Question

我在R中使用Keras来预测财务序列。我需要训练MLP有2个隐藏层，每个层有40个神经元来预测股票价格。目标数据由股票价格组成，列车数据由四个价格组成。

input_data看起来像：

               price_lag_4 price_lag_3 price_lag_2 price_lag_1  price
    2018-04-13      157.73      161.21      160.28      162.21 161.37
    2018-04-16      161.21      160.28      162.21      161.37 162.60
    2018-04-17      160.28      162.21      161.37      162.60 166.10
    2018-04-18      162.21      161.37      162.60      166.10 166.44
    2018-04-19      161.37      162.60      166.10      166.44 164.91
    2018-04-20      162.60      166.10      166.44      164.91 162.30

此外，我将数据分成训练和目标集

    train_data = input_data["2014::2017",1:4]
    train_targets = input_data["2014::2017",5]

并使用min-max标准化

对其进行标准化

    train_data = as.matrix(train_data)
    train_targets = as.matrix(train_targets)

    train_data = (train_data - min(train_data)) / (max(train_data) - 
    min(train_data))
    train_targets = (train_targets - min(train_targets)) / 
    (max(train_targets) - min(train_targets))

然后我在输入层构建了4个神经元的MLP，2个隐藏层，每个神经元有40个神经元，输出层有1个神经元。然后我适合它：

    validation_split = 0.05
    model = keras_model_sequential() %>%

    layer_dense(units = 40, activation = "relu", input_shape = 
    dim(train_data)[2]) %>%
    layer_dense(units = 40, activation = "relu") %>%  
    layer_dense(units = 1, activation = "relu")

    model %>% compile(optimizer = optimizer_sgd(), loss = "mse", metrics = 
    c("mae"))

    fit(x = train_data, y = train_targets, epochs = 60, batch_size = 32, 
    validation_split = validation_split)

拟合已融合：

Trained on 956 samples, validated on 51 samples (batch_size=32, epochs=60)
Final epoch (plot to see history):
           val_loss: 0.0004162
val_mean_absolute_error: 0.0159
               loss: 0.0002706
mean_absolute_error: 0.01215

进一步预测我将在2018年使用价格

validation_data = input_data["2018",1:4]
tail(validation_data)
           price_lag_4 price_lag_3 price_lag_2 price_lag_1
2018-04-13      157.73      161.21      160.28      162.21
2018-04-16      161.21      160.28      162.21      161.37
2018-04-17      160.28      162.21      161.37      162.60
2018-04-18      162.21      161.37      162.60      166.10
2018-04-19      161.37      162.60      166.10      166.44
2018-04-20      162.60      166.10      166.44      164.91

prediction_sgd = predict(object = model, x = validation_data)

tail(prediction_sgd)
          [,1]
[71,] 147.2574
[72,] 148.6506
[73,] 148.6407
[74,] 149.8874
[75,] 150.8464
[76,] 151.8221

预测在某种程度上接近价格

validation_targets = prices["2018"]
tail(validation_targets)
         [,1]
2018-04-13 161.37
2018-04-16 162.60
2018-04-17 166.10
2018-04-18 166.44
2018-04-19 164.91
2018-04-20 162.30

所以，这个MLP架构以某种方式工作，但当我将激活函数更改为tanh时，模型变为：

   validation_split = 0.05
   model = keras_model_sequential() %>%

   layer_dense(units = 40, activation = "tanh", input_shape = 
   dim(train_data)[2]) %>%
   layer_dense(units = 40, activation = "tanh") %>%  
   layer_dense(units = 1)

   model %>% compile(optimizer = optimizer_sgd(), loss = "mse", metrics = 
   c("mae"))

   history = model %>% fit(x = train_data, y = train_targets, epochs = 60, 
   batch_size = 32, validation_split = validation_split)

   Trained on 956 samples, validated on 51 samples (batch_size=32, 
   epochs=60)
   Final epoch (plot to see history):
                  val_loss: 0.0306
   val_mean_absolute_error: 0.1728
                    loss: 0.001923
       mean_absolute_error: 0.0343

我得到了奇怪的预测：

    prediction_sgd = predict(object = model, x = validation_data)
    tail(prediction_sgd)
               [,1]
    [71,] 0.9751762
    [72,] 0.9749264
    [73,] 0.9750333
    [74,] 0.9750219
    [75,] 0.9747972
    [76,] 0.9749493

当我使用sigmoid传递函数时，我也会得到奇怪的预测

因此，我有以下问题：

1）为什么预测数据在第二种情况下如此奇怪？我做错了吗？

2）我是否需要规范化目标数据，即在y函数中输入fit？

Answer 1

问题是tanh和sigmoid的输出范围分别为[-1,1]和[0,1]。因此，如果y不在该范围内，则网络无法学习，因为它无法预测这些值。它会尝试预测网络可能的高度，但这只是1.

因此，您需要使用一个函数作为最终激活，该函数能够生成所需输出范围内的值，例如：线性激活或ReLU。但这不适用于中间层。

Keras奇怪预测中的多层感知器

1 个答案: