In this article, we will discuss how to access elements in a 3D Tensor in Pytorch. PyTorch is an optimized tensor library majorly used for Deep Learning applications using GPUs and CPUs. It is one of the widely used Machine learning libraries, others being TensorFlow and Keras. The python supports the torch module, so to work with this first we import the module to the workspace.
Syntax:
import torch
We can create a vector by using the torch.tensor() function
Syntax:
torch.tensor([value1,value2,.value n])
Example 1: Python code to create an 3 D Tensor and display
# import torch module import torch
# create an 3 D tensor with 8 elements each a = torch.tensor([[[1, 2, 3, 4, 5, 6, 7, 8],
[10, 11, 12, 13, 14, 15, 16, 17]],
[[71, 72, 73, 74, 75, 76, 77, 78],
[81, 82, 83, 84, 85, 86, 87, 88]]])
# display actual tensor print(a)
|
Output:
tensor([[[ 1, 2, 3, 4, 5, 6, 7, 8],
[10, 11, 12, 13, 14, 15, 16, 17]],
[[71, 72, 73, 74, 75, 76, 77, 78],
[81, 82, 83, 84, 85, 86, 87, 88]]])To access elements from a 3-D tensor Slicing can be used. Slicing means selecting the elements present in the tensor by using “:” slice operator. We can slice the elements by using the index of that particular element.
Note: Indexing starts with 0
Syntax:
tensor[tensor_position_start:tensor_position_end, tensor_dimension_start:tensor_dimension_end , tensor_value_start:tensor_value_end]
where,
- tensor_position_start – Specifies the Tensor to start iterating
- tensor_position_end – Specifies the Tensor to stop iterating
- tensor_dimension_start – Specifies the Tensor to start the iteration of tensor in given positions
- tensor_dimension_stop– Specifies the Tensor to stop the iteration of tensor in given positions
- tensor_value_start – Specifies the start position of the tensor to iterate the elements given in dimensions
- tensor_value_stop – Specifies the end position of the tensor to iterate the elements given in dimensions
Given below are the various examples for the same.
Example 2: Python code to access all the tensors of 1 dimension and get only 7 values in that dimension
# import torch module import torch
# create an 3 D tensor with 8 elements each a = torch.tensor([[[1, 2, 3, 4, 5, 6, 7, 8],
[10, 11, 12, 13, 14, 15, 16, 17]],
[[71, 72, 73, 74, 75, 76, 77, 78],
[81, 82, 83, 84, 85, 86, 87, 88]]])
# display actual tensor print(a)
# access all the tensors of 1 dimension # and get only 7 values in that dimension print(a[0:1, 0:1, :7])
|
Output:
tensor([[[ 1, 2, 3, 4, 5, 6, 7, 8],
[10, 11, 12, 13, 14, 15, 16, 17]],
[[71, 72, 73, 74, 75, 76, 77, 78],
[81, 82, 83, 84, 85, 86, 87, 88]]])
tensor([[[1, 2, 3, 4, 5, 6, 7]]])Example 3: Python code to access all the tensors of all dimensions and get only 3 values in each dimension
# import torch module import torch
# create an 3 D tensor with 8 elements each a = torch.tensor([[[1, 2, 3, 4, 5, 6, 7, 8],
[10, 11, 12, 13, 14, 15, 16, 17]],
[[71, 72, 73, 74, 75, 76, 77, 78],
[81, 82, 83, 84, 85, 86, 87, 88]]])
# display actual tensor print(a)
# access all the tensors of all dimensions # and get only 3 values in each dimension print(a[0:1, 0:2, :3])
|
Output:
tensor([[[ 1, 2, 3, 4, 5, 6, 7, 8],
[10, 11, 12, 13, 14, 15, 16, 17]],
[[71, 72, 73, 74, 75, 76, 77, 78],
[81, 82, 83, 84, 85, 86, 87, 88]]])
tensor([[[ 1, 2, 3],
[10, 11, 12]]])Example 4: access 8 elements in 1 dimension on all tensors
# import torch module import torch
# create an 3 D tensor with 8 elements each a = torch.tensor([[[1, 2, 3, 4, 5, 6, 7, 8],
[10, 11, 12, 13, 14, 15, 16, 17]],
[[71, 72, 73, 74, 75, 76, 77, 78],
[81, 82, 83, 84, 85, 86, 87, 88]]])
# display actual tensor print(a)
# access 8 elements in 1 dimension on all tensors print(a[0:2, 1, 0:8])
|
Output:
tensor([[[ 1, 2, 3, 4, 5, 6, 7, 8],
[10, 11, 12, 13, 14, 15, 16, 17]],
[[71, 72, 73, 74, 75, 76, 77, 78],
[81, 82, 83, 84, 85, 86, 87, 88]]])
tensor([[10, 11, 12, 13, 14, 15, 16, 17],
[81, 82, 83, 84, 85, 86, 87, 88]])