origin_input = Input(shape=(time_step, features)) # 模型输入 time_step*(N+1),N为分解所得分量数 cominput = origin_input[:, :, 1:] # 分解所得分量构成的序列 time_step*N output = concatenate( [Conv1D(kernel_size=3, filters=64, activation='relu', padding='same')(tf.expand_dims(cominput[:, :, ts], axis=-1)) for ts in range(features-1)], axis=-1) output = Dense(64, activation='relu')(output) # 拼接所得结果经全连接层进行降维&转换 res = Conv1D(kernel_size=1, filters=64, activation='relu')(tf.expand_dims(origin_input[:, :, 0], axis=-1)) output = concatenate((output, res), axis=-1) output = Bidirectional(GRU(64, return_sequences=True))(output) output = Bidirectional(GRU(64, return_sequences=True))(output) time_last = tf.transpose(output, [0, 2, 1]) att_1 = Dense(time_step, activation='tanh')(time_last) att_2 = Dense(time_step, activation='softmax', use_bias=False)(att_1) time_att = Multiply()([time_last, att_2]) out = tf.reduce_sum(time_att, axis=-1) output = Dense(1, activation='sigmoid')(out) model = Model(inputs=origin_input, outputs=output, name='proposed_model') opt = tf.keras.optimizers.Adam(learning_rate=0.001) model.compile(loss=losses.mse, optimizer=opt) model.summary() lr_reducer = ReduceLROnPlateau(factor=0.5, patience=5) callbacks = [lr_reducer] model.fit(x_train_scaled, y_train_scaled, epochs=100, batch_size=512, validation_split=0.1, callbacks=callbacks)

这段代码是一个使用 Keras 搭建的神经网络模型，用于进行时间序列数据的预测。该模型使用了卷积神经网络和双向 GRU 神经网络对输入的时间序列数据进行处理和特征提取，然后使用全连接层和注意力机制等结构将处理后的特征进行汇总和加权，最终输出一个标量值作为预测结果。在训练过程中，该模型使用了均方误差作为损失函数，使用 Adam 优化器进行参数优化，并使用 ReduceLROnPlateau 回调函数自动调整学习率，以提高模型的精度和稳定性。