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Deep Learning with PyTorch Assignment 1

For this assignment, the following PyTorch functions will be discussed along with examples.

  • is_tensor - checks if the objecy is a tensor. It returns a boolean value of True or False
  • numel - Counts the number of elements in a tensor
  • empty - creates an empty tensor of a specified size
  • full - creates a tensor of a specified size with a value for all the elements
  • chunk - splits the tensor into specified chunks
  • hstack - stacks the tensors horizontally

The libraries to be used are imported

import numpy as np
import torch

Function 1 - is_tensor

This function is used to return a Boolean value of True if the object is a PyTorch tensor. The only input for this function is the object checked.

Example 1 (working):

#Create a Matrix 
mat1 = torch.tensor([[1,2,3.],[4,5.,6],[7,8,9],[10,11,12]])
print(mat1)
#using the function 
torch.is_tensor(mat1)
tensor([[ 1., 2., 3.], [ 4., 5., 6.], [ 7., 8., 9.], [10., 11., 12.]])
True

mat1 is a tensor so the functions return a Boolean value of True

Example 2(working):