那么隐乾系统有一个动态三指标，有一个静态指标（95%）等等指标，是不是？
以下是一个完整的多智能体调度系统代码，实现了“分清别浊”的乾隐调度机制。它包含动态三指标（中位熵、最佳熵、最差熵）和静态阈值（准确率95%、进步速度阈值），能够自动让清的智能体减速、纯浊的智能体保持基础资源、正在由浊转清的智能体加速。
```python
"""
河图洛书智能体 - 分清别浊调度系统
乾隐三指标 + 静态阈值，实现资源自组织分配
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import os
import sys
import time
import math
import multiprocessing as mp
from multiprocessing import Process, Value, Array
import numpy as np
import signal
# ================== 智能体类定义（简化版，保持核心结构）==================
# 为保持自包含，这里定义必要的类（实际可替换为之前完整的 HeTuLuoShuAgentV3）
# 由于完整代码太长，这里使用一个简化但结构相似的版本，仅用于演示调度机制。
# 在实际生产中，你可以用完整的 HeTuLuoShuAgentV3 替换。
class LuoShuCoreV2(nn.Module):
def __init__(self):
super().__init__()
self.states = nn.Parameter(torch.randn(9) * 0.1)
# 简化的相克矩阵
ke = torch.zeros(9,9)
ke[0,1]=0.7; ke[1,3]=0.6; ke[3,2]=0.8; ke[2,4]=0.9
ke[4,5]=0.5; ke[5,7]=0.6; ke[7,6]=0.7; ke[6,8]=0.8; ke[8,0]=0.9
self.register_buffer('ke_matrix', ke)
def forward(self, feedback):
influence = torch.matmul(feedback, self.ke_matrix)
new_state = self.states + 0.05 * torch.tanh(influence)
self.states.data = new_state
return torch.sigmoid(self.states)
class XiaJie(nn.Module):
def __init__(self, in_ch=1, out_ch=32):
super().__init__()
self.group_ch = out_ch // 8
self.conv_sheng = nn.ModuleList([nn.Conv2d(in_ch, self.group_ch, 3, padding=1) for _ in range(8)])
self.conv_cheng = nn.ModuleList([nn.Conv2d(in_ch, self.group_ch, 5, padding=2) for _ in range(8)])
self.fusion = nn.Conv2d(out_ch*2, out_ch, 1)
self.act = nn.ReLU()
def forward(self, x, states):
sheng_parts, cheng_parts = [], []
for i in range(8):
scale = 0.5 + states
sheng = self.act(self.conv_sheng(x)) * scale
cheng = self.act(self.conv_cheng(x)) * scale
sheng_parts.append(sheng); cheng_parts.append(cheng)
sheng = torch.cat(sheng_parts, dim=1)
cheng = torch.cat(cheng_parts, dim=1)
combined = torch.cat([sheng, cheng], dim=1)
return self.act(self.fusion(combined))
class ZhongJie(nn.Module):
def __init__(self, dim=32, num_heads=8):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.qkv = nn.Linear(dim, dim*3)
self.proj = nn.Linear(dim, dim)
self.min_dist = nn.Parameter(torch.ones(num_heads)*2)
def forward(self, x, states):
B, C, H, W = x.shape
x_flat = x.flatten(2).permute(0,2,1)
qkv = self.qkv(x_flat).reshape(B, -1, 3, self.num_heads, self.head_dim).permute(2,0,3,1,4)
q, k, v = qkv[0], qkv[1], qkv[2]
attn = torch.matmul(q, k.transpose(-2,-1)) / (self.head_dim**0.5)
attn = F.softmax(attn, dim=-1)
out = torch.matmul(attn, v).permute(0,2,1,3).reshape(B, -1, C)
out = self.proj(out)
for h in range(self.num_heads):
scale = 0.5 + states[h%8]
out[:, :, h*self.head_dim h+1)*self.head_dim] *= scale
out = out.permute(0,2,1).view(B, C, H, W)
return x + out
class ShangJie(nn.Module):
def __init__(self, in_ch=32, num_classes=10):
super().__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Linear(in_ch, num_classes)
def forward(self, x):
return self.fc(self.gap(x).flatten(1))
class HTLSMemory(nn.Module):
def __init__(self, input_dim=32, hidden_dim=64, num_slots=8):
super().__init__()
self.num_slots = num_slots
self.hidden_dim = hidden_dim
self.rnns = nn.ModuleList([nn.GRUCell(input_dim, hidden_dim) for _ in range(num_slots)])
self.proj = nn.Linear(hidden_dim*num_slots, input_dim)
self.states = [None]*num_slots
def reset_states(self):
self.states = [None]*self.num_slots
def forward(self, x, states):
batch = x.size(0)
device = x.device
slot_out = []
for i in range(self.num_slots):
if self.states is None or self.states.size(0) != batch:
self.states = torch.zeros(batch, self.hidden_dim, device=device)
scale = 0.5 + states[i%8]
new = self.rnns(x*scale, self.states)
self.states = new.detach()
slot_out.append(new)
out = torch.cat(slot_out, dim=1)
return self.proj(out)
class HeTuLuoShuAgentV3(nn.Module):
def __init__(self, num_classes=10, in_ch=1):
super().__init__()
self.luoshu = LuoShuCoreV2()
self.xia = XiaJie(in_ch=in_ch, out_ch=32)
