Spaces:
Sleeping
Sleeping
File size: 1,646 Bytes
9dce9f3 |
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 |
# evo_transformer.py
import random
class EvoTransformer:
def __init__(self, config=None):
# Initialize with default or passed config
self.config = config or {
"layers": 4,
"attention_heads": 4,
"ffn_dim": 1024,
"dropout": 0.1,
"memory": False,
}
self.history = [self.config.copy()]
def mutate(self):
new_config = self.config.copy()
trait = random.choice(list(new_config.keys()))
if trait == "layers":
new_config[trait] = max(1, new_config[trait] + random.choice([-1, 1]))
elif trait == "attention_heads":
new_config[trait] = random.choice([2, 4, 6, 8])
elif trait == "ffn_dim":
new_config[trait] = random.choice([512, 1024, 2048])
elif trait == "dropout":
new_config[trait] = round(min(max(0.0, new_config[trait] + random.uniform(-0.05, 0.05)), 0.5), 2)
elif trait == "memory":
new_config[trait] = not new_config[trait]
self.config = new_config
self.history.append(new_config.copy())
def evolve(self, generations=3):
for _ in range(generations):
self.mutate()
def get_history(self):
return self.history
def evaluate(self):
# Simulate an accuracy score for demo purposes
score = round(random.uniform(0.85, 0.95), 4)
return {"accuracy": score, "params": self.estimate_params()}
def estimate_params(self):
# Simulated parameter count based on config
return 10 + self.config["layers"] * self.config["ffn_dim"] * 0.001
|