我有一个单步模型，若改为多步预测，如何修改 ： input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) conv1 = Conv1D(filters=128 , kernel_size=config.CK, strides=1, activation='relu') # for input1 # 多输入权重共享 conv2 = Conv1D(filters=128 , kernel_size=config.CK, strides=config.CK, activation='relu') # for input2 conv2.set_weights(conv1.get_weights()) # at least use same weight # CNN conv1out = conv1(input1) conv1out = Dropout(config.dropout)(conv1out) # RNN lstm1out = LSTM(config.lstm_batch_size)(conv1out) lstm1out = Dropout(config.dropout)(lstm1out) # Input2: long-term time series with period input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) # CNN conv2out = conv2(input2) conv2out = Dropout(config.dropout)(conv2out) # RNN lstm2out = LSTM(config.lstm_batch_size)(conv2out) lstm2out = Dropout(config.dropout)(lstm2out) lstm_out = concatenate([lstm1out,lstm2out]) model.add(keras.layers.Embedding(input_dim=10000, output_dim=300, mask_zero=True)) model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=128, return_sequences=True))) #model.add(SeqSelfAttention(attention_activation='sigmoid')) model.add(keras.layers.Dense(units=5)) #x = BatchNormalization() #x = Activation('relu')(x) res = Dense(trainY.shape[1])(lstm_out)

若要将单步预测模型改为多步预测模型，需要进行以下修改：

1. 修改网络结构。多步预测模型需要考虑多个时间步的输出，因此需要对模型进行修改，使其能够同时预测多个时间步的输出。可以在模型最后添加一个长度为多步预测步数的全连接层，输出预测结果。

2. 修改损失函数和评价指标。多步预测模型需要考虑多个时间步的输出，因此需要使用适合多步预测的损失函数和评价指标。例如，可以使用平均绝对误差（mean absolute error，MAE）作为损失函数和评价指标。

下面是可能的修改代码：

# 修改网络结构
input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2]))
conv1 = Conv1D(filters=128 , kernel_size=config.CK, strides=1, activation='relu')  # for input1
# 多输入权重共享
conv2 = Conv1D(filters=128 , kernel_size=config.CK, strides=config.CK, activation='relu')  # for input2
conv2.set_weights(conv1.get_weights())  # at least use same weight

# CNN
conv1out = conv1(input1)
conv1out = Dropout(config.dropout)(conv1out)

# RNN
lstm1out = LSTM(config.lstm_batch_size)(conv1out)
lstm1out = Dropout(config.dropout)(lstm1out)

# Input2: long-term time series with period
input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2]))

# CNN
conv2out = conv2(input2)
conv2out = Dropout(config.dropout)(conv2out)

# RNN
lstm2out = LSTM(config.lstm_batch_size)(conv2out)
lstm2out = Dropout(config.dropout)(lstm2out)

lstm_out = concatenate([lstm1out,lstm2out])

model.add(keras.layers.Embedding(input_dim=10000, output_dim=300, mask_zero=True))
model.add(keras.layers.Bidirectional(keras.layers.LSTM(units=128, return_sequences=True)))
#model.add(SeqSelfAttention(attention_activation='sigmoid'))
model.add(keras.layers.Dense(units=5))
model.add(keras.layers.Dense(units=多步预测步数)) # 添加多步预测输出层

# 修改损失函数和评价指标
model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])

需要注意的是，多步预测模型的训练时间较长，需要更多的训练数据和计算资源。