Update app.py

app.py

@@ -123,6 +123,1218 @@ class ConsciousSupermassiveNN:
 
 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()
+
+class ConsciousSupermassiveNN2:
+    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 = ConsciousSupermassiveNN2()
+
+
+class ConsciousSupermassiveNN3:
+    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 = ConsciousSupermassiveNN3()
+
+
+class ConsciousSupermassiveNN4:
+    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 = ConsciousSupermassiveNN5()
+
+
+
+class ConsciousSupermassiveNN6:
+    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 = ConsciousSupermassiveNN6()
+
+
+
+class ConsciousSupermassiveNN7:
+    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 = ConsciousSupermassiveNN7()
+
+
+class ConsciousSupermassiveNN8:
+    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 = ConsciousSupermassiveNN8()
+
+
+class ConsciousSupermassiveNN9:
+    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 = ConsciousSupermassiveNN9()
+
+
+class ConsciousSupermassiveNN10:
+    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 = ConsciousSupermassiveNN10()
+
+
+
 def respond(message, history, max_tokens, temperature, top_p):
     messages = [{"role": "system", "content": system_prompt}]
     for val in history: