案例目的

有一份EXCEL标注数据，如下，训练出合适的模型来预测儿童神经缺陷分类。

参考文章：机器学习——5.案例: 乳腺癌预测-CSDN博客

代码逻辑步骤

1. 读取数据
2. 训练集与测试集拆分
3. 数据标准化
4. 数据转化为Pytorch张量
5. label维度转换
6. 定义模型
7. 定义损失计算函数
8. 定义优化器
9. 定义梯度下降函数
10. 模型训练（正向传播、计算损失、反向传播、梯度清空）
11. 模型测试
12. 精度计算

代码实现

import numpy as np
import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler


df = pd.read_excel('/Users/guojun/Desktop/Learning/machine_learning/Preprocess_Without_WDE_Channels_Data.xlsx')

X = df[df.columns[0:8]].values
mapping = {"TD":0,"ADHD":1}
Y = df["Class"].replace(mapping)

# 数据集拆分
X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2,random_state=5)
Y_train = Y_train.to_numpy()
Y_test = Y_test.to_numpy()

# 数据标准化
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)


# 转化为张量
X_train = torch.from_numpy(X_train.astype(np.float32))
X_test = torch.from_numpy(X_test.astype(np.float32))
Y_train = torch.from_numpy(Y_train.astype(np.float32))
Y_test = torch.from_numpy(Y_test.astype(np.float32))

# 真值转为为二维数据
Y_train = Y_train.view(Y_train.shape[0],-1)
Y_test = Y_test.view(Y_test.shape[0],-1)

# 定义模型
class Model(torch.nn.Module):
    def __init__(self,n_input_features):
        super(Model,self).__init__()
        self.linear = torch.nn.Linear(n_input_features,1)
        
    def forward(self,x):
        return torch.sigmoid(self.linear(x))

model = Model(X_train.shape[1])
# 定义损失函数
loss = torch.nn.BCELoss()
# 定义优化器
learning_rate = 0.001
optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)

# 梯度下降函数
def gradient_descent():
    # 预测Y值
    pre_y = model(X_train)
    # 计算损失
    l = loss(pre_y,Y_train)
    # 反向传播
    l.backward()
    # 梯度更新
    optimizer.step()
    # 梯度清空
    optimizer.zero_grad()
    return l,list(model.parameters())

# 模型训练
for i in range(10000):
    l,p = gradient_descent()
    print(l,p)

# 模型测试
mapping = {0:"TD",1:"ADHD"}
index = np.random.randint(0,X_test.shape[0])
pre_y = model(X_test[index])
pre_y = mapping[int(pre_y.round().item())]
gt_y = mapping[int(Y_test[index].item())]
print(pre_y,gt_y)


# 计算模型准确率
pres_y = model(X_test).round()
result = np.where(pres_y==Y_test,1,0)
ac = np.sum(result)/result.size
print(ac)

 即使调整参数后，损失在0.68左右就不会再下降了。

最终的准确率只有54%-60%，我会在后面的笔记中使用深度神经网络来重新训练，提升模型精度。