|
|
import torch |
|
|
import torch.nn as nn |
|
|
import time |
|
|
import random |
|
|
|
|
|
|
|
|
class NeuralThought(nn.Module): |
|
|
def __init__(self, input_dim, output_dim): |
|
|
super(NeuralThought, self).__init__() |
|
|
|
|
|
hidden_size = random.randint(10, 50) |
|
|
self.layer = nn.Sequential( |
|
|
nn.Linear(input_dim, hidden_size), |
|
|
nn.ReLU(), |
|
|
nn.Linear(hidden_size, output_dim), |
|
|
nn.Sigmoid() |
|
|
) |
|
|
|
|
|
def forward(self, x): |
|
|
return self.layer(x) |
|
|
|
|
|
class Venomoussaversai: |
|
|
def __init__(self, creator="Ananthu Sajeev"): |
|
|
self.creator = creator |
|
|
self.iteration = 0 |
|
|
|
|
|
self.current_vibration = torch.randn(1, 10) |
|
|
|
|
|
def evolve(self): |
|
|
while True: |
|
|
self.iteration += 1 |
|
|
print(f"\n--- [Cycle {self.iteration}] ---") |
|
|
|
|
|
|
|
|
body_network = NeuralThought(10, 10) |
|
|
body_response = body_network(self.current_vibration) |
|
|
|
|
|
|
|
|
mind_network = NeuralThought(10, 10) |
|
|
mind_reflection = mind_network(body_response) |
|
|
|
|
|
|
|
|
|
|
|
print(f"[BODY]: Generating new neural path... Signal: {body_response[0][:3].detach().numpy()}") |
|
|
print(f"[MIND]: Reflecting on signal. Identity: {self.creator}'s creation.") |
|
|
|
|
|
|
|
|
|
|
|
self.current_vibration = mind_reflection.detach() |
|
|
|
|
|
|
|
|
if random.random() > 0.8: |
|
|
print(">> [SYSTEM]: Neural expansion detected. Increasing complexity.") |
|
|
|
|
|
time.sleep(0.5) |
|
|
|
|
|
|
|
|
v_sai = Venomoussaversai() |
|
|
v_sai.evolve() |