I am trying to create some testing torch
tensors by assembling the dimensions from vectors calculated via basic math functions. As a precursor: assembling Tensors from primitive python arrays
does work:
import torch
import numpy as np
torch.Tensor([[1.0, 0.8, 0.6],[0.0, 0.5, 0.75]])
>> tensor([[1.0000, 0.8000, 0.6000],
[0.0000, 0.5000, 0.7500]])
In addition we can assemble Tensors from numpy
arrays https://pytorch.org/docs/stable/tensors.html :
torch.tensor(np.array([[1, 2, 3], [4, 5, 6]]))
tensor([[ 1, 2, 3],
[ 4, 5, 6]])
However assembling from calculated vectors is eluding me. Here are some of the attempts made:
X = torch.arange(0,6.28)
x = X
torch.Tensor([[torch.cos(X),torch.tan(x)]])
torch.Tensor([torch.cos(X),torch.tan(x)])
torch.Tensor([np.cos(X),np.tan(x)])
torch.Tensor([[np.cos(X),np.tan(x)]])
torch.Tensor(np.array([np.cos(X),np.tan(x)]))
All of the above have the following error:
ValueError: only one element tensors can be converted to Python scalars
What is the correct syntax?
Update A comment requested showing x
/ X
. They're actually set to the same (I changed mind mid-course which to use)
In [56]: x == X
Out[56]: tensor([True, True, True, True, True, True, True])
In [51]: x
Out[51]: tensor([0., 1., 2., 3., 4., 5., 6.])
In [52]: X
Out[52]: tensor([0., 1., 2., 3., 4., 5., 6.])
torch.arange
returns a torch.Tensor as seen below -
X = torch.arange(0,6.28)
x
>> tensor([0., 1., 2., 3., 4., 5., 6.])
Similarly, torch.cos(x)
and torch.tan(x)
returns instances of torch.Tensor
The ideal way to concatenate a sequence of tensors in torch is to use torch.stack
torch.stack([torch.cos(x), torch.tan(x)])
Output
>> tensor([[ 1.0000, 0.5403, -0.4161, -0.9900, -0.6536, 0.2837, 0.9602],
[ 0.0000, 1.5574, -2.1850, -0.1425, 1.1578, -3.3805, -0.2910]])
If you prefer to concatenate along axis=0, use torch.cat([torch.cos(x), torch.tan(x)])
instead.
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