门店视频解析DTW《二》——用户画像数据挖掘与销售解决方案

到访客户的订购空调价格purchase price预测技术~

x = layers.Conv1D(128, 5, activation='relu')(embedded_posts)
x = layers.MaxPooling1D(5)(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.MaxPooling1D(5)(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.GlobalMaxPooling1D()(x)
x = layers.Dense(56, activation='relu')(x)
purchprice_prediction = layers.Dense(1, name='purchprice')(x)

此处 ，也可以借鉴深度可分离卷积层的核心思想，注重通道数据信息的混合研究，构架起新的深度可分离回归预测价格模型：

x = layers.Conv1D(128, 5, activation='relu')(embedded_posts)
x = layers.MaxPooling1D(5)(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x= layers.Conv1D(256, 1, activation='relu')(x)
x = layers.MaxPooling1D(5)(x)
x= layers.Conv1D(256, 1, activation='relu')(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x=