How Do I Know My Torch Data Type?


Scott Campbell

In Python, determining the data type of a variable is essential for understanding its characteristics and how it can be used. Torch, a popular machine learning library, offers several methods to help you identify the data type of your Torch tensors. Let’s explore some of these methods in this tutorial.

Using the type() function

The simplest way to check the data type of a Torch tensor is by using the type() function. This function returns the class type of an object, allowing you to determine if it’s a Torch tensor or not. Here’s an example:

import torch

x = torch.tensor([1, 2, 3])

The output will be:

<class 'torch.Tensor'>

This confirms that the variable x is indeed a Torch tensor.

Using the dtype attribute

Torch tensors have a dtype attribute that provides information about their underlying data type. This can be helpful when you need to know if your tensor contains integers, floats, or another specific data type. Here’s an example:

x = torch.tensor([1., 2., 3.])


This tells us that the elements in tensor x are stored as 32-bit floating-point numbers.

Using isinstance() function

If you want to check whether your variable is an instance of a specific Torch tensor class, you can use the isinstance() function. This function takes two arguments: the variable you want to check and the Torch tensor class you’re interested in.tensor([1, 2, 3])
print(isinstance(x, torch.Tensor))


This confirms that x is an instance of the Torch tensor class.

Using torch.is_tensor() function

To check if a variable is a Torch tensor regardless of its specific class, you can use the torch.is_tensor() function. This function returns True if the input variable is a tensor and False otherwise.tensor([1, 2, 3])

This indicates that x is indeed a Torch tensor.

In conclusion

Determining the data type of your Torch tensors is crucial for understanding how to manipulate and process them effectively. In this tutorial, we explored several methods to help you identify the data type of your tensors using functions like type(), x.dtype, isinstance(), and torch.is_tensor(). By utilizing these methods, you’ll be able to work with your Torch tensors more confidently and efficiently.

Now that you know how to determine the data type of a Torch tensor, you can move on to more advanced tasks and explore the vast possibilities of machine learning with Torch!

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