Laboratory Task 5

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1. Perform Standard Imports

import numpy as np
import torch

2. Create a function called set_seed() that accepts seed: int as a parameter, this function must return nothing but just set the seed to a certain value.

def set_seed(seed: int):
    np.random.seed(seed)

3. Create a NumPy array called “arr” that contains 6 random integers between 0 (inclusive) and 5 (exclusive), call the set_seed() function and use 42 as the seed parameter.

seed = 42
set_seed(seed)
arr = np.random.randint(low=0, high=5, size=6)
arr

array([3, 4, 2, 4, 4, 1], dtype=int32)

4. Create a tensor “x” from the array above.

x = torch.from_numpy(arr)
x

tensor([3, 4, 2, 4, 4, 1], dtype=torch.int32)

5. Change the dtype of x from int32 to int64.

x = x.type(torch.int64)
x

tensor([3, 4, 2, 4, 4, 1])

6. Reshape x into a 3x2 tensor.

x = x.reshape(3,2)
x

tensor([[3, 4],
        [2, 4],
        [4, 1]])

7. Return the right-hand column of tensor x.

x[:,1]

tensor([4, 4, 1])

8. Without changing x, return a tensor of square values of x.

torch.square(x)

tensor([[ 9, 16],
        [ 4, 16],
        [16,  1]])

9. Create a tensor y with the same number of elements as x, that can be matrix-multiplied with x.

torch.manual_seed(seed)
y = torch.randint_like(x.T, 0, 5)
y

tensor([[2, 1, 1],
        [2, 4, 0]])

10. Find the matrix product of x and y.

torch.mm(x,y)

tensor([[14, 19,  3],
        [12, 18,  2],
        [10,  8,  4]])