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Pytorch toturial

PyTorch is a python Deep Learning open library developped and maintained by Facebook. In this notebook, we will explore 5 functions and show how they work and break. The chosen functions as listed below:

  • function 1: torch.randn
  • function 2: torch.numel
  • function 3: torch.cat
  • function 4: torch.reshape
  • function 5: torch.where

They correspond respectively for generating normal random samples, computing the number of elements of a tensor, concatenating and reshaping tensors, and filtering and replacing tensors values.

# Import torch and other required modules
import torch

Function 1 - torch.randn

The function torch.randn can be used to generate random samples from a normal distribution given a size and data type. Let's illustrate how it works and brake.

# Example 1 - working
rand_samples_1 = torch.randn((2, 3, 4), dtype=torch.float64)
rand_samples_1
tensor([[[-0.3894, -1.0230,  0.3322,  0.1062],
         [-0.3839, -0.1314,  1.1745, -0.7863],
         [ 0.4125,  1.9637, -0.1670, -2.3159]],

        [[-0.8091, -0.6501, -0.6064,  0.6309],
         [-0.5878, -1.6139,  0.4727,  0.6539],
         [-0.5988,  0.3458, -1.9131, -0.1992]]], dtype=torch.float64)

In the example above, we generate a random normal distribution tensor with size (2, 3, 4) and assign it to the variable rand_samples. The size (2, 3, 4) means that we will the rand_samples will contain 2 matrix of size (3, 4) each. We've all specify the type of data to torch.float64.