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"""
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文件名: train_gpu_blance_10features.py
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训练部分代码
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作者: 王春林
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创建日期: 2023年10月18日
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最后修改日期: 2023年10月20日
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版本号: 1.0.0
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"""
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import numpy as np
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import torch
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from torch import nn
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# 定义 MLP 网络
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class MLP(nn.Module):
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def __init__(self):
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super(MLP, self).__init__()
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self.model = nn.Sequential(
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nn.Linear(10, 32), # 输入层
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nn.ReLU(), # 激活函数
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nn.Linear(32, 128), # 隐藏层
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nn.ReLU(), # 激活函数
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nn.Linear(128, 32), # 隐藏层
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nn.ReLU(), # 激活函数
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nn.Linear(32, 4), # 输出层，4个类别
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)
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def forward(self, x):
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return self.model(x)
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# 检查GPU是否可用
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#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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device = torch.device("cpu")
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def predict_with_model(data):
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# # 读取特征和标签
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# data = pd.read_excel('feature_label.xlsx')
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# 以下是你的特征名
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feature_names = ["强迫症状数字化", "人际关系敏感数字化", "抑郁数字化", "多因子症状", "母亲教养方式数字化", "父亲教养方式数字化", "自评家庭经济条件数字化", "有无心理治疗（咨询）史数字化", "学业情况数字化", "出勤情况数字化"]
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# 定义特征权重列表
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feature_weights = [0.135, 0.085, 0.08, 0.2, 0.09, 0.09, 0.06, 0.06, 0.08, 0.12]
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# 找到最大值
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max_value = max(feature_weights)
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# 缩放权重
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feature_weights_scaled = [x / max_value for x in feature_weights]
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# 打印缩放后的特征权重
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# print("Scaled Feature Weights:", feature_weights_scaled)
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# 将特征和标签分开，并做归一化处理
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X = data[feature_names].values
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# 分别乘以权重，放在归一化后
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for i in range(len(feature_names)):
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X[:, i] = X[:, i] * feature_weights_scaled[i]
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model = MLP().to(device)
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model.load_state_dict(torch.load('psychology.pth', map_location=device))
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model.eval()
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# 转换输入数据为 Tensor，并放到设备上
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X_tensor = torch.from_numpy(X).float().to(device)
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# 推理
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with torch.no_grad():
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outputs = model(X_tensor)
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# 获取预测结果
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_, predictions = torch.max(outputs, 1)
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# 实际类别从1开始，程序类别从0开始
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predictions += 1
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# 打印预测结果
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# print("预测结果:", predictions.cpu().numpy())
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# 返回预测结果
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return predictions.cpu().numpy().tolist() |