Keras 中的多步多元时间序列分类

如何解决Keras 中的多步多元时间序列分类

不知道你能不能帮帮我，我有一个3类时间序列分类问题，我需要预测未来最多两步的类。 我用一个热编码器对输出进行了编码，所以我有 3 列 我对输出层的维度有疑问。当我只迈向未来的一步时，我可以使用

"model.add(Dense(3,activation='softmax'))"

以及我工作正常的代码，但是当我有 2 个步骤时，我不确定要使用哪个输出层。

我的训练数据维度如下：

ytrain2 (100861,2,3) xtrain2 (100861,6,9)

---

from numpy import array
print(xnorm.shape)
Xtrain2,ytrain2 = split_sequence(xnorm,ynorm,2)
print(ytrain2)
Xtestf,ytestf = split_sequence(xtestnorm,ytestnorm,2)
print(ytrain2.shape)
recallV=list()
misclasV=list()
for i in n:
  for j in neurons:
    from keras.layers import Dense
    from keras.models import Sequential
    from keras.layers import Dropout
    import tensorflow as tf
    import keras as ks
    n_input = Xtrain2.shape[1] * Xtrain2.shape[2]
    ny=ytrain2.shape[1] * ytrain2.shape[2]
    X = Xtrain2.reshape(Xtrain2.shape[0],n_input)
    ytrain3=ytrain2.reshape(ytrain2.shape[0],ny)
    optimizer = ks.optimizers.Adam(lr=0.01)
    from keras.callbacks import ReduceLROnPlateau
    from keras.callbacks import EarlyStopping
    callbackEarlyStop = tf.keras.callbacks.EarlyStopping(monitor='val_loss',patience=15)

    reduce_lr = ReduceLROnPlateau(monitor='val_loss',factor=0.95,patience=5,min_lr=0.0000001,verbose=1)
    # define model
    model = Sequential()
    model.add(Dense(j,activation='relu',input_dim=n_input))
    model.add(Dropout(0.1))
    model.add(Dense(j,activation='relu'))
    model.add(Dropout(0.1))
    ###problem is here##
    model.add(Dense(3,activation='softmax'))**
    model.compile(optimizer=optimizer,loss='categorical_crossentropy',metrics=['accuracy'])

    model.fit(X,ytrain3,epochs=100,batch_size=256,shuffle=False,validation_split=0.2,callbacks=[reduce_lr,callbackEarlyStop],verbose=2)

解决方法

嗨，我找到了答案，问题只是通过使用 Keras 函数式 API 并执行多输出神经网络来解决，代码摘录是：

 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