impatientnet.py

import torch
import torch.nn as nn
import random

class ImpatientNetLayer(nn.Module):
def init(self, input_dim, output_dim, alpha, gamma, p_max):
    """
    This implementation highlights how ImpatientNet adjusts its weight updates 
    based on a step-skipping probability, speeding through training epochs and 
    reflecting the human tendencies of impatience and overconfidence in the 
    learning process.
    """
    super(ImpatientNetLayer, self).init()
    self.weight = nn.Parameter(torch.randn(input_dim, output_dim))
    self.alpha = alpha
    self.gamma = gamma
    self.p_max = p_max

def step_skipping_probability(self):
    return random.uniform(0, self.p_max)

def forward(self, x):
    skip_probability = self.step_skipping_probability()
    alpha_t = self.gamma * self.alpha if skip_probability < self.p_max else 0
    self.weight += alpha_t * x.grad
    return x @ self.weight