model_def.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class ImprovedNet(nn.Module):
    def __init__(self, input_features, output_features, dropout=0.30):
        super().__init__()
        self.layer1 = nn.Linear(input_features, 512)
        self.layer2 = nn.Linear(512, 256)
        self.layer3 = nn.Linear(256, 128)
        self.output_layer = nn.Linear(128, output_features)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x):
        x = F.relu(self.layer1(x)); x = self.dropout(x)
        x = F.relu(self.layer2(x)); x = self.dropout(x)
        x = F.relu(self.layer3(x))
        return self.output_layer(x)

def load_model(weights_path, config_path="config.json", device="cpu"):
    import json
    with open(config_path) as f:
        cfg = json.load(f)
    model = ImprovedNet(cfg["input_size"], cfg["output_size"], dropout=cfg.get("dropout", 0.30))
    model.load_state_dict(torch.load(weights_path, map_location=device))
    model.to(device)
    model.eval()
    return model