cnn做回归预测python代码

以下是使用CNN进行回归预测的Python代码示例：

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv2D, Flatten

# 读取数据集
data = pd.read_csv('regression_data.csv')

# 将特征和标签分离
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 对特征进行归一化处理
X_train = X_train / 255.0
X_test = X_test / 255.0

# 将特征重塑为图像格式
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)

# 创建CNN模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(X_train.shape[1], 1)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='linear'))

# 编译模型
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mean_absolute_error'])

# 训练模型
history = model.fit(X_train, y_train, batch_size=32, epochs=50, validation_data=(X_test, y_test))

# 使用测试集评估模型
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

这是一个简单的CNN模型，其中包括两个卷积层、一个Dropout层、一个Flatten层和两个全连接层。在编译模型时，我们使用均方误差作为损失函数，并使用Adam优化器进行优化。在训练模型时，我们使用训练集进行训练，并使用测试集评估模型。