【机器学习系列（12）】智能算法与终端部署精要-优快云博客

【机器学习系列（12）】智能算法与终端部署精要

一、元学习快速适应

1. MAML核心公式

$θi′=θ−α∇θLTi(fθ)\min_\theta \sum_{\mathcal{T}_i \sim p(\mathcal{T})} \mathcal{L}_{\mathcal{T}_i}(f_{\theta'_i}) \quad \text{其中} \ \theta'_i = \theta - \alpha \nabla_\theta \mathcal{L}_{\mathcal{T}_i}(f_\theta)$

2. 小样本正弦回归实战

import higher
import torch

# 任务生成器
def sample_task():
    amp = torch.rand(1)*5 + 1  # 随机振幅1-6
    phase = torch.rand(1)*3     # 随机相位0-3
    return lambda x: amp * torch.sin(x + phase)

# MAML训练
model = torch.nn.Linear(1, 20)
opt = torch.optim.Adam(model.parameters(), lr=1e-3)

for _ in range(1000):
    task = sample_task()
    with higher.innerloop_ctx(model, opt) as (fnet, diffopt):
        # 内循环适应
        for _ in range(3):  # 3步梯度更新
            x = torch.rand(5,1)*2 - 1  # 5个支持样本
            loss = (fnet(x) - task(x)).pow(2).mean()
            diffopt.step(loss)
        # 外循环优化
        x_query = torch.rand(10,1)*2 -1
        meta_loss = (fnet(x_query) - task(x_query)).pow(2).mean()
        meta_loss.backward()
        opt.step()

二、联邦学习隐私保护

1. 联邦平均算法

$θglobalt+1=1N∑i=1Nθlocal(i,t)(服务器聚合本地模型)\theta_{global}^{t+1} = \frac{1}{N} \sum_{i=1}^N \theta_{local}^{(i,t)} \quad \text{(服务器聚合本地模型)}$

2. PySyft联邦训练示例

import syft as sy
hook = sy.TorchHook(torch)

# 创建虚拟客户端
clients = [sy.VirtualWorker(hook, id=f"client{i}") for i in range(3)]

# 数据分片
mnist_data = torch.load('mnist.pt')
data_shards = mnist_data.split([20000, 20000, 20000])
for client, shard in zip(clients, data_shards): shard.send(client)

# 联邦训练循环
global_model = torch.nn.Linear(784,10)
for round in range(10):
    local_models = []
    for client in clients:
        # 客户端本地训练
        model = global_model.copy().send(client)
        opt = torch.optim.SGD(model.parameters(), lr=0.1)
        data = client.data      
        for _ in range(5):  # 本地5批次训练
            x, y = data.next_batch(64)
            loss = torch.nn.functional.cross_entropy(model(x), y)
            loss.backward()
            opt.step()
            opt.zero_grad()
        local_models.append(model.copy().get())
    # 模型聚合
    with torch.no_grad():
        global_model.weight.data = sum(m.weight.data for m in local_models)/3

三、AIoT终端极简部署

1. 微控制器代码生成

# 导出TensorFlow Lite Micro格式
import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

# 保存为C头文件
with open('model.h', 'w') as f:
    f.write('const unsigned char model[] = {')
    f.write(','.join([str(b) for b in tflite_model]))
    f.write('};')