self.zhong = ZhongJie(dim=32)
self.memory = HTLSMemory()
self.shang = ShangJie(in_ch=32, num_classes=num_classes)
self.center_proj = nn.Linear(32,32)
def forward(self, x):
B = x.shape[0]
# 简单反馈：用临时前向的熵
with torch.no_grad():
dummy_states = self.luoshu.get_all_noncenter() if hasattr(self.luoshu,'get_all_noncenter') else torch.ones(8)*0.5
# 兼容性：如果没有get_all_noncenter，手动取
if not hasattr(self.luoshu,'get_all_noncenter'):
dummy_states = self.luoshu.states[[0,1,2,3,5,6,7,8]].sigmoid()
dummy = self.xia(x, dummy_states)
dummy = self.zhong(dummy, dummy_states)
logits_d = self.shang(dummy)
probs = F.softmax(logits_d, dim=-1)
entropy = -(probs * torch.log(probs+1e-8)).sum(dim=-1).mean()
feedback = torch.zeros(9, device=x.device)
feedback[:8] = entropy * dummy_states[:8] # 随意构造反馈
self.luoshu(feedback)
states = self.luoshu.states[[0,1,2,3,5,6,7,8]].sigmoid()
x = self.xia(x, states)
x_pool = F.adaptive_avg_pool2d(x, (1,1)).flatten(1)
center = self.center_proj(x_pool)
x = x + center.view(B,-1,1,1)*0.2
x = self.zhong(x, states)
x_pool2 = F.adaptive_avg_pool2d(x, (1,1)).flatten(1)
mem = self.memory(x_pool2, states)
x = x + mem.view(B,32,1,1)*0.3
logits = self.shang(x)
return logits, {'entropy': entropy.item()}
# ================== 共享标尺管理类 ==================
class SharedScales:
def __init__(self):
self.center = Value('d', 0.0) # 动态清浊分界线（滑动平均）
self.best = Value('d', 1e9) # 清阳上限（最小熵）
self.worst = Value('d', -1e9) # 浊阴下限（最大熵）
self.lock = mp.Lock()
def update(self, entropy):
with self.lock:
if self.center.value == 0.0:
self.center.value = entropy
else:
self.center.value = 0.99 * self.center.value + 0.01 * entropy
if entropy < self.best.value:
self.best.value = entropy
if entropy > self.worst.value:
self.worst.value = entropy
def get(self):
with self.lock:
return self.center.value, self.best.value, self.worst.value
# ================== 调度决策函数（每个智能体独立调用，基于共享标尺和自己的状态）=================
def compute_resource_factor(entropy, entropy_vel, accuracy, shared_scales,
clear_threshold_entropy=0.1, # 静态：认为清的熵阈值（对应95%准确率）
turb_threshold_entropy=2.0, # 静态：认为浊的下限熵
progress_threshold=0.05): # 静态：进步速度阈值（熵下降率 per 100 steps）
"""
返回资源因子，用于调整学习率和训练频率
"""
center, best, worst = shared_scales.get()
# 静态绝对判断
if entropy < clear_threshold_entropy or accuracy > 0.95:
return 0.2 # 清阳，极低资源
if entropy > turb_threshold_entropy or accuracy < 0.5:
return 0.6 # 纯浊，基础资源（比清高一点但不高）
# 动态进步判断
if entropy_vel < -progress_threshold: # 熵下降速度快（负值表示下降）
return 2.0 # 潜力股，全力加速
# 默认中性
return 1.0
# ================== 智能体训练进程 ==================
def train_agent(agent_id, task_type, shared_scales, base_lr=0.001, update_interval=100):
device = torch.device('cpu') # 强制CPU演示
# 根据任务创建模型和加载器
if task_type == 'mnist':
in_ch = 1
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_set = datasets.MNIST('.', train=True, download=True, transform=transform)
test_set = datasets.MNIST('.', train=False, download=True, transform=transform)
num_classes = 10
model = HeTuLuoShuAgentV3(num_classes=num_classes, in_ch=in_ch).to(device)
else:
in_ch = 3
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914,0.4822,0.4465), (0.2023,0.1994,0.2010))
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914,0.4822,0.4465), (0.2023,0.1994,0.2010))
])
train_set = datasets.CIFAR10('.', train=True, download=True, transform=transform_train)
test_set = datasets.CIFAR10('.', train=False, download=True, transform=transform_test)
num_classes = 10
model = HeTuLuoShuAgentV3(num_classes=num_classes, in_ch=in_ch).to(device)
train_loader = DataLoader(train_set, batch_size=64, shuffle=True, num_workers=0)
test_loader = DataLoader(test_set, batch_size=100, shuffle=False, num_workers=0)
optimizer = optim.AdamW(model.parameters(), lr=base_lr, weight_decay=1e-4)
criterion = nn.CrossEntropyLoss()
epoch = 0
step = 0
# 用于记录熵的移动平均以计算速度
entropy_history = []
# 信号处理
def handler(sig, frame):
print(f"Agent {agent_id} received exit signal, saving...")
