一:老版本的 variable
二:新版 tensor
曾经:求导相关
如今:数据相关
--dtype: 张量的数据类型,三大类,共9种。torch.FloatTensor, torch.cuda.FloatTensor
--shape: 张量的形状。如:(64,3,224,224)
--decive: 所在设备
三:tensor创建方法
Tensor创建一:直接创建 (共享内存:out)
1)
torch.tensor(data, dtype=None, device=None, requires_grad=False, pin_memory=False)
2) 从numpy创建tensor : torch.from_numpy(ndarray)
注意:共享内容
python
import torch
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
t = torch.from_numpy(arr)
t[0, 0] = -1
print(arr)
print(t)
Tensor创建二:依据数值创建(等差,均分,单位矩阵)
4.2 依据数值创建
1)torch.zeros():按照size创建全0张量
torch.zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False)
-
21torch.zeros_like()
torch.zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False)
-
torch.ones()
-
torch.ones_like()
-
torch.full()
-
torch.full_like()
-
torch.arange(),创建等差数列,区间:[start, end,数值) 等差创建
6.torch.linspace(),创建均分数列,区间:[start, end]
注意:step是步长;steps是长度
t = torch.linspace(start=0, end=100, steps=5, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False)
Tensor创建三:依据概率创建(正太,标准正太,伯努利分布)
第二章:Tensor 操作与线性回归
2.1:张量的操作:拼接、切分、索引和变换
2.1.1拼接:
- torch.cat(): 将张量按维度dim进行拼接
python
t_0 = torch.cat([t, t], dim=0)
t_1 = torch.cat([t, t], dim=1)
torch.Size([4, 3])
torch.Size([2, 6])
- torch.stack():在新建的维度dim上进行拼接
python
t = torch.ones((2, 3))
t_0 = torch.cat([t, t], dim=0)
t_1 = torch.stack([t, t], dim=0)
print(t_0)
print(t_0.shape)
print(t_1)
print(t_1.shape)
2.1.2 切分
按维度dim进行平均切分: torch.chunk(input, chunks, dim)
python
t = torch.ones((2, 7))
print(t)
list_of_tensor = torch.chunk(t, dim=1, chunks=3)
print(list_of_tensor)
指定切分:torch.split() [2,2,3]切分三个,每个都有特定的te'zheng'sh
python
t = torch.ones((2, 7))
print(t)
list_of_tensor_2 = torch.split(t, 3, dim=1)
print(list_of_tensor_2)
list_of_tensor_3 = torch.split(t, [2, 2, 3], dim=1)
print(list_of_tensor_3)
2.1.3 reshape
torch.reshape: 变换张量形状
notice: 注意事项:当张量在内存中是连续时,新张 量与input共享数据内存
python
# torch.reshape
t = torch.randperm(8)
print(t)
t_reshape = torch.reshape(t, (2, 4)) # -1代表不关心
print(t_reshape)
2.2张量的数学运算
2.3线性回归
用wx+b 去拟合谁?
逻辑回归:
Deepshare-Pytorch/作业/第二周作业1/第二周作业1.md at master · 799609164/Deepshare-Pytorch · GitHub