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| 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() | |