Tensor_Operation.py

# pip3 install torch torchvision
import torch

# Most operations we can do with NumPy arrays can also be done with PyTorch tensors
# Element-wise addition for compatile shapes
data_1 = torch.tensor([[1, 5, 6], [2, 6, 9]])
data_2 = torch.tensor([[0, 1, 1], [3, 3, 2]])
data_1 + data_2
# tensor([[ 1,  6,  7],
#         [ 5,  9, 11]])

data_1 = torch.tensor([[1, 5, 6], [2, 6, 9]])
data_2 = torch.tensor([[0, 1], [3, 3]])
data_1 + data_2
# Traceback (most recent call last):
#   File "<stdin>", line 1, in <module>
# RuntimeError: The size of tensor a (3) must match the size of tensor b (2) at non-singleton dimension 1

# Element-wise multiplication
data_1 = torch.tensor([[1, 5, 6], [2, 6, 9]])
data_2 = torch.tensor([[0, 1, 1], [3, 3, 2]])
data_1 * data_2
# tensor([[ 0,  5,  6],
#         [ 6, 18, 18]])

# Transposition
data_1.T

# Matrix multiplication
data_1 = torch.tensor([[1, 5, 6], [2, 6, 9]])
data_2_t = torch.tensor([[0, 1, 1], [3, 3, 2]]).T
data_1 @ data_2_t

# Concatenation
# Concatenate along the rows
torch.cat((data_1, data_2), dim=0)
# tensor([[1, 5, 6],
#         [2, 6, 9],
#         [0, 1, 1],
#         [3, 3, 2]])

# Concatenate along the columns
torch.cat((data_1, data_2), dim=1)
# tensor([[1, 5, 6, 0, 1, 1],
#         [2, 6, 9, 3, 3, 2]])