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When I print a torch tensor, I get the below output. How could I get that tensor without [] for the inner elements? I printed the type of the first element and it returns <class 'torch.Tensor'> So this tensor seems to be a tensor of tensors...How could I convert it to a tensor of numbers?

tensor([[-5.6117e-01], [ 3.5726e-01], [-2.5853e-01], [-4.8641e-01], [-1.0581e-01], [-1.8322e-01], [-1.2732e+00], [-5.9760e-02], [ 1.2819e-01], [ 6.3894e-02], [-9.1817e-01], [-1.6539e-01], [-1.1471e+00], [ 1.9666e-01], [-6.3297e-01], [-4.0876e-01], [-2.4590e-02], [ 2.7065e-01], [ 3.5308e-01], [-4.6348e-01], [-4.1755e-01], [-1.1554e-01], [-4.2062e-01], [ 1.4067e-01], [-2.9788e-01], [-7.4582e-02], [-5.3751e-01], [ 1.1344e-01], [-2.6100e-01], [ 2.6951e-02], [-5.0437e-02], [-1.9163e-01], [-3.3893e-02], [-5.9640e-01], [-1.1574e-01], [ 1.4613e-01], [ 1.2263e-01], [-1.5566e-01], [ 1.4740e-01], [-9.9924e-01], [ 2.0878e-01], [-2.0074e-01], [ 7.8383e-02], [ 7.4679e-02], [-5.8065e-01], [ 6.7777e-01], [ 5.9879e-01], [ 6.6301e-01], [-4.7051e-01], [-2.5468e-01], [-2.7382e-01], [ 1.7585e-01], [ 3.6151e-01], [-9.2532e-01], [-1.6999e-01], [ 8.4971e-02], [-6.6083e-01], [-3.1204e-02], [ 6.3712e-01], [-5.8580e-02], [-7.7901e-04], [-4.6792e-01], [ 1.0796e-01], [ 7.8766e-01], [ 1.6809e-01], [-7.0058e-01], [-2.9299e-01], [-8.2735e-02], [ 2.0875e-01], [-2.9426e-01], [-7.6748e-02], [-1.5762e-01], [-5.7432e-01], [-5.2042e-01], [-1.5152e-01], [ 1.4119e+00], [-1.5752e-01], [-3.0565e-01], [-5.1378e-01], [-5.8924e-01], [-1.0163e+00], [-2.2021e-01], [ 2.9112e-02], [ 1.8521e-01], [ 6.2814e-01], [-6.8793e-01], [ 2.1395e-02], [ 5.7168e-01], [ 9.0977e-01], [ 3.8899e-01], [ 3.0209e-01], [ 2.4655e-01], [-1.1688e-01], [-5.9835e-02], [ 3.6426e-02], [-5.2782e-01], [ 1.4604e+00], [ 2.9685e-01], [-2.4077e-01], [ 1.0163e+00], [ 6.9770e-01], [-2.6183e-01], [ 3.6770e-01], [ 3.6535e-03], [ 4.2364e-01], [-5.4703e-01], [ 8.9173e-02], [-3.9032e-01], [-5.9740e-01], [ 3.7479e-02], [ 3.0257e-01], [ 8.2539e-02], [-6.0559e-01], [-4.3660e-01], [-7.0624e-01], [-5.0503e-01], [-4.0929e-01], [-2.3300e-01], [ 2.0298e-01], [-6.3697e-01], [-1.2584e-01], [ 5.6092e-02], [ 5.0150e-02], [-1.5358e-01], [ 2.9248e-02], [ 1.1180e-01], [-1.5535e-01], [ 1.1964e-01], [-6.5698e-01], [ 4.1923e-01], [ 7.4044e-02], [ 2.4536e-02], [ 3.2647e-01], [-7.7464e-01], [ 3.9898e-01], [-2.5777e-01], [ 8.5569e-02], [-4.0305e-01], [ 5.4463e-01], [-3.4124e-01], [-4.0789e-01], [ 4.2093e-01], [-3.8487e-01], [-4.0491e-01], [-2.1539e-01], [-1.7979e-02], [ 3.2492e-01], [-2.0894e-01], [ 2.5629e-01], [ 9.6046e-01]], device='cuda:0', grad_fn=<AddmmBackward0>)
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  • What did you use to print the type of the first element of a tensor? Commented Mar 7, 2022 at 11:48

