Z3ta_Z / CSMNN_Template.py
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Create CSMNN_Template.py
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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()