AI 创作日记 | 当新零售遇见 MoE 架构：DeepSeek 模型设计的商业启示

import torch
import torch.nn as nn

# 传统全连接网络
class DNN(nn.Module):
    def __init__(self, input_dim=256):
        super().__init__()
        self.fc = nn.Sequential(
            nn.Linear(input_dim, 512),
            nn.ReLU(),
            nn.Linear(512, 256),
            nn.Linear(256, 1)
        )
        
    def forward(self, x):
        return self.fc(x)

# MoE网络实现
class RetailMoE(nn.Module):
    def __init__(self, num_experts=4):
        super().__init__()
        # 定义领域专家
        self.promo_expert = nn.Linear(256, 128)
        self.inventory_expert = nn.Linear(256, 128) 
        self.user_behavior_expert = nn.Linear(256, 128)
        self.store_ops_expert = nn.Linear(256, 128)
        
        # 门控网络
        self.gate = nn.Sequential(
            nn.Linear(256, 64),
            nn.ReLU(),
            nn.Linear(64, num_experts),
            nn.Softmax(dim=1)
        )
        
    def forward(self, x):
        # 专家输出
        expert_outs = torch.stack([
            self.promo_expert(x),
            self.inventory_expert(x),
            self.user_behavior_expert(x),
            self.store_ops_expert(x)
        ], dim=2)
        
        # 动态权重
        gates = self.gate(x).unsqueeze(1)
        
        # 加权融合
        return (gates @ expert_outs).squeeze(1)

import tensorflow as tf
from tensorflow.keras.layers import Layer

class Expert(Layer):
    """品类专属专家网络"""
    def __init__(self, units=64):
        super().__init__()
        self.dense1 = tf.keras.layers.Dense(units, activation='relu')
        self.dense2 = tf.keras.layers.Dense(units//2)
        
    def call(self, inputs):
        x = self.dense1(inputs)
        return self.dense2(x)

class GatingNetwork(Layer):
    """动态门控权重生成"""
    def __init__(self, num_experts):
        super().__init__()
        self.gate_layer = tf.keras.layers.Dense(num_experts, activation='softmax')
        
    def call(self, inputs):
        # 输入：实时销售数据+库存状态
        return self.gate_layer(inputs)

class MoE(Layer):
    """混合专家决策系统"""
    def __init__(self, experts):
        super().__init__()
        self.experts = experts
        self.gate = GatingNetwork(len(experts))
        
    def call(self, inputs):
        gate_weights = self.gate(inputs)  # 生成专家权重
        expert_outputs = [expert(inputs) for expert in self.experts]
        # 加权聚合专家输出
        return tf.tensordot(gate_weights, expert_outputs, axes=1)

def dynamic_resource_allocation(batch_data):
    """基于数据特征动态分配计算资源"""
    # 特征重要性分析
    importance = calculate_feature_importance(batch_data)
    
    allocation = {
        'regional': 0,
        'category': 0,
        'timeliness': 0
    }
    
    # 规则1：地域差异特征占比超过30%时
    if importance['regional'] > 0.3:
        allocation['regional'] += 0.6
        allocation['category'] += 0.3
    
    # 规则2：实时订单增速超过200%时
    if batch_data['order_growth'] > 2.0:
        allocation['timeliness'] += 0.5
        allocation['regional'] -= 0.2
        
    return normalize_allocation(allocation)

# 模型推理过程
inputs = prepare_inputs(
    region_diff=0.8,  # 区域差异度
    urgency=0.95,     # 紧急程度
    category='fresh'  # 生鲜品类
)

moe_model = load_pretrained_moe()
output = moe_model(inputs)

# 专家权重输出
print(gating_weights)  
# 输出：区域专家0.7 | 时效专家0.25 | 品类专家0.05

def train_moe():
    # 差异化学习率设置
    optimizer = torch.optim.AdamW([
        {'params': gate.parameters(), 'lr': 1e-3},
        {'params': experts.parameters(), 'lr': 5e-4}
    ])
    
    # 专家负载均衡损失
    def load_balancing_loss(weights):
        expert_load = torch.mean(weights, dim=0)
        return 0.01 * torch.sum(expert_load * torch.log(expert_load))
    
    # 训练循环
    for x, y in dataloader:
        pred = moe(x)
        task_loss = F.mse_loss(pred, y)
        balance_loss = load_balancing_loss(gate_weights)
        total_loss = task_loss + balance_loss
        optimizer.zero_grad()
        total_loss.backward()
        optimizer.step()

def calculate_roi():
    base_cost = 2.3e4  # 原系统月成本
    moe_cost = 1.1e4
    revenue_gain = 5e4  # 销售额提升
    
    payback_period = (moe_cost * 12) / (revenue_gain + (base_cost - moe_cost))
    return f"投资回收期：{payback_period:.1f}月"

print(calculate_roi())  # 输出：投资回收期：2.3月