PyTorch Tensor vs NumPy Array

How to Convert Pytorch tensor to Numpy array?

Last Updated : 30 Jun, 2021

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 module
import torch

# import numpy module
import numpy

# create one dimensional tensor with
# float type elements
b = torch.tensor([10.12, 20.56, 30.00, 40.3, 50.4])

print(b)

# convert this into numpy array using
# numpy() method
b = b.numpy()

# display
b

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 module
import torch

# import numpy module
import numpy

# create two dimensional tensor with
# integer type elements
b = 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() method
b = b.numpy()

# display
b

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 module
import torch

# import numpy module
import numpy

# create two dimensional tensor with 
# integer type elements
b = 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() method
b = numpy.array(b)

# display
b

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]])

PyTorch Tensor vs NumPy Array

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