Spaces:
Running
Running
File size: 7,268 Bytes
8c5f2af |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 |
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()
|