I'm using PyTorch to learn in-depth learning.
I use numpy's np.tile and np.reshape in the forward calculation, but if I convert the Tensor type to numpy's ndarray type, the requirements_grad information will be lost and I cannot learn.
Is there a way to do things like tile while still in the Tensor type?
Or is it not possible to calculate with numpy while keeping the requirements_grad information?
python numpy deep-learning pytorch
How do you like this?
#!/usr/bin/env python
import torch
# tile
x=torch.tensor([[1,2], [3,4]], dtype=torch.float32)
x.requires_grad_()
print(x)
x = x.repeat(2,3)
print(x)
# reshape
x=torch.tensor([1,2], [3,4], [5,6]], dtype=torch.float32)
x.requires_grad_()
print(x)
x = x.view(2, -1)
print(x)
The output is as follows:
luna:~%./testu1.py
US>tensor([1., 2.],
[3.,4.]], requires_grad=True)
US>tensor([[1., 2., 1., 2., 1., 2.],
[3., 4., 3., 4., 3., 4.],
[1., 2., 1., 2., 1., 2.],
[3. , 4. , 3. , 4. , 3. , 4. ] , grad_fn = <RepeatBackward > )
US>tensor([1., 2.],
[3., 4.],
[5, 6.] , requires_grad = True )
US>tensor([1., 2., 3.],
[4. , 5. , 6. ] , grad_fn = <ViewBackward >)
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