What is it?
A perceptron is the simplest neural network unit, being historically modeled after a biological neuron. Similar to the neuron, there are signals flowing from the input to the output. These signals interact with three properties: the bias, the weight and the activation function.
Given, as the activation function, as the weight as the input and output, and as the bias, the perceptron can be mathematically represented as:
Normally, we can represent the set of inputs as Vectors, and the product of becomes a dot product:
Implementing with PyTorch
One can easily implement a perceptron algorithm using PyTorch:
import torch
from torch import nn
class Perceptron(nn.Module):
def __init__(self, input_dimension):
super(Perceptron, self).__init__()
self.fcl = nn.Linear(input_dimension, 1)
def forward(self, x):
# Using a sigmoid activation function
return torch.sigmoid(self.fcl(x)).squeeze()