TORCH.TENSOR

A torch.Tensor is a multi-dimensional matrix containing elements of a single data type. Tensors are similar to NumPy’s ndarrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.

Torch defines nine CPU tensor types and nine GPU tensor types:

A tensor can be constructed from a Python list or sequence using the torch.tensor() constructor

WHAT IS PYTORCH?

It’s a Python-based scientific computing package targeted at two sets of audiences:

A replacement for NumPy to use the power of GPUs
a deep learning research platform that provides maximum flexibility and speed

Chosen functions.

torch.diagonal()
torch.matmul()
torch.inverse()
torch.squeeze()
torch.unique()

# Import torch and other required modules
import torch
import numpy as np

Function 1

`torch.diagonal(input, offset=0, dim1=0, dim2=1)`

Returns a partial view of input with the its diagonal elements with respect to dim1 and dim2 appended as a dimension at the end of the shape.

The argument offset controls which diagonal to consider:

If offset = 0, it is the main diagonal.
If offset > 0, it is above the main diagonal.
If offset < 0, it is below the main diagonal.

Applying torch.diag_embed() to the output of this function with the same arguments yields a diagonal matrix with the diagonal entries of the input. However, torch.diag_embed() has different default dimensions, so those need to be explicitly specified.

Parameters

input (Tensor) – the input tensor. Must be at least 2-dimensional.
offset (int, optional) – which diagonal to consider. Default: 0 (main diagonal).
dim1 (int, optional) – first dimension with respect to which to take diagonal. Default: 0.
dim2 (int, optional) – second dimension with respect to which to take diagonal. Default: 1.

# Example 1 - working
a = torch.randn(3, 3)
a

tensor([[ 1.3418,  1.3554,  0.8112],
        [-1.1586,  0.2967,  3.0477],
        [-1.2395,  0.2124,  0.7688]])

torch.diagonal(a, 0)

tensor([1.3418, 0.2967, 0.7688])