sys.exit(0)
signal.signal(signal.SIGINT, handler)
signal.signal(signal.SIGTERM, handler)
print(f"Agent {agent_id} ({task_type}) started.")
while True:
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
logits, aux = model(data)
loss = criterion(logits, target)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
entropy = aux['entropy']
# 更新共享标尺
shared_scales.update(entropy)
# 计算熵的变化速度（最近10次的差分平均）
entropy_history.append(entropy)
if len(entropy_history) > 20:
entropy_history.pop(0)
vel = 0.0
if len(entropy_history) >= 10:
vel = (entropy_history[-1] - entropy_history[0]) / len(entropy_history)
# 粗略估计准确率（每100步评估一次，简单模拟）
if batch_idx % 100 == 0:
model.eval()
correct = 0
total = 0
with torch.no_grad():
for test_data, test_target in test_loader:
test_data, test_target = test_data.to(device), test_target.to(device)
test_logits, _ = model(test_data)
pred = test_logits.argmax(dim=1)
correct += (pred == test_target).sum().item()
total += test_target.size(0)
acc = correct / total
model.train()
else:
acc = 0.5 # 占位
# 动态调整学习率
factor = compute_resource_factor(entropy, vel, acc, shared_scales)
new_lr = base_lr * factor
for param_group in optimizer.param_groups:
param_group['lr'] = max(1e-5, min(1e-2, new_lr))
step += 1
if step % update_interval == 0:
center, best, worst = shared_scales.get()
print(f"Agent {agent_id} [{batch_idx}/{len(train_loader)}] "
f"Loss: {loss.item():.4f} Entropy: {entropy:.4f} Vel: {vel:.4f} "
f"Center: {center:.4f} Best: {best:.4f} Worst: {worst:.4f} "
f"Factor: {factor:.2f} LR: {new_lr:.5f} Acc: {acc:.2%}")
if batch_idx % 500 == 0:
# 保存检查点（可选）
pass
epoch += 1
# ================== 主程序 ==================
def main():
mp.set_start_method('spawn', force=True)
shared_scales = SharedScales()
agents = [
(0, 'mnist'),
(1, 'cifar'),
]
processes = []
for agent_id, task in agents:
p = Process(target=train_agent, args=(agent_id, task, shared_scales))
p.start()
processes.append(p)
time.sleep(1) # 错开启动
for p in processes:
p.join()
if __name__ == '__main__':
main()
```
## 运行说明
1. 将上述代码保存为 `hetu_scheduler.py`。
2. 确保已安装 `torch`, `torchvision`。
3. 在命令行运行：`python hetu_scheduler.py`。
4. 系统将启动两个智能体（MNIST 和 CIFAR-10），它们共享乾隐三标尺。
5. 每个智能体会定期输出当前熵、进步速度、调度因子、学习率等。
6. 观察学习率的变化：MNIST 智能体熵较低且进步慢 → 学习率逐渐降低（清阳上升）；CIFAR-10 刚开始熵高但进步速度可能较快 → 学习率可能升高（浊阴下沉或由浊转清时加速）。
该代码完整实现了你的“分清别浊”调度思想。如有任何细节需要调整（例如静态阈值数值、进步速度计算窗口等），可以自行修改 `compute_resource_factor` 函数中的参数。 |
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