Learn practical skills, build real-world projects, and advance your career
Updated 4 years ago
PyTorch Introductory - Assignment 1
A brief walkthrough with Shripal ...
... to understand 5 of the functions that can be applied to tensors - torch.Tensor objects.
A short introduction about PyTorch and about the chosen functions.
- function 1 - new_empty()
- function 2 - add()
- function 3 - sqrt()
- function 4 - bernoulli()
- function 5 - sin()
# Import torch and other required modules
import torch
Function 1 - torch.Tensor.new_empty()
- returns a new empty Tensor, with specified size (from arguments) with uninitialized elements and attributes (torch.dtype and torch.device) same as that of the mentioned tensor.
# Example 1 - working
t1 = torch.zeros(())
print("t1 =", t1, "\nt1.dtype = ", t1.dtype, "; t1.device = ", t1.device)
print()
t2 = t1.new_empty((2,4))
print("t1 =", t1)
print("t2 =", t2, "\nt2.dtype = ", t2.dtype, "; t2.device = ", t2.device)
t1 = tensor(0.)
t1.dtype = torch.float32 ; t1.device = cpu
t1 = tensor(0.)
t2 = tensor([[7.5510e-34, 4.5623e-41, 7.5510e-34, 4.5623e-41],
[4.4842e-44, 0.0000e+00, 8.9683e-44, 0.0000e+00]])
t2.dtype = torch.float32 ; t2.device = cpu
Explanation about example:
By default, the dtype of t1
is torch.float32
so, dtype of t2
is also torch.float32
since it is not explicitly mentioned to something else.
Same is for device = cpu