Keras中的多步多元时间序列分类

我想知道你是否能帮助我，我有一个三级时间序列分类问题，我需要预测未来的两级。 我用一个热编码器对输出进行了编码，所以我有3列 我对输出层的尺寸有疑问。当我朝着未来迈出一步时，我可以用

model.add（密集（3，activation='softmax'））

我已经完成的代码还可以，但是我不确定当我有两个步骤时，应该使用哪一个输出层

我的培训数据的维度如下：

氧化钇2 (100861, 2, 3) xtrain2 （100861,6,9）

---

---

嗨，我找到了答案，通过使用Keras functional API和多输出神经网络解决了问题，代码摘录如下：

 y1 = ytrain2[:, 0].reshape((ytrain2.shape[0], ytrain2.shape[1]))
 y2 = ytrain2[:, 1].reshape((ytrain2.shape[0], ytrain2.shape[1]))
 y3 = ytrain2[:, 2].reshape((ytrain2.shape[0],ytrain2.shape[1]))
 
 Input_1= Input(shape=(n_input,))

 x = Dense(120, activation='relu')(Input_1)
 x=  Dropout(0.15)(x)
 x = Dense(120, activation='relu')(x)
 x=  Dropout(0.15)(x)
 x = Dense(120, activation='relu')(x)
 x=  Dropout(0.15)(x)

 out1 = Dense(3,activation='softmax')(x)
 out2 = Dense(3,activation='softmax')(x)
 out3 = Dense(3,activation='softmax')(x)
 model = Model(inputs=Input_1, outputs=[out1, out2, out3])
 model.compile(optimizer=optimizer, loss='categorical_crossentropy',metrics=rec)
 model.fit(X, [y1,y2,y3],epochs=1054,batch_size=1024,shuffle=True,validation_split=0.12,callbacks=[reduce_lr,callbackEarlyStop], verbose=2  

 y1 = ytrain2[:, 0].reshape((ytrain2.shape[0], ytrain2.shape[1]))
 y2 = ytrain2[:, 1].reshape((ytrain2.shape[0], ytrain2.shape[1]))
 y3 = ytrain2[:, 2].reshape((ytrain2.shape[0],ytrain2.shape[1]))
 
 Input_1= Input(shape=(n_input,))

 x = Dense(120, activation='relu')(Input_1)
 x=  Dropout(0.15)(x)
 x = Dense(120, activation='relu')(x)
 x=  Dropout(0.15)(x)
 x = Dense(120, activation='relu')(x)
 x=  Dropout(0.15)(x)

 out1 = Dense(3,activation='softmax')(x)
 out2 = Dense(3,activation='softmax')(x)
 out3 = Dense(3,activation='softmax')(x)
 model = Model(inputs=Input_1, outputs=[out1, out2, out3])
 model.compile(optimizer=optimizer, loss='categorical_crossentropy',metrics=rec)
 model.fit(X, [y1,y2,y3],epochs=1054,batch_size=1024,shuffle=True,validation_split=0.12,callbacks=[reduce_lr,callbackEarlyStop], verbose=2