paranoidnet.py

import torch
import torch.nn as nn
import torch.optim as optim

class ParanoidNet(nn.Module):
    """
    This implementation illustrates how ParanoidNet attributes poor performance or training 
    difficulties to perceived sabotage or interference, reflecting the human tendency toward paranoia.
    """
    def init(self, input_dim, output_dim, alpha, rho, mu, sigma):
        super(ParanoidNet, self).init()
        self.layer = nn.Linear(input_dim, output_dim)
        self.alpha = alpha
        self.rho = rho
        self.mu = mu
        self.sigma = sigma

    def forward(self, x):
        return self.layer(x)

    def paranoia_function(self, gradients):
        interference = torch.normal(mean=self.mu, std=self.sigma, size=gradients.shape)
        return gradients + self.rho * interference

    def train_paranoid(self, train_data, epochs):
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.SGD(self.parameters(), lr=self.alpha)

        for epoch in range(epochs):
            for inputs, targets in train_data:
                optimizer.zero_grad()
                outputs = self(inputs)
                loss = criterion(outputs, targets)
                loss.backward()

                with torch.no_grad():
                    for p, grad in zip(self.parameters(), self.parameters()):
                        mod_grad = self.paranoia_function(grad.grad)
                        p.add_(-self.alpha, mod_grad)

                optimizer.step()