基于python的BP神经网络红酒品质分类预测模型

1 导入必要的库

python 复制代码

import pandas as pd  
import numpy as np  
import matplotlib.pyplot as plt  
from sklearn.model_selection import train_test_split  
from sklearn.preprocessing import LabelEncoder  
from tensorflow.keras.models import Sequential  
from tensorflow.keras.layers import Dense  
from tensorflow.keras.callbacks import EarlyStopping  
from sklearn.metrics import classification_report, confusion_matrix
# 忽略Matplotlib的警告（可选）  
import warnings  
warnings.filterwarnings("ignore") 
# 设置中文显示和负号正常显示
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

2 数据加载与预处理

python 复制代码

# 读取数据  
df = pd.read_csv('train.csv')  
  
# 处理缺失值（这里假设我们删除含有缺失值的行）  
df.dropna(inplace=True)  
  
# 处理重复值（这里选择删除重复的行）  
df.drop_duplicates(inplace=True)  
  
# 将'wine types'列的文本转换为数值  
df['wine types'] = df['wine types'].map({'red': 1, 'white': 2})    
# 假设'quality'是我们要预测的标签  
X = df.drop('quality', axis=1)  
y = df['quality']

3 数据探索

python 复制代码

# 选择绘制特征数据的折线图
X_columns_to_plot = X.columns
  
df_plot = df[X_columns_to_plot]  
  
df_plot.plot(subplots=True, figsize=(15, 15))  
plt.tight_layout()  
plt.show()

图 3-1

4 BP神经网络模型构建

python 复制代码

import tensorflow as tf  
from tensorflow.keras.models import Sequential  
from tensorflow.keras.layers import Dense  
from sklearn.model_selection import train_test_split  
from sklearn.preprocessing import StandardScaler  
  
# 分离特征和标签  
X = df.drop('quality', axis=1)  
y = df['quality']  
  
# 划分训练集和测试集  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  
  
# 特征缩放  
scaler = StandardScaler()  
X_train_scaled = scaler.fit_transform(X_train)  
X_test_scaled = scaler.transform(X_test)  
  
# 构建模型  
model = Sequential([  
    Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),  
    Dense(32, activation='relu'),  
    Dense(10, activation='softmax')  # 假设有10个类别，根据实际情况调整  
])  
  
# 编译模型  
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])  
  
# 训练模型  
history = model.fit(X_train_scaled, y_train, epochs=100, validation_split=0.2, verbose=1)

图 4-1

5 训练评估可视化

python 复制代码

# 绘制训练和验证的准确率与损失  
plt.figure(figsize=(12, 6))  
plt.subplot(1, 2, 1)  
plt.plot(history.history['accuracy'], color='#B0D5DF',label='Training Accuracy')  
plt.plot(history.history['val_accuracy'],  color='#1BA784',label='Validation Accuracy')  
plt.title('Training and Validation Accuracy')  
plt.legend()  
  
plt.subplot(1, 2, 2)  
plt.plot(history.history['loss'],  color='#D11A2D',label='Training Loss')  
plt.plot(history.history['val_loss'], color='#87723E', label='Validation Loss')  
plt.title('Training and Validation Loss')  
plt.legend()  
plt.show()

图 5-1 过拟合

成功过拟合了，其实早有预料，我手里的数据集都挺顽固的，训练效果都不好。

6 正则化

这里采用L2正则化

python 复制代码

import tensorflow as tf  
from tensorflow.keras.models import Sequential  
from tensorflow.keras.layers import Dense 
from tensorflow.keras.regularizers import l2  
from sklearn.model_selection import train_test_split  
from sklearn.preprocessing import StandardScaler  
  
# 分离特征和标签  
X = df.drop('quality', axis=1)  
y = df['quality']  
  
# 划分训练集和测试集  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  
  
# 特征缩放  
scaler = StandardScaler()  
X_train_scaled = scaler.fit_transform(X_train)  
X_test_scaled = scaler.transform(X_test)  
  
# 构建模型，添加L2正则化  
model = Sequential([    
    Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],), kernel_regularizer=l2(0.01)),  # 对第一个Dense层的权重添加L2正则化  
    Dense(64, activation='relu', kernel_regularizer=l2(0.01)),  # 对第二个Dense层的权重也添加L2正则化  
    Dense(10, activation='softmax')  # 输出层，假设是多分类问题  
])  
  
# 编译模型  
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])  
  
# 训练模型  
history = model.fit(X_train_scaled, y_train, epochs=100, validation_split=0.2, verbose=1)

python 复制代码

# 绘制训练和验证的准确率与损失  
plt.figure(figsize=(12, 6))  
plt.subplot(1, 2, 1)  
plt.plot(history.history['accuracy'], color='#B0D5DF',label='Training Accuracy')  
plt.plot(history.history['val_accuracy'],  color='#1BA784',label='Validation Accuracy')  
plt.title('Training and Validation Accuracy')  
plt.legend()  
  
plt.subplot(1, 2, 2)  
plt.plot(history.history['loss'],  color='#D11A2D',label='Training Loss')  
plt.plot(history.history['val_loss'], color='#87723E', label='Validation Loss')  
plt.title('Training and Validation Loss')  
plt.legend()  
plt.show()

图 6-1

这就不错了。