Gradient with PyTorch - ( Tensors in PyTorch )

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Syntax

import torch

x = torch.tensor([2, 4, 6], dtype=torch.float32, requires_grad=True)

y = x.mean()

y.backward()

Example

import torch

x = torch.tensor([2, 4, 6], dtype=torch.float32, requires_grad=True)

y = x.mean()

y.backward()

print(x.grad)

Output

tensor([0.3333, 0.3333, 0.3333])

Explanation

In PyTorch, tensors can be used to represent mathematical entities such as scalars, vectors, matrices, and more. Tensors can also be used to compute gradients.

The requires_grad flag is used to indicate whether a tensor should be tracked for gradient computation or not. Tensors with requires_grad=True are tracked for gradient computation.

The backward() method is used to compute gradients of a tensor. It accumulates the gradients in the grad attribute of the tensor.

Use

Gradients can be used for training models using machine learning algorithms such as deep learning. By computing gradients of the model parameters with respect to the loss function, the model can update the parameters in the direction of decreasing loss.

Important Points

• Tensors in PyTorch represent mathematical entities such as scalars, vectors, matrices, and more
• Tensors can be used to compute gradients using the requires_grad flag and the backward() method
• Gradients can be used to train machine learning models such as deep learning models

Summary

In conclusion, tensors in PyTorch are useful for representing mathematical entities and computing gradients. By using the requires_grad flag and the backward() method, gradients can be computed for tensors and used to train machine learning models.