Keras LSTM序列分类-损失和验证损失减少了三倍，准确度和验证准确性保持不变

问题描述

在我正式放弃将深度学习应用于股票预测的希望之前，希望第二眼很快。

目标是使用LSTM预测两个类别之一。正类对应于导致接下来六个周期内价格上涨5％或更高的序列；负类对应于不引起价格上涨的序列。不出所料，这导致了一些阶级失衡，负数与正数之比约为6：1。 现在的问题是，该模型在所有时期都显示出相同的准确性，并且仅预测负数类。这使我认为我的模型结构可能有问题。输入是一个数据框，其中包含价格数据和少量移动平均线：

                   price_open  price_high  price_low  price_close       ma_8      ma_13      ma_21      ma_55  6prd_pctchange  entry_flag
time_period_start                                                                                                                        
11-02-2016 23:00     10.83280    10.98310   10.72591     10.96000  10.932415  10.855693  10.960608  11.087525        0.008535         0.0
11-03-2016 03:00     10.96016    11.02560   10.96000     11.00003  10.937569  10.873219  10.948081  11.075059        0.004544         0.0
11-03-2016 07:00     11.00007    11.14997   10.91000     11.00006  10.954170  10.919378  10.929689  11.062878       -0.007442         0.0
11-03-2016 11:00     11.05829    11.14820   10.90001     10.99208  10.959396  10.923376  10.912183  11.057317        0.008392         0.0
11-03-2016 15:00     10.90170    11.03112   10.70000     10.91529  10.938490  10.933783  10.890906  11.048504        0.006289         0.0
11-03-2016 19:00     10.89420    10.95000   10.82460     10.94980  10.944640  10.947429  10.882745  11.041227        0.005234         0.0
11-03-2016 23:00     10.94128    11.08475   10.88404     11.08475  10.974350  10.957118  10.888859  11.032288        0.011382         0.0
11-04-2016 03:00     11.02761    11.22778   10.94360     10.99813  10.987517  10.967185  10.893531  11.023518       -0.000173         0.0
11-04-2016 07:00     10.95076    11.01814   10.92000     10.92100  10.982642  10.964934  10.904055  11.011691       -0.007187         0.0
11-04-2016 11:00     10.94511    11.06298   10.89000     10.99557  10.982085  10.958244  10.914692  11.000365        0.000318         0.0 

并已转换为长度为6个周期的numpy数组，并使用scikit-learn方法MinMaxScaler进行了规范化。例如，第一个序列如下所示：

array([[0.,0.16552483,0.09965385,0.52742716,0.,1.,1.        ],[0.5648144,0.37805671,0.9996461,0.19101228,0.19104958,0.83911884,0.73073358],[0.74180673,0.80769231,0.80630067,0.69421501,0.60290376,0.46764059],[1.,0.99114867,0.76926923,0.90586292,0.73780155,0.37807623,0.34751414],[0.30555679,0.40566085,0.22515636,0.85124563,0.104818,0.15716305],[0.27229589,0.47923077,0.40710157,0.45309243,0.        ]])

在这些序列上构建，编译和拟合模型时，我的结果很快达到平稳状态，并且该模型最终只能预测负类。

# Constants:
loss = 'binary_crossentropy'
optimizer = 'adam'
epochs = 12
batch_size = 300

# Complie model:
model = Sequential()
model.add(LSTM(100))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss=loss,optimizer=optimizer,metrics=['accuracy']) 
results = model.fit(X_train,y_train,epochs=epochs,batch_size=batch_size,verbose=1,validation_data=(X_test,y_test),shuffle=False)
model.summary()

它输出：

Epoch 1/12
22/22 [==============================] - 0s 16ms/step - loss: 0.5696 - accuracy: 0.8410 - val_loss: 0.3953 - val_accuracy: 0.8885
Epoch 2/12
22/22 [==============================] - 0s 10ms/step - loss: 0.4355 - accuracy: 0.8473 - val_loss: 0.3569 - val_accuracy: 0.8885
Epoch 3/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4379 - accuracy: 0.8473 - val_loss: 0.3612 - val_accuracy: 0.8885
Epoch 4/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4320 - accuracy: 0.8473 - val_loss: 0.3554 - val_accuracy: 0.8885
Epoch 5/12
22/22 [==============================] - 0s 10ms/step - loss: 0.4338 - accuracy: 0.8473 - val_loss: 0.3577 - val_accuracy: 0.8885
Epoch 6/12
22/22 [==============================] - 0s 10ms/step - loss: 0.4297 - accuracy: 0.8473 - val_loss: 0.3554 - val_accuracy: 0.8885
Epoch 7/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4303 - accuracy: 0.8473 - val_loss: 0.3570 - val_accuracy: 0.8885
Epoch 8/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4273 - accuracy: 0.8473 - val_loss: 0.3558 - val_accuracy: 0.8885
Epoch 9/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4285 - accuracy: 0.8473 - val_loss: 0.3577 - val_accuracy: 0.8885
Epoch 10/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4254 - accuracy: 0.8473 - val_loss: 0.3565 - val_accuracy: 0.8885
Epoch 11/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4270 - accuracy: 0.8473 - val_loss: 0.3581 - val_accuracy: 0.8885
Epoch 12/12
22/22 [==============================] - 0s 9ms/step - loss: 0.4243 - accuracy: 0.8473 - val_loss: 0.3569 - val_accuracy: 0.8885
Model: "sequential_6"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_6 (LSTM)                (None,100)               42400     
_________________________________________________________________
dense_6 (Dense)              (None,1)                 101       
=================================================================

快速检查表明它只是在预测否定类别：

predictions = model.predict(X_test)
predictions_round = [1 if x > 0.5 else 0 for x in predictions]
pd.Series(predictions_round).value_counts()

0    1641
dtype: int64

我将第一个说这可能是因为预测股价进入点是一项充满噪音的任务。但我也希望模型至少会犯一些错误猜测而不是简单地猜测所有相同的类。在我看来，这似乎与我建立模型或构建输入的方式有关。

X_train.shape和y_train.shape分别给我(6561,6,8)和(6561,)。

在此先感谢您的帮助！

解决方法

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