#tensor和numpy
import torch
import numpy as np

numpy_tensor = np.random.randn(3,4)
print(numpy_tensor)
#将numpy的ndarray转换到tendor上
pytorch_tensor1 = torch.Tensor(numpy_tensor)
pytorch_tensor2 = torch.from_numpy(numpy_tensor)
print(pytorch_tensor1)
print(pytorch_tensor2)
#将pytorch的tensor转换到numpy的ndarray
numpy_array = pytorch_tensor1.numpy()   #如果pytorch在cpu上
print(numpy_array)
#tensor的一些属性,得到tensor的大小
print(pytorch_tensor1.shape)
print(pytorch_tensor1.size())
print(pytorch_tensor1.type()) #得到tensor的数据类型
print(pytorch_tensor1.dim()) #得到tensor的维度
print(pytorch_tensor1.numel()) #得到tensor所有元素的个数

x = torch.rand(3,2)
x.type(torch.DoubleTensor)
print(x)
np_array = x.numpy()
print(np_array.dtype)

[[ 1.05174423  1.09272735  0.46027768 -0.03255727]
 [ 0.57027229  1.22165706 -0.77909099 -0.17678552]
 [ 0.02112402 -1.08971068  0.72317744 -1.45482622]]
tensor([[ 1.0517,  1.0927,  0.4603, -0.0326],
        [ 0.5703,  1.2217, -0.7791, -0.1768],
        [ 0.0211, -1.0897,  0.7232, -1.4548]])
tensor([[ 1.0517,  1.0927,  0.4603, -0.0326],
        [ 0.5703,  1.2217, -0.7791, -0.1768],
        [ 0.0211, -1.0897,  0.7232, -1.4548]], dtype=torch.float64)
[[ 1.0517442   1.0927273   0.46027768 -0.03255726]
 [ 0.57027227  1.221657   -0.779091   -0.17678553]
 [ 0.02112402 -1.0897107   0.72317743 -1.4548262 ]]
torch.Size([3, 4])
torch.Size([3, 4])
torch.FloatTensor
2
12
tensor([[0.1810, 0.5168],
        [0.9859, 0.1294],
        [0.9262, 0.6952]])
float32

#Tensor的操作1
import torch
x = torch.ones(2,3)
print(x)
print(x.type())
x = x.long()
print(x.type())
x = x.float()
print(x.type())

y = torch.rand(3,4)
print(y)
#沿着行取最大值
maxval,maxindex = torch.max(y,dim=1)
print(maxval,’\n’,maxindex)

#沿着行对y求和
sum = torch.sum(y,dim=1)
print(sum)

tensor([[1., 1., 1.],
        [1., 1., 1.]])
torch.FloatTensor
torch.LongTensor
torch.FloatTensor
tensor([[0.8910, 0.0130, 0.9600, 0.6760],
        [0.5184, 0.6240, 0.9589, 0.2151],
        [0.6904, 0.3474, 0.7502, 0.2055]])
tensor([0.9600, 0.9589, 0.7502]) 
 tensor([2, 2, 2])
tensor([2.5400, 2.3164, 1.9936])

#Tensor操作2
import torch

x = torch.rand(3,2)
print(x)
print(x.size())
#增加一个维度
x = x.unsqueeze(0)
print(x.size())
#减少一个维度
x = x.squeeze(0)
print(x.size())
#增加回来
x = x.unsqueeze(1)
print(x.size())
#使用permute和transpose来对矩阵维度进行变换
#permute 可以重新排列tensor的维度
#transpose 可以交换两个维度
x = x.permute(1,0,2)
print(x.size())
x = x.transpose(0,2)
print(x.size())

tensor([[0.9131, 0.2160],
        [0.0987, 0.5013],
        [0.1715, 0.8862]])
torch.Size([3, 2])
torch.Size([1, 3, 2])
torch.Size([3, 2])
torch.Size([3, 1, 2])
torch.Size([1, 3, 2])
torch.Size([2, 3, 1])

#使用view对tensor进行reshape

import torch
x = torch.rand(3,4,5)
print(x.shape)
x = x.view(-1,5)
print(x.size())
x = x.view(60)
print(x.shape)

#两个Tensor求和
a = torch.rand(3,4)
b = torch.rand(3,4)
c = a + b
print(c)
z = torch.add(a,b)
print(z)

torch.Size([3, 4, 5])
torch.Size([12, 5])
torch.Size([60])
tensor([[0.8822, 1.3766, 1.3586, 0.8951],
        [1.0096, 0.5511, 0.2035, 0.9684],
        [1.2502, 0.0963, 1.3955, 0.9479]])
tensor([[0.8822, 1.3766, 1.3586, 0.8951],
        [1.0096, 0.5511, 0.2035, 0.9684],
        [1.2502, 0.0963, 1.3955, 0.9479]])

import torch
x = torch.ones(4,4)
print(x)
x[1:3,1:3] = 2
print(x)

tensor([[1., 1., 1., 1.],
        [1., 1., 1., 1.],
        [1., 1., 1., 1.],
        [1., 1., 1., 1.]])
tensor([[1., 1., 1., 1.],
        [1., 2., 2., 1.],
        [1., 2., 2., 1.],
        [1., 1., 1., 1.]])