Create a Numpy array from a torch.tensor
A Pytorch Tensor is basically the same as a NumPy array. This means it does not know anything about deep learning or computational graphs or gradients and is just a generic n-dimensional array to be used for arbitrary numeric computation.
Example 1:
To create a Numpy array from Tensor, Tensor is converted to a tensor.numpy() first.
Python3
# pip install torchimport torchtensor = torch.tensor([1, 2, 3, 4, 5])np_a = tensor.numpy() |
Output:
array([1, 2, 3, 4, 5])
Example 2:
To create a Numpy array from Tensor, Tensor is converted to a tensor.detach.numpy() first.
Python3
# pip install torchimport torchtensor = torch.tensor([1, 2, 3, 4, 5])np_b = tensor.detach().numpy() |
Output:
array([1, 2, 3, 4, 5])
Example 3:
To create a Numpy array from Tensor, Tensor is converted to a tensor.detach().cpu().numpy() first.
Python3
# pip install torchimport torchtensor = torch.tensor([1, 2, 3, 4, 5])np_c = tensor.detach().cpu().numpy() |
Output:
array([1, 2, 3, 4, 5])
TensorFlow – How to create a numpy ndarray from a tensor
TensorFlow is an open-source Python library designed by Google to develop Machine Learning models and deep-learning, neural networks.