In [0]:

    

import torch


    

In [2]:

    

## empty matrix
x = torch.empty(5,3)
x


    

    
Out[2]:





tensor([[1.8607e-35, 0.0000e+00, 0.0000e+00],
        [0.0000e+00, 0.0000e+00, 0.0000e+00],
        [0.0000e+00, 0.0000e+00, 2.8026e-45],
        [0.0000e+00, 1.1210e-44, 0.0000e+00],
        [1.4013e-45, 0.0000e+00, 0.0000e+00]])

In [4]:

    

## random matrix
x = torch.rand(3,2)
x


    

    
Out[4]:





tensor([[0.8732, 0.5434],
        [0.6724, 0.5409],
        [0.8621, 0.4036]])

In [6]:

    

## zero matrix with dtype as long. 
x = torch.zeros(5,3, dtype=torch.long)
x


    

    
Out[6]:





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

In [11]:

    

## prepare a tensor directly from data
data = [5.2, 3]
x = torch.tensor(data, dtype=torch.int)
x


    

    
Out[11]:





tensor([5, 3], dtype=torch.int32)

In [15]:

    

## create a new tensor from existing tensor by overriding
x = x.new_ones(2,2)

x = torch.randn_like(x, dtype=float)
x ## note the structure remains the same as the previous tensor


    

    
Out[15]:





tensor([[-1.3235,  0.7385],
        [ 3.0591,  0.0499]], dtype=torch.float64)

In [16]:

    

## size of tensor
x.size()


    

    
Out[16]:





torch.Size([2, 2])

In [18]:

    

## adding two tensors 
y = torch.rand(2,2)
print(y)
x.add(y)


    

    




tensor([[0.7648, 0.0676],
        [0.9298, 0.6317]])






    
Out[18]:





tensor([[-0.5587,  0.8061],
        [ 3.9888,  0.6815]], dtype=torch.float64)

In [21]:

    

print(x[:,1])


    

    




tensor([0.7385, 0.0499], dtype=torch.float64)

In [5]:

    

## numpy bridge

x = torch.ones(2,3)
x


    

    
Out[5]:





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

In [7]:

    

a = x.numpy()
a


    

    
Out[7]:





array([[1., 1., 1.],
       [1., 1., 1.]], dtype=float32)

In [0]: