import pandas as pd import numpy as np from keras.models import Sequential from keras.layers import Conv1D, MaxPooling1D, Flatten, Dense

读取数据

data = pd.read_excel('E:\pythonProject5\深度学习\新建 XLS 工作表.xls')

将数据转化为二维数组

data = np.array(data)

定义CNN模型

model = Sequential() model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(None, 1))) model.add(MaxPooling1D(pool_size=2)) model.add(Conv1D(filters=64, kernel_size=3, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(100, activation='relu')) model.add(Dense(1, activation='linear'))

编译模型

model.compile(loss='mean_squared_error', optimizer='adam')

将数据转化为适合CNN输入的形式

X = data[:, :-1] # 取数据的前n-1列作为输入 X = X.reshape(X.shape[0], X.shape[1], 1) # 将输入数据的最后一维设为1 y = data[:, -1]

训练模型

model.fit(X, y, epochs=10, batch_size=32)

提取特征

feature_map = model.predict(X)