Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Output
tensor([[1., 2., 1.],
[1., 1., 1.]])
tensor([[1., 3., 1., 1., 1., 1.]])
Output
UserWarning: To copy construct from a tensor, it is
recommended to use sourceTensor.clone().detach() or
sourceTensor.clone().detach().requires_grad_(True),
rather than tensor.new_tensor(sourceTensor).
"""Entry point for launching an IPython kernel.
It seems that PyTorch prefers that we use clone()—together with
detach()—instead of new_tensor(). Both ways accomplish exactly the same
result, but the code below is deemed cleaner and more readable.
# Let's follow PyTorch's suggestion and use "clone" method
another_matrix = matrix.view(1, 6).clone().detach()
# Again, if we change one of its elements...
another_matrix[0, 1] = 4.
# The original tensor (matrix) is left untouched!
print(matrix)
print(another_matrix)
Output
tensor([[1., 2., 1.],
[1., 1., 1.]])
tensor([[1., 4., 1., 1., 1., 1.]])
You’re probably asking yourself: "But, what about the detach()
method—what does it do?"
It removes the tensor from the computation graph, which probably raises more
questions than it answers, right? Don’t worry, we’ll get back to it later in this
chapter.
PyTorch | 75