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That tensor has a singleton dimension (ie. it's of shape [Nx1]). Just squeeze that dimension or pick the 0th element:

In [1]: import torch In [2]: a = torch.zeros([10,1]) In [3]: a Out[3]: tensor([[0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.], [0.]]) In [4]: a[:,0] Out[4]: tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) In [5]: a.squeeze(1) Out[5]: tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
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if i understand your question right you can use the flatten method

input:

 tvalue=torch.tensor([[-5.6117e-01], [ 3.5726e-01], [-2.5853e-01], [-4.8641e-01], [-1.0581e-01], [-1.8322e-01], [-1.2732e+00], [-5.9760e-02], [ 1.2819e-01], [ 6.3894e-02], [-9.1817e-01], [-1.6539e-01], [-1.1471e+00], [ 1.9666e-01], [-6.3297e-01], [-4.0876e-01], [-2.4590e-02], [ 2.7065e-01], [ 3.5308e-01], [-4.6348e-01], [-4.1755e-01], [-1.1554e-01], [-4.2062e-01], [ 1.4067e-01], [-2.9788e-01], [-7.4582e-02], [-5.3751e-01], [ 1.1344e-01], [-2.6100e-01], [ 2.6951e-02], [-5.0437e-02], [-1.9163e-01], [-3.3893e-02], [-5.9640e-01], [-1.1574e-01], [ 1.4613e-01], [ 1.2263e-01], [-1.5566e-01], [ 1.4740e-01], [-9.9924e-01], [ 2.0878e-01], [-2.0074e-01], [ 7.8383e-02], [ 7.4679e-02], [-5.8065e-01], [ 6.7777e-01], [ 5.9879e-01], [ 6.6301e-01], [-4.7051e-01], [-2.5468e-01], [-2.7382e-01], [ 1.7585e-01], [ 3.6151e-01], [-9.2532e-01], [-1.6999e-01], [ 8.4971e-02], [-6.6083e-01], [-3.1204e-02], [ 6.3712e-01], [-5.8580e-02], [-7.7901e-04], [-4.6792e-01], [ 1.0796e-01], [ 7.8766e-01], [ 1.6809e-01], [-7.0058e-01], [-2.9299e-01], [-8.2735e-02], [ 2.0875e-01], [-2.9426e-01], [-7.6748e-02], [-1.5762e-01], [-5.7432e-01], [-5.2042e-01], [-1.5152e-01], [ 1.4119e+00], [-1.5752e-01], [-3.0565e-01], [-5.1378e-01], [-5.8924e-01], [-1.0163e+00], [-2.2021e-01], [ 2.9112e-02], [ 1.8521e-01], [ 6.2814e-01], [-6.8793e-01], [ 2.1395e-02], [ 5.7168e-01], [ 9.0977e-01], [ 3.8899e-01], [ 3.0209e-01], [ 2.4655e-01], [-1.1688e-01], [-5.9835e-02], [ 3.6426e-02], [-5.2782e-01], [ 1.4604e+00], [ 2.9685e-01], [-2.4077e-01], [ 1.0163e+00], [ 6.9770e-01], [-2.6183e-01], [ 3.6770e-01], [ 3.6535e-03], [ 4.2364e-01], [-5.4703e-01], [ 8.9173e-02], [-3.9032e-01], [-5.9740e-01], [ 3.7479e-02], [ 3.0257e-01], [ 8.2539e-02], [-6.0559e-01], [-4.3660e-01], [-7.0624e-01], [-5.0503e-01], [-4.0929e-01], [-2.3300e-01], [ 2.0298e-01], [-6.3697e-01], [-1.2584e-01], [ 5.6092e-02], [ 5.0150e-02], [-1.5358e-01], [ 2.9248e-02], [ 1.1180e-01], [-1.5535e-01], [ 1.1964e-01], [-6.5698e-01], [ 4.1923e-01], [ 7.4044e-02], [ 2.4536e-02], [ 3.2647e-01], [-7.7464e-01], [ 3.9898e-01], [-2.5777e-01], [ 8.5569e-02], [-4.0305e-01], [ 5.4463e-01], [-3.4124e-01], [-4.0789e-01], [ 4.2093e-01], [-3.8487e-01], [-4.0491e-01], [-2.1539e-01], [-1.7979e-02], [ 3.2492e-01], [-2.0894e-01], [ 2.5629e-01], [ 9.6046e-01]])

