LSTM，多个二进制数组输入和过度拟合处理

Question

现在我正在研究一个空间环境模型，该模型将使用最近3天的冠状孔信息来预测明天的最大Kp指数。
（数据总量约为4300天）

对于输入，使用3个具有136个元素的数组（一天一个数组，所以3天数据）。例如，

inputArray_day1 = [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
inputArray_day2 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0] 
inputArray_day3 = [0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

输出是长度为28的单个一热点向量，表示第4天的最大 Kp指数。我使用下面的词典轻松地在 Kp索引和一键矢量之间进行转换。

kp2idx = {0.0:0, 0.3:1, 0.7:2, 1.0:3, 1.3:4, 1.7:5, 2.0:6, 2.3:7, 2.7:8, 3.0:9, 3.3:10, 3.7:11, 4.0:12, 4.3:13,
       4.7:14, 5.0:15, 5.3:16, 5.7:17, 6.0:18, 6.3:19, 6.7:20, 7.0:21, 7.3:22, 7.7:23, 8.0:24, 8.3:25, 8.7:26, 9.0:27}
idx2kp = {0:0.0, 1:0.3, 2:0.7, 3:1.0, 4:1.3, 5:1.7, 6:2.0, 7:2.3, 8:2.7, 9:3.0, 10:3.3, 11:3.7, 12:4.0, 13:4.3,
       14:4.7, 15:5.0, 16:5.3, 17:5.7, 18:6.0, 19:6.3, 20:6.7, 21:7.0, 22:7.3, 23:7.7, 24:8.0, 25:8.3, 26:8.7, 27:9.0}

该模型包含两个带辍学的LSTM层。

def fit_lstm2(X,Y,Xv,Yv, n_batch, nb_epoch, n_neu1, n_neu2, dropout):
  model = tf.keras.Sequential()
  model.add(tf.keras.layers.LSTM(n_neu1, batch_input_shape = (n_batch,X.shape[1],X.shape[2]), return_sequences=True))
  model.add(tf.keras.layers.Dropout(dropout))
  model.add(tf.keras.layers.LSTM(n_neu2))
  model.add(tf.keras.layers.Dropout(dropout))
  model.add(tf.keras.layers.Dense(28,activation='softmax'))
  model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy','mse'])

  for i in range(nb_epoch):
    print('epochs : ' + str(i))
    model.fit(X,Y, epochs=1, batch_size = n_batch, verbose=1, shuffle=False,callbacks=[custom_hist], validation_data = (Xv,Yv))
    model.reset_states()

  return model

我尝试了各种神经元数量和辍学率，例如

n_batch = 1
nb_epochs = 100
n_neu1 = [128,64,32,16]
n_neu2 = [64,32,16,8]
n_dropout = [0.2,0.4,0.6,0.8]

for dropout in n_dropout:
  for i in range(len(n_neu1)):
    model = fit_lstm2(x_train,y_train,x_val,y_val,n_batch, nb_epochs,n_neu1[i],n_neu2[i],dropout)

问题在于预测准确性永远不会超过10％，并且过度训练会在初始化训练后不久就开始。

以下是训练历史的一些图像。 （对不起传说的位置）

• n_neu1，n_neu2，辍学=（64,32,0.2）

• n_neu1，n_neu2，dropout =（32,16,0.2）

• n_neu1，n_neu2，辍学=（16,8,0.2）

老实说，我不知道为什么验证准确性永远不会提高，而过度拟合如此迅速地开始。.是否有更好的方法来使用输入数据？我的意思是，我应该标准化或标准化输入吗？

请帮助我，任何意见和建议将不胜感激。