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

Output
tensor(3.1416)
tensor([1, 2, 3])
tensor([[1., 1., 1.],
[1., 1., 1.]])
tensor([[[-1.0658, -0.5675, -1.2903, -0.1136],
[ 1.0344, 2.1910, 0.7926, -0.7065],
[ 0.4552, -0.6728, 1.8786, -0.3248]],
[[-0.7738, 1.3831, 1.4861, -0.7254],
[ 0.1989, -1.0139, 1.5881, -1.2295],
[-0.5338, -0.5548, 1.5385, -1.2971]]])
You can get the shape of a tensor using its size() method or its shape attribute.
print(tensor.size(), tensor.shape)
Output
torch.Size([2, 3, 4]) torch.Size([2, 3, 4])
All tensors have shapes, but scalars have "empty" shapes, since they are
dimensionless (or zero dimensions, if you prefer):
print(scalar.size(), scalar.shape)
Output
torch.Size([]) torch.Size([])
You can also reshape a tensor using its view() (preferred) or reshape() methods.
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