snippet_tensor_basic_1.py

import torch  
#Creates two tensor objects 
#Where X is a simple 1 Dimentional Tensor 
#Y is a vector
X = torch.tensor(1.0)
Y = torch.tensor([1.0,2.0])
#Alternatively we can also create a tensor from data like this 
Z = torch.tensor([[1.0,2.0,3.0],
                  [2.0,3.0,4.0],
                  [3.0,4.0,5.0]])

snippet_tensor_basic_2.py

print('Shape of X :',X.size())
print('Shape of Y :',Y.size())
print('Shape of Z :',z.size())

snippet_tensor_basic_3.py

#we can create Tensors with different default values using 
#Fills with uniform distribution from (0,1]
A = torch.rand(3,3) 
#Fills random numbers between mean 0 and variance 1 
B = torch.randn(3,3) 
#Both creates a 3x3 tensor
#Uninitalized data, could be anything :P 
C = torch.empty(20,20) 
#strictly zeros
D = torch.zeros(5,5)  
#We can get the type and also size using following
print(C.dtype)
print(C.size())

snippet_tensor_full_file.py

import torch  
#Creates two tensor objects 
#Where X is a simple 1 Dimentional Tensor 
#Y is a vector
X = torch.tensor(1.0)
Y = torch.tensor([1.0,2.0])
#Alternatively we can also create a tensor from data like this 
Z = torch.tensor([[1.0,2.0,3.0],
                  [2.0,3.0,4.0],
                  [3.0,4.0,5.0]])
print('Shape of X :',X.size())
print('Shape of Y :',Y.size())
print('Shape of Z :',z.size())
#we can create Tensors with different default values using 
#Fills with uniform distribution from (0,1]
A = torch.rand(3,3) 
#Fills random numbers between mean 0 and variance 1 
B = torch.randn(3,3) 
#Both creates a 3x3 tensor
#Uninitalized data, could be anything :P 
C = torch.empty(20,20) 
#strictly zeros
D = torch.zeros(5,5)  
#We can get the type and also size using following
print(C.dtype)
print(C.size())