PyTorch is an open-source framework available with a Python programming language.
A tensor is a multidimensional array that is used to store the data. So for using a Tensor, we have to import the torch module.
It is possible to check whether the given object is a tensor or not.
torch.is_tensor() is used to check whether the given object is tensor or not.
If the object is a tensor, it will return True otherwise, False.”
Syntax:
Parameter:
object refers to the collection of data.
Example 1
Here, we will create a tensor with 5 elements and check if it is a tensor or not.
import torch
#create a 1D tensor with 5 elements
data1 = torch.tensor([23,45,67,0,0])
#check whether data1 is tensor or not
print(torch.is_tensor(data1))
Output:
We can see that the given object is a tensor. So it returned True.
Example 2
Let’s create a list with 5 elements and check if it is tensor or not.
import torch
#create a list with 5 elements
data1 = [23,45,67,0,0]
#check whether data1 is tensor or not
print(torch.is_tensor(data1))
Output:
It returned False.
Now, we will see how to return the metadata of a tensor.
Metadata explains the tensor structure and elements present in the vector.
torch.size()
torch.size() returns the total number of elements present in a tensor.
Syntax:
Where tensor_object is the tensor.
It takes no parameters.
Example 1
Let’s create a 1D tensor and return size.
import torch
#create a 1D tensor with 5 elements
data1 = torch.tensor([23,45,67,0,0])
#display
print("Tensor: ",data1)
#return tensor size
print("Size: ",data1.size())
Output:
Size: torch.Size([5])
We can see that 5 is returned since there are 5 elements in the above tensor.
Example 2
Let’s create a 2D tensor and return size.
import torch
#create a 2D tensor with 5 elements in each row
data1 = torch.tensor([[23,45,67,0,0],[23,45,67,0,0]])
#display
print("Tensor: ",data1)
#return tensor size
print("Size: ",data1.size())
Output:
[23, 45, 67, 0, 0]])
Size: torch.Size([2, 5])
We can see that 2,5 is returned and represents 2 rows and 5 columns.
torch.shape
torch.shape() returns the shape of a tensor.
Syntax:
Where tensor_object is the tensor.
It takes no parameters.
Example 1
import torch
#create a 1D tensor with 5 elements
data1 = torch.tensor([23,45,67,0,0])
#display
print("Tensor: ",data1)
#return tensor Shape
print("Shape: ",data1.shape)
Output:
Shape: torch.Size([5])
We can see that 5 is returned since there are 5 elements in the above tensor.
Example 2
import torch
#create a 2D tensor with 5 elements in each row
data1 = torch.tensor([[23,45,67,0,0],[23,45,67,0,0]])
#display
print("Tensor: ",data1)
#return tensor Shape
print("Shape: ",data1.shape)
Output:
[23, 45, 67, 0, 0]])
Shape: torch.Size([2, 5])
We can see that 2,5 is returned and represents 2 rows and 5 columns.
torch.numel()
torch.numel() returns the total number of elements present in a tensor.
Syntax:
Where tensor_object is the tensor.
It takes no parameters.
Example 1
import torch
#create a 1D tensor with 5 elements
data1 = torch.tensor([23,45,67,0,0])
#display
print("Tensor: ",data1)
#return total number of elements in a tensor
print("Total elements: ",data1.numel())
Output:
Total elements: 5
We can see that 5 is returned since there are 5 elements in the above tensor.
Example 2
import torch
#create a 2D tensor with 5 elements in each row
data1 = torch.tensor([[23,45,67,0,0],[23,45,67,0,0]])
#display
print("Tensor: ",data1)
#return total number of elements in a tensor
print("Total elements: ",data1.numel())
Output:
[23, 45, 67, 0, 0]])
Total elements: 10
We can see that 10 is returned since there are a total of 10 elements present in the tensor.
Conclusion
In this PyTorch lesson, we saw how to check if the given object is tensor or not using the is_tensor() function. To return the metadata, we used size() and shape methods to return the size and shape of the given tensor.
