([[0.2496, 0.8405, 0.7555],
[0.9820, 0.9988, 0.5419],
[0.6570, 0.4990, 0.4165],
[0.6985, 0.9972, 0.4234],
[0.0096, 0.6374, 0.8520]])

給定資料型別為long 的\(5\times 3\) 的全零矩陣：

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]])

直接從list 中建立張量：

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

tensor([5.5000, 3.0000])

直接從Numpy 中建立張量：

import numpy as np
a = np.array([2, 3, 5], dtype='B')
x = torch.tensor(a)# numpy
print(x)
x.numel()# Tensor 中元素的個數

tensor([2, 3, 5], dtype=torch.uint8)





3

x = torch.rand(5, 3)
size = x.size()
print(size)
h, w = size
h, w

torch.Size([5, 3])





(5, 3)

Operations(運算)

Tensor 的運算大都與 Numpy 相同，下面僅僅介紹一些特殊的運算方式：

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)

tensor([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]], dtype=torch.float64)
tensor([[-0.9367, -0.1121,1.9103],
[ 0.2284,0.3823,1.0877],
[-0.2797,0.7217, -0.7032],
[ 0.9047,1.7789,0.4215],
[-1.0368, -0.2644, -0.7948]])

result = torch.empty(5, 3)# 建立一個為初始化的矩陣
y = torch.rand(5, 3)

torch.add(x, y, out=result) # 計算 x + y 並將結果賦值給 result
print(result)

tensor([[-0.0202,0.6110,2.8150],
[ 1.0288,1.2454,1.7464],
[-0.1786,0.8212, -0.2493],
[ 1.5294,2.2713,0.8383],
[-0.9292,0.5749, -0.1146]])

任何一個 可變的 tensor 所對應的運算在其適當的位置後加上_ ， 便會修改原 tensor 的值：

x = torch.tensor([7])
y = torch.tensor([2])
print(y, y.add(x))
print(y, y.add_(x))
y

tensor([2]) tensor([9])
tensor([9]) tensor([9])





tensor([9])

x = torch.tensor(7)
x.item()# 轉換為 python 的 number

reshape tensor：veiw()

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])

NumPy Bridge(與 Numpy 互動)

Tensor 轉換為 Numpy

a = torch.ones(5)
print(a)

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

Tensor 轉換為 Numpy

b = a.numpy()
print(b)

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

_ 的作用依然存在：

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

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

Numpy 轉換為 Tensor

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)

CUDA

使用.to 方法，Tensors 可被移動到任何 device：

# 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(8, device='cuda:0')
tensor(8., dtype=torch.float64)