股票价格特征提取：使用卷积神经网络 (CNN) 提取趋势和波动等重要特征

import tensorflow as tf from tensorflow.keras.layers import Conv1D, MaxPooling1D, Dense, Dropout

inputs = tf.keras.Input(shape=(None, 1)) # None表示输入的序列长度可以是任意值，1表示输入的单个特征维度 x = Conv1D(32, 3, activation='relu')(inputs) # 一维卷积层，32个卷积核，卷积核大小为3 x = MaxPooling1D(2)(x) # 一维最大池化层，池化窗口大小为2 x = Conv1D(64, 3, activation='relu')(x) x = MaxPooling1D(2)(x) x = Conv1D(128, 3, activation='relu')(x) x = MaxPooling1D(2)(x) x = Dense(256, activation='relu')(x) x = Dropout(0.5)(x) # 防止过拟合的dropout层 x = Dense(1)(x)

model = tf.keras.Model(inputs=inputs, outputs=x) model.summary() # 打印模型结构信息