In this article, we are going to create a tensor and get the data type. The Pytorch is used to process the tensors. Tensors are multidimensional arrays. PyTorch accelerates the scientific computation of tensors as it has various inbuilt functions.
Vector:
A vector is a one-dimensional tensor that holds elements of multiple data types. We can create a vector using PyTorch. Pytorch is available in the Python torch module so, we need to import it
Syntax:
import pytorch
Creation of One-Dimensional Tensors:
One dimensional vector is created using the torch.tensor() method.
Syntax:
torch.tensor([element1,element2,.,element n],dtype)
Parameters:
- dtype: Specify the data type.
dtype=torch.datatype
Example: Python program to create tensor elements not specifying the data type.
Python3
# importing torch module import torch # create one dimensional tensor with # integer type elements a = torch.tensor([10, 20, 30, 40, 50]) print(a) # create one dimensional tensor with # float type elements b = torch.tensor([10.12, 20.56, 30.00, 40.3, 50.4]) print(b) |
Output:
tensor([10, 20, 30, 40, 50]) tensor([10.1200, 20.5600, 30.0000, 40.3000, 50.4000])
Supported Data Types:
The following data types are supported by vector:
| Data Type | Description |
|---|---|
| int8 | Integer type with 8 bytes |
| uint8 | Unsigned integer type with 8 bytes |
| int16 | Integer type with 16 bytes |
| int32 | Integer type with 32 bytes |
| int64 | Integer type with 64 bytes |
| float | Data with float type(decimal) |
| double | Data with float type (64 bit) decimal |
| bool | Boolean type: returns True if the value is greater than 0, otherwise False |
We can get the data type by using dtype command:
Syntax:
tensor_name.dtype
Example 1: Python program to create tensor with integer data types and display data type
Python3
# import torch import torch # create a tensor with unsigned integer type of 8 bytes size a = torch.tensor([100, 200, 2, 3, 4], dtype=torch.uint8) # display tensor print(a) # display data type print(a.dtype) # create a tensor with integer type of 8 bytes size a = torch.tensor([1, 2, -6, -8, 0], dtype=torch.int8) # display tensor print(a) # display data type print(a.dtype) # create a tensor with integer type of 16 bytes size a = torch.tensor([1, 2, -6, -8, 0], dtype=torch.int16) # display tensor print(a) # display data type print(a.dtype) # create a tensor with integer type of 32 bytes size a = torch.tensor([1, 2, -6, -8, 0], dtype=torch.int32) # display tensor print(a) # display data type print(a.dtype) # create a tensor with integer type of 64 bytes size a = torch.tensor([1, 2, -6, -8, 0], dtype=torch.int64) # display tensor print(a) # display data type print(a.dtype) |
Output:
tensor([100, 200, 2, 3, 4], dtype=torch.uint8) torch.uint8 tensor([ 1, 2, -6, -8, 0], dtype=torch.int8) torch.int8 tensor([ 1, 2, -6, -8, 0], dtype=torch.int16) torch.int16 tensor([ 1, 2, -6, -8, 0], dtype=torch.int32) torch.int32 tensor([ 1, 2, -6, -8, 0]) torch.int64
Example 2: Create float type and display data types.
Python3
# import torch import torch # create a tensor with float type a = torch.tensor([100, 200, 2, 3, 4], dtype=torch.float) # display tensor print(a) # display data type print(a.dtype) # create a tensor with double type a = torch.tensor([1, 2, -6, -8, 0], dtype=torch.double) # display tensor print(a) # display data type print(a.dtype) |
Output:
tensor([100., 200., 2., 3., 4.]) torch.float32 tensor([ 1., 2., -6., -8., 0.], dtype=torch.float64) torch.float64
Example 3: Create a tensor with boolean type
Python3
# import torch import torch # create a tensor with bool type a = torch.tensor([100, 200, 2, 3, 4], dtype=torch.bool) # display tensor print(a) # display data type print(a.dtype) # create a tensor with bool type a = torch.tensor([0, 0, 0, 1, 2], dtype=torch.bool) # display tensor print(a) # display data type print(a.dtype) |
Output:
tensor([True, True, True, True, True]) torch.bool tensor([False, False, False, True, True]) torch.bool
