# pytorch教程之Tensor的值及操作使用学习

## 1、Tensors

Tensors are similar to NumPy's ndaeeays,不同的是可以在GPU上使用和加速计算。

```from __future__ import print_function
import torch```

### 建立5*3的矩阵，未初始化

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

out

```tensor([[ 1.4395e-36,  4.5848e-41,  1.4395e-36],
[ 4.5848e-41,  1.4395e-36,  4.5848e-41],
[ 1.4395e-36,  4.5848e-41,  2.8026e-45],
[-1.9501e+00,  8.5165e+23,  0.0000e+00],
[ 2.5223e-43,  0.0000e+00,  0.0000e+00]])```

### 建立随机初始化矩阵

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

out

```tensor([[ 0.8074,  0.9175,  0.8109],
[ 0.3313,  0.5902,  0.9179],
[ 0.6562,  0.3283,  0.9798],
[ 0.8218,  0.0817,  0.4454],
[ 0.5934,  0.0040,  0.3411]])```

### 建立零初始化矩阵，数据类型是Long

```...
x = torch.zeros(5,3,dtype = torch.long)
print(x)
...```

out

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

### 建立一个tensor数据来源于data

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

out

`tensor([ 5.5000,  3.0000])`

```x = x.new_ones(5,3,dtype = torch.double)
print(x)
x = torch.randn_like(x,dype = torch.float)
print(x)```

out

```tensor([[ 1.,  1.,  1.],
[ 1.,  1.,  1.],
[ 1.,  1.,  1.],
[ 1.,  1.,  1.],
[ 1.,  1.,  1.]], dtype=torch.float64)
tensor([[-0.0730, -0.0716, -0.8259],
[-1.7004,  0.8790, -0.0659],
[-0.8969,  0.8736, -0.6035],
[-0.1539, -2.9178, -0.7456],
[-0.0245,  0.4075,  1.4904]])```

### 获取tensor的size

`print(x.size())`

out

`torch.Size([5, 3])`

torch.size是一个元组，支持所有元组(tuple)的操作

## 2、对Tensor的操作

### 实现加法的四种方式

`print(x+y)`

`print(torch.add(x,y))`

```result = torch.empty(5,3)
print (result)```

`print(y)`

### 使用标准的numpy操作

`print(x[：1]`

out

`tensor([-0.0716,  0.8790,  0.8736, -2.9178,  0.4075])`

### 使用torch.view 改变tensor的形状

```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.xize(),z.size())```

out

`torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])`

### tensor转化为numpy的数字，使用item

```x = torch.rnadn(1)
print(x)
print(x.item())```

### Torch Tensor 和numpy的相互转换

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

out

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

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

out

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

### 将numpy array转化为pytorch Tensor

```import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
print(a)
print（b）```

out

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

### CUDA Tensors

Tensors 可以被移动到其他设备使用.to的方法

```...
if torch.cuda.is_avaulable():
device = torch.device(“cuda”)
y = torch.ones_like(x,device = devcie)
x= x.to(device)
z = x+y
print(z)
print(z.to(“cpu”,torch.double))
...```

out

```tensor([-1.0620], device='cuda:0')
tensor([-1.0620], dtype=torch.float64)```