Keras LSTM序列分类-损失和验证损失减少了琐碎的数量，准确性和验证准确性保持不变

在我正式放弃将深度学习应用于股票预测的希望之前，我希望能有一双快速的第二双眼睛

目标是使用LSTM预测两类中的一类。正类对应于在接下来的六个时期内导致价格上涨5%或以上的序列，负类对应于没有上涨的序列。正如预期的那样，这导致了一点阶级不平衡，比例约为6:1负到正但现在的问题是，该模型在所有时代都显示出相同的准确性，并且只预测了负面类别。这让我觉得我的模型结构可能有问题。输入为adataframe，其中包括价格数据和少量移动平均数：

                   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学习方法

MinMaxScaler

进行规范化。例如，第一个序列如下所示：

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

当我在这些序列上构建、编译和拟合一个模型时，我的结果很快稳定下来，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，它只预测了负类：

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

我首先要说，这可能是因为预测股价切入点是一项充满噪音的任务。但我也希望该模型至少会做出一些错误的猜测，而不是简单地猜测同一类。对我来说，这似乎是我构建模型或构建输入的方式的问题

X_-train.shape

和

y_-train.shape

分别给我

（6561,6,8）

和

（6561，）

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提前感谢您的帮助

培训数据中“阳性”病例的流行率是多少？你所看到的可能是“不平衡的阶级”的问题。尽管如此，这种股票预测确实不太可能让你致富。任何可能出现的信号都是更早地被提取出来的，更复杂的模型，计算能力远远超过你，而观察到的股价，几乎可以定义为剩余随机性。它确定了sigmoid和ReLU激活在这里是如何交互的…sigmoid+舍入为0表示任何<≈ 10也许可以试试softmax？@MatthiasWinkelmann我在原始帖子中回应过。谢谢你的帮助。