One-Dimensional Tensor - ( Tensors in PyTorch )

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Syntax

import torch

# Creating a one-dimensional tensor
x = torch.tensor([1, 2, 3, 4, 5])

Example

import torch

# Creating a one-dimensional tensor
x = torch.tensor([1, 2, 3, 4, 5])

# Printing the tensor
print(x)

Output

tensor([1, 2, 3, 4, 5])

Explanation

In PyTorch, tensors are similar to NumPy arrays but are designed to utilize GPUs for faster computation. A one-dimensional tensor is a tensor with only one dimension, similar to a one-dimensional array in NumPy. They can be created using the torch.tensor() function and passing a list of values.

Use

One-dimensional tensors can be used to represent vectors and are commonly used for tasks such as linear regression and neural networks.

Important Points

• A one-dimensional tensor has only one dimension.
• They can be created using the torch.tensor() function and passing a list of values.
• One-dimensional tensors are commonly used for tasks such as linear regression and neural networks.

Summary

One-dimensional tensors are a fundamental data structure in PyTorch and are used to represent vectors. They can be created using the torch.tensor() function and passed a list of values. One-dimensional tensors are commonly used for tasks such as linear regression and neural networks.