How Do I Check My PyTorch Tensor Data Type?


Larry Thompson

Have you ever wondered how to check the data type of a PyTorch tensor? Well, you’re in luck!

In this tutorial, we’ll explore different ways to determine the data type of your tensors. Let’s dive in!

Using the .dtype Attribute

PyTorch provides a handy attribute called .dtype that allows you to easily check the data type of a tensor. The .dtype attribute returns a string representing the data type.

To use this attribute, simply call it on your tensor object. For example:

import torch

# Create a tensor
tensor = torch.tensor([1, 2, 3])

# Check the data type
data_type = tensor.dtype


The output will be:


In this example, we created a tensor with values [1, 2, 3] and assigned it to the variable tensor. We then checked its data type using .dtype and printed the result, which is torch.int64.

Using isinstance()

If you prefer to work with conditionals or want more flexibility in handling different data types, you can use the isinstance() function. This function allows you to check if an object belongs to a specific class or its subclasses.

To check if a PyTorch tensor is of a certain data type using isinstance(), follow these steps:

  1. Create your tensor.
  2. Instantiate variables for each desired data type.
  3. Use isinstance() to check if the tensor matches any of the desired data types.
  4. Perform actions based on the results.

Let’s see an example:

# Check if it’s an integer tensor
if isinstance(tensor, torch.IntTensor):
print(“Integer tensor”)
elif isinstance(tensor, torch.FloatTensor):
print(“Float tensor”)
print(“Other data type”)

Integer tensor

In this example, we created a tensor with values [1, 2, 3]. We checked if it is an integer tensor using isinstance() and printed “Integer tensor” as the result.


In this tutorial, we explored two different methods to check the data type of PyTorch tensors. Using the .dtype attribute provides a straightforward way to retrieve the data type as a string.

On the other hand, using isinstance() allows for more control and flexibility when dealing with different data types. Choose the method that suits your needs best!

I hope you found this tutorial helpful! Happy coding with PyTorch!

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