Perceptron: Model Setup - ( Perceptron in PyTorch )

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Syntax

class Perceptron(nn.Module):
    def __init__(self, input_dim):
        super(Perceptron, self).__init__()
        self.fc1 = nn.Linear(input_dim, 1)
    
    def forward(self, x_in):
        return torch.sigmoid(self.fc1(x_in))

Example

import torch
import torch.nn as nn

model = Perceptron(input_dim=2)
print(model)

Output

Perceptron(
  (fc1): Linear(in_features=2, out_features=1, bias=True)
)

Explanation

A perceptron is a basic neural network model that can be used for binary classification problems. We start by defining a PyTorch model using the nn.Module class. The Perceptron class requires the input dimension of the data as a parameter in its initialization method. Inside the __init__() method, we create the single linear layer of the perceptron using the nn.Linear class.

The forward() method of the Perceptron class is where the computation occurs. The input tensor is passed through the linear layer, the result of which is then passed through the sigmoid activation function using torch.sigmoid().

Use

The Perceptron model can be used for binary classification problems when the data has a linearly separable boundary. It can be used as a building block for more complex neural networks.

Important Points

• The Perceptron model can be used for binary classification problems with a linearly separable boundary
• PyTorch's nn.Module class can be used to define the Perceptron model
• The Perceptron model consists of a single linear layer and a sigmoid activation function
• The forward() method of the Perceptron class is where the computation occurs

Summary

In conclusion, the Perceptron model is a basic neural network model for binary classification problems. PyTorch's nn.Module class can be used to define the Perceptron model with a single linear layer and a sigmoid activation function in the forward() method. The Perceptron model can be used as a building block for more complex neural networks.