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

Beware: The view() method only returns a tensor with the
desired shape that shares the underlying data with the original
tensor—it DOES NOT create a new, independent, tensor!
The reshape() method may or may not create a copy! The
reasons behind this apparently weird behavior are beyond the
scope of this section, but this behavior is the reason why view()
is preferred.
# We get a tensor with a different shape but it still is
# the SAME tensor
same_matrix = matrix.view(1, 6)
# If we change one of its elements...
same_matrix[0, 1] = 2.
# It changes both variables: matrix and same_matrix
print(matrix)
print(same_matrix)
Output
tensor([[1., 2., 1.],
[1., 1., 1.]])
tensor([[1., 2., 1., 1., 1., 1.]])
If you want to copy all data, that is, duplicate the data in memory, you may use
either its new_tensor() or clone() methods.
# We can use "new_tensor" method to REALLY copy it into a new one
different_matrix = matrix.new_tensor(matrix.view(1, 6))
# Now, if we change one of its elements...
different_matrix[0, 1] = 3.
# The original tensor (matrix) is left untouched!
# But we get a "warning" from PyTorch telling us
# to use "clone()" instead!
print(matrix)
print(different_matrix)
74 | Chapter 1: A Simple Regression Problem