pytorch笔记——pytorch基础

import torch
import torchvision
print(torch)

随机生成

x=torch.rand(5,3)
print(x)
'''
tensor([[0.8781, 0.6810, 0.4982],
        [0.7308, 0.6844, 0.8016],
        [0.9965, 0.7614, 0.0788],
        [0.6502, 0.6808, 0.0835],
        [0.0102, 0.9330, 0.2545]])
'''

构造一个矩阵，不初始化

x=torch.empty(5,3)
print(x)
'''
tensor([[1.0286e-38, 1.0194e-38, 9.6429e-39],
        [9.2755e-39, 9.1837e-39, 9.3674e-39],
        [1.0745e-38, 1.0653e-38, 9.5510e-39],
        [1.0561e-38, 1.0194e-38, 1.1112e-38],
        [1.0561e-38, 9.9184e-39, 1.0653e-38]])
'''

生成0

x=torch.zeros(5,3,)
print(x)
'''
tensor([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]])
'''

构造张量

x=torch.tensor([5,3],dtype=torch.float)
print(x)
x=torch.tensor([5.56,3],dtype=torch.int32)
print(x)
x=torch.tensor([5.56,3],dtype=torch.float)
print(x)
'''
tensor([5., 3.])
tensor([5, 3], dtype=torch.int32)
tensor([5.5600, 3.0000])
'''

创建一个tensor，基于已经存在的tensor

x=x.new_ones(5,3)
x=torch.randn_like(x,dtype=torch.float)
print(x)
print(x)
'''
tensor([[-1.3639,  0.8326, -0.4899],
        [ 1.6127, -2.0743,  1.5063],
        [-1.1452, -0.9633, -0.0357],
        [-2.1144, -1.9547,  1.2708],
        [-1.2065,  0.7668,  0.1674]])
tensor([[-1.3639,  0.8326, -0.4899],
        [ 1.6127, -2.0743,  1.5063],
        [-1.1452, -0.9633, -0.0357],
        [-2.1144, -1.9547,  1.2708],
        [-1.2065,  0.7668,  0.1674]])
'''
print(x.size())
'''
torch.Size([1])
'''

加法

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

print(torch.add(x,y))

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

y.add_(x)
print(y)
'''
tensor([[-0.4606,  1.4884,  0.1468],
        [ 2.0469, -1.7316,  2.3033],
        [-0.3407, -0.9139,  0.9012],
        [-1.8327, -1.5334,  2.1550],
        [-0.5702,  1.7230,  1.0674]])
tensor([[-0.4606,  1.4884,  0.1468],
        [ 2.0469, -1.7316,  2.3033],
        [-0.3407, -0.9139,  0.9012],
        [-1.8327, -1.5334,  2.1550],
        [-0.5702,  1.7230,  1.0674]])
tensor([[-0.4606,  1.4884,  0.1468],
        [ 2.0469, -1.7316,  2.3033],
        [-0.3407, -0.9139,  0.9012],
        [-1.8327, -1.5334,  2.1550],
        [-0.5702,  1.7230,  1.0674]])
tensor([[-0.4606,  1.4884,  0.1468],
        [ 2.0469, -1.7316,  2.3033],
        [-0.3407, -0.9139,  0.9012],
        [-1.8327, -1.5334,  2.1550],
        [-0.5702,  1.7230,  1.0674]])
'''

类似列表的索引

print(x[:,1])
'''
tensor([ 0.8326, -2.0743, -0.9633, -1.9547,  0.7668])
'''

改变大小

x=torch.randn(4,4)
y=x.view(16)
z=x.view(-1,8)#-1表示根据另一列推断
print(x.size(),y.size(),z.size())
'''
torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])
'''

只有1个元素的时候可以用.item()获得值

x=torch.rand(1)
print(x)
print(x.item())
y=torch.rand(5,5)
print(y)
print(y.item())
'''
tensor([0.6056])
0.6056373119354248
tensor([[0.5475, 0.7503, 0.6943, 0.1225, 0.1180],
        [0.1781, 0.6898, 0.0210, 0.5957, 0.1206],
        [0.7138, 0.4520, 0.5671, 0.5965, 0.1314],
        [0.5944, 0.8185, 0.4200, 0.9304, 0.8873],
        [0.0336, 0.6716, 0.7224, 0.3542, 0.9614]])

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-57-04dec2814a89> in <module>
      4 y=torch.rand(5,5)
      5 print(y)
----> 6 print(y.item())

ValueError: only one element tensors can be converted to Python scalars



'''

自动广播

x=torch.arange(1,3).view(1,2)
print(x)
y=torch.arange(1,4).view(3,1)
print(y)
print(x+y)
'''tensor([[1, 2]])
tensor([[1],
        [2],
        [3]])
tensor([[2, 3],
        [3, 4],
        [4, 5]])
'''