output

tvalue.flatten() tensor([-5.6117e-01, 3.5726e-01, -2.5853e-01, -4.8641e-01, -1.0581e-01, -1.8322e-01, -1.2732e+00, -5.9760e-02, 1.2819e-01, 6.3894e-02, -9.1817e-01, -1.6539e-01, -1.1471e+00, 1.9666e-01, -6.3297e-01, -4.0876e-01, -2.4590e-02, 2.7065e-01, 3.5308e-01, -4.6348e-01, -4.1755e-01, -1.1554e-01, -4.2062e-01, 1.4067e-01, -2.9788e-01, -7.4582e-02, -5.3751e-01, 1.1344e-01, -2.6100e-01, 2.6951e-02, -5.0437e-02, -1.9163e-01, -3.3893e-02, -5.9640e-01, -1.1574e-01, 1.4613e-01, 1.2263e-01, -1.5566e-01, 1.4740e-01, -9.9924e-01, 2.0878e-01, -2.0074e-01, 7.8383e-02, 7.4679e-02, -5.8065e-01, 6.7777e-01, 5.9879e-01, 6.6301e-01, -4.7051e-01, -2.5468e-01, -2.7382e-01, 1.7585e-01, 3.6151e-01, -9.2532e-01, -1.6999e-01, 8.4971e-02, -6.6083e-01, -3.1204e-02, 6.3712e-01, -5.8580e-02, -7.7901e-04, -4.6792e-01, 1.0796e-01, 7.8766e-01, 1.6809e-01, -7.0058e-01, -2.9299e-01, -8.2735e-02, 2.0875e-01, -2.9426e-01, -7.6748e-02, -1.5762e-01, -5.7432e-01, -5.2042e-01, -1.5152e-01, 1.4119e+00, -1.5752e-01, -3.0565e-01, -5.1378e-01, -5.8924e-01, -1.0163e+00, -2.2021e-01, 2.9112e-02, 1.8521e-01, 6.2814e-01, -6.8793e-01, 2.1395e-02, 5.7168e-01, 9.0977e-01, 3.8899e-01, 3.0209e-01, 2.4655e-01, -1.1688e-01, -5.9835e-02, 3.6426e-02, -5.2782e-01, 1.4604e+00, 2.9685e-01, -2.4077e-01, 1.0163e+00, 6.9770e-01, -2.6183e-01, 3.6770e-01, 3.6535e-03, 4.2364e-01, -5.4703e-01, 8.9173e-02, -3.9032e-01, -5.9740e-01, 3.7479e-02, 3.0257e-01, 8.2539e-02, -6.0559e-01, -4.3660e-01, -7.0624e-01, -5.0503e-01, -4.0929e-01, -2.3300e-01, 2.0298e-01, -6.3697e-01, -1.2584e-01, 5.6092e-02, 5.0150e-02, -1.5358e-01, 2.9248e-02, 1.1180e-01, -1.5535e-01, 1.1964e-01, -6.5698e-01, 4.1923e-01, 7.4044e-02, 2.4536e-02, 3.2647e-01, -7.7464e-01, 3.9898e-01, -2.5777e-01, 8.5569e-02, -4.0305e-01, 5.4463e-01, -3.4124e-01, -4.0789e-01, 4.2093e-01, -3.8487e-01, -4.0491e-01, -2.1539e-01, -1.7979e-02, 3.2492e-01, -2.0894e-01, 2.5629e-01, 9.6046e-01])
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