''' Created on 2019年4月11日 @author: User ''' from keras import layers,models,losses,optimizers from BiddingKG.dl.common.Utils import * def getTextCNNModel(shape=(100,60),out_len=6): input_left = layers.Input(shape=shape) input_center = layers.Input(shape=shape) input_right = layers.Input(shape=shape) list_kernel = [5,10,15] out_left = [] out_center = [] out_right = [] for kernel in list_kernel: out_left.append(layers.Conv1D(10, kernel, activation="relu",padding='same')(input_left)) concat_left = layers.merge(out_left,mode="concat") for kernel in list_kernel: out_center.append(layers.Conv1D(4,kernel,activation="relu",padding="same")(input_center)) concat_center = layers.merge(out_center,mode="concat") for kernel in list_kernel: out_right.append(layers.Conv1D(10,kernel,activation="relu",padding="same")(input_right)) concat_right = layers.merge(out_right,mode="concat") matrix_left = layers.Dense(12,activation="relu")(concat_left) matrix_center = layers.Dense(12,activation="relu")(concat_center) matrix_right = layers.Dense(12,activation="relu")(concat_right) concat_matrix = layers.merge([matrix_left,matrix_center,matrix_right],mode="ave") flattern = layers.Flatten()(concat_matrix) out = layers.Dense(out_len,activation="softmax")(flattern) model = models.Model([input_left,input_center,input_right],out) model.compile(optimizer=optimizers.Adadelta(), loss=losses.categorical_crossentropy, metrics=[precision,recall,f1_score]) model.summary() return model if __name__=="__main__": getTextCNNModel()