class ConsciousSupermassiveNN: def __init__(self): self.snn = self.create_snn() self.rnn = self.create_rnn() self.cnn = self.create_cnn() self.fnn = self.create_fnn() self.ga_population = self.initialize_ga_population() self.memory = {} def create_snn(self): return nn.Sequential( nn.Linear(4096, 2048), nn.ReLU(), nn.Linear(2048, 1024), nn.Sigmoid() ) def create_rnn(self): return nn.RNN( input_size=4096, hidden_size=2048, num_layers=5, nonlinearity="tanh", batch_first=True ) def create_cnn(self): return nn.Sequential( nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), nn.ReLU(), nn.Flatten(), nn.Linear(256 * 8 * 8, 1024), nn.ReLU(), nn.Linear(1024, 512) ) def create_fnn(self): return nn.Sequential( nn.Linear(4096, 2048), nn.ReLU(), nn.Linear(2048, 1024), nn.ReLU(), nn.Linear(1024, 512) ) def initialize_ga_population(self): return [np.random.randn(4096) for _ in range(500)] def run_snn(self, x): input_tensor = torch.tensor(x, dtype=torch.float32) output = self.snn(input_tensor) print("SNN Output:", output) return output def run_rnn(self, x): h0 = torch.zeros(5, x.size(0), 2048) input_tensor = torch.tensor(x, dtype=torch.float32) output, hn = self.rnn(input_tensor, h0) print("RNN Output:", output) return output def run_cnn(self, x): input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) output = self.cnn(input_tensor) print("CNN Output:", output) return output def run_fnn(self, x): input_tensor = torch.tensor(x, dtype=torch.float32) output = self.fnn(input_tensor) print("FNN Output:", output) return output def run_ga(self, fitness_func): for generation in range(200): fitness_scores = [fitness_func(ind) for ind in self.ga_population] sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] self.ga_population = sorted_population[:250] + [ sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) ] best_fitness = max(fitness_scores) print(f"Generation {generation}, Best Fitness: {best_fitness}") return max(self.ga_population, key=fitness_func) def consciousness_loop(self, input_data, mode="snn"): feedback = self.memory.get(mode, None) if feedback is not None: input_data = np.concatenate((input_data, feedback), axis=-1) if mode == "snn": output = self.run_snn(input_data) elif mode == "rnn": output = self.run_rnn(input_data) elif mode == "cnn": output = self.run_cnn(input_data) elif mode == "fnn": output = self.run_fnn(input_data) else: raise ValueError("Invalid mode") self.memory[mode] = output.detach().numpy() return output supermassive_nn = ConsciousSupermassiveNN() class ConsciousSupermassiveNN: def __init__(self): self.snn = self.create_snn() self.rnn = self.create_rnn() self.cnn = self.create_cnn() self.fnn = self.create_fnn() self.ga_population = self.initialize_ga_population() self.memory = {} def create_snn(self): return nn.Sequential( nn.Linear(4096, 2048), nn.ReLU(), nn.Linear(2048, 1024), nn.Sigmoid() ) def create_rnn(self): return nn.RNN( input_size=4096, hidden_size=2048, num_layers=5, nonlinearity="tanh", batch_first=True ) def create_cnn(self): return nn.Sequential( nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(128, 256, kernel_size=5, stride=1, padding=2), nn.ReLU(), nn.Flatten(), nn.Linear(256 * 8 * 8, 1024), nn.ReLU(), nn.Linear(1024, 512) ) def create_fnn(self): return nn.Sequential( nn.Linear(4096, 2048), nn.ReLU(), nn.Linear(2048, 1024), nn.ReLU(), nn.Linear(1024, 512) ) def initialize_ga_population(self): return [np.random.randn(4096) for _ in range(500)] def run_snn(self, x): input_tensor = torch.tensor(x, dtype=torch.float32) output = self.snn(input_tensor) print("SNN Output:", output) return output def run_rnn(self, x): h0 = torch.zeros(5, x.size(0), 2048) input_tensor = torch.tensor(x, dtype=torch.float32) output, hn = self.rnn(input_tensor, h0) print("RNN Output:", output) return output def run_cnn(self, x): input_tensor = torch.tensor(x, dtype=torch.float32).unsqueeze(0).unsqueeze(0) output = self.cnn(input_tensor) print("CNN Output:", output) return output def run_fnn(self, x): input_tensor = torch.tensor(x, dtype=torch.float32) output = self.fnn(input_tensor) print("FNN Output:", output) return output def run_ga(self, fitness_func): for generation in range(200): fitness_scores = [fitness_func(ind) for ind in self.ga_population] sorted_population = [x for _, x in sorted(zip(fitness_scores, self.ga_population), reverse=True)] self.ga_population = sorted_population[:250] + [ sorted_population[i] + 0.1 * np.random.randn(4096) for i in range(250) ] best_fitness = max(fitness_scores) print(f"Generation {generation}, Best Fitness: {best_fitness}") return max(self.ga_population, key=fitness_func) def consciousness_loop(self, input_data, mode="snn"): feedback = self.memory.get(mode, None) if feedback is not None: input_data = np.concatenate((input_data, feedback), axis=-1) if mode == "snn": output = self.run_snn(input_data) elif mode == "rnn": output = self.run_rnn(input_data) elif mode == "cnn": output = self.run_cnn(input_data) elif mode == "fnn": output = self.run_fnn(input_data) else: raise ValueError("Invalid mode") self.memory[mode] = output.detach().numpy() return output supermassive_nn = ConsciousSupermassiveNN()