In this article, we are going to convert Pytorch tensor to NumPy array.
Method 1: Using numpy().
Syntax: tensor_name.numpy()
Example 1: Converting one-dimensional a tensor to NumPy array
Python3
# importing torch moduleimport torch # import numpy moduleimport numpy # create one dimensional tensor with# float type elementsb = torch.tensor([10.12, 20.56, 30.00, 40.3, 50.4]) print(b) # convert this into numpy array using# numpy() methodb = b.numpy() # displayb |
Output:
tensor([10.1200, 20.5600, 30.0000, 40.3000, 50.4000]) array([10.12, 20.56, 30. , 40.3 , 50.4 ], dtype=float32)
Example 2: Converting two-dimensional tensors to NumPy array
Python3
# importing torch moduleimport torch # import numpy moduleimport numpy # create two dimensional tensor with# integer type elementsb = torch.tensor([[1, 2, 3, 4, 5], [3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) print(b) # convert this into numpy array using# numpy() methodb = b.numpy() # displayb |
Output:
tensor([[1, 2, 3, 4, 5],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8]])
array([[1, 2, 3, 4, 5],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8]])
Method 2: Using numpy.array() method.
This is also used to convert a tensor into NumPy array.
Syntax: numpy.array(tensor_name)
Example: Converting two-dimensional tensor to NumPy array
Python3
# importing torch moduleimport torch # import numpy moduleimport numpy # create two dimensional tensor with # integer type elementsb = torch.tensor([[1, 2, 3, 4, 5], [3, 4, 5, 6, 7], [4, 5, 6, 7, 8]]) print(b) # convert this into numpy array using # numpy.array() methodb = numpy.array(b) # displayb |
Output:
tensor([[1, 2, 3, 4, 5],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8]])
array([[1, 2, 3, 4, 5],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8]])
