Backpropagation Process - ( Deep Neural Network with PyTorch )

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Syntax

PyTorch

import torch
from torch.autograd import Variable

# Create input tensor
x = Variable(torch.Tensor([1.0, 2.0, 3.0]), requires_grad=True)

# Create weight tensor
w = Variable(torch.Tensor([4.0, 5.0, 6.0]), requires_grad=True)

# Create bias tensor
b = Variable(torch.Tensor([7.0]), requires_grad=True)

# Create the model
y = torch.dot(w, x) + b

# Compute the gradients
y.backward()

# Get the gradients
x_grad = x.grad
w_grad = w.grad
b_grad = b.grad

Example

PyTorch

import torch
from torch.autograd import Variable

# Create input tensor
x = Variable(torch.Tensor([2.0, 3.0]), requires_grad=True)

# Create weight tensor
w = Variable(torch.Tensor([4.0, 5.0]), requires_grad=True)

# Create bias tensor
b = Variable(torch.Tensor([7.0]), requires_grad=True)

# Create the model
y = torch.dot(w, x) + b

# Compute the gradients
y.backward()

# Get the gradients
x_grad = x.grad
w_grad = w.grad
b_grad = b.grad

# Print the gradients
print("x_grad: ", x_grad)
print("w_grad: ", w_grad)
print("b_grad: ", b_grad)

Output

PyTorch

x_grad: tensor([4., 5.])
w_grad: tensor([2., 3.])
b_grad: tensor([1.])

Explanation

Backpropagation is an algorithm used to train neural networks. It is based on the concept of finding the gradient of the loss function with respect to the trainable parameters of the model. It is possible to calculate the gradient of the loss function with respect to the trainable parameters using the chain rule.

In PyTorch, the backward() function is used to compute gradients. It calculates the gradient of the output with respect to all the tensors which have requires_grad set to True.

Use

Backpropagation is used to train neural networks. It is used to adjust the weights and biases of the network such that the output is as close to the desired output as possible. This process involves iteratively adjusting the weights and biases using the gradients computed in the backward pass.

Important Points

• Backpropagation is an algorithm used to train neural networks
• It is based on the concept of finding the gradient of the loss function with respect to the trainable parameters of the model
• The backward() function in PyTorch is used to compute gradients
• Backpropagation is used to adjust the weights and biases of the network to get the desired output

Summary

In conclusion, backpropagation is an important algorithm used to train neural networks. It involves calculating the gradient of the loss function with respect to the trainable parameters of the model and adjusting the weights and biases of the network iteratively using the gradients computed in the backward pass. In PyTorch, the backward() function is used to compute gradients.