|
import torch |
|
import torch.nn as nn |
|
import torch.nn.functional as F |
|
from collections import deque |
|
from typing import Deque, Dict, Any |
|
|
|
class CognitiveMemory(nn.Module): |
|
"""Memory system dengan dimensi konsisten""" |
|
def __init__(self, context_size: int, capacity: int = 100): |
|
super().__init__() |
|
self.context_size = context_size |
|
self.capacity = capacity |
|
self.memory_queue: Deque[Dict[str, Any]] = deque(maxlen=capacity) |
|
|
|
|
|
self.key_proj = nn.Linear(context_size, context_size) |
|
self.value_proj = nn.Linear(context_size, context_size) |
|
self.importance_decay = nn.Parameter(torch.tensor(0.95)) |
|
|
|
def add_memory(self, context: torch.Tensor, activation: float): |
|
"""Menyimpan memori dengan dimensi yang sesuai""" |
|
importance = torch.sigmoid(torch.tensor(activation * 0.5 + 0.2)) |
|
self.memory_queue.append({ |
|
'context': context.detach().clone(), |
|
'importance': importance, |
|
'age': torch.tensor(0.0) |
|
}) |
|
|
|
def consolidate_memories(self): |
|
"""Konsolidasi memori dengan manajemen dimensi""" |
|
self.memory_queue = deque( |
|
[m for m in self.memory_queue if m['importance'] > 0.2], |
|
maxlen=self.capacity |
|
) |
|
|
|
def retrieve(self, query: torch.Tensor) -> torch.Tensor: |
|
"""Retrieval dengan penanganan dimensi yang aman""" |
|
if not self.memory_queue: |
|
return torch.zeros(self.context_size, device=query.device) |
|
|
|
contexts = torch.stack([m['context'] for m in self.memory_queue]) |
|
keys = self.key_proj(contexts) |
|
values = self.value_proj(contexts) |
|
query_proj = self.key_proj(query) |
|
|
|
scores = F.softmax(keys @ query_proj, dim=0) |
|
return (scores.unsqueeze(1) * values).sum(dim=0) |
