Keras LSTM 模型未学习

Question

几天前我写了这段代码，我有一些错误，但在一些帮助下，我能够修复它们。 模型不是在学习。 我尝试了不同的批次大小、不同的时期数、不同的激活函数，并多次检查我的数据以查找我无法找到的缺陷。 一个学校项目将在一周左右的时间内到期。 任何帮助都将受到高度重视。

这是代码。

from keras.layers import Dense, Input, Concatenate, Dropout
from sklearn.preprocessing import MinMaxScaler
from keras.models import Model
from keras.layers import LSTM
import tensorflow as tf
import NetworkRequest as NR
import ParseNetworkRequest as PNR
import numpy as np


def buildModel():
    _Price = Input(shape=(1, 1))
    _Volume = Input(shape=(1, 1))
    PriceLayer = LSTM(128)(_Price)
    VolumeLayer = LSTM(128)(_Volume)
    merged = Concatenate(axis=1)([PriceLayer, VolumeLayer])
    Dropout(0.2)
    dense1 = Dense(128, input_dim=2, activation='relu', use_bias=True)(merged)
    Dropout(0.2)
    dense2 = Dense(64, input_dim=2, activation='relu', use_bias=True)(dense1)
    Dropout(0.2)
    output = Dense(1, activation='softmax', use_bias=True)(dense2)

    opt = tf.keras.optimizers.Adam(learning_rate=1e-3, decay=1e-6)

    _Model = Model(inputs=[_Price, _Volume], output=output)
    _Model.compile(optimizer=opt, loss='mse', metrics=['accuracy'])

    return _Model


if __name__ == '__main__':
    api_key = "47BGPYJPFN4CEC20"
    stock = "DJI"
    Index = ['4. close', '5. volume']

    RawData = NR.Initial_Network_Request(api_key, stock)

    Closing = PNR.Parse_Network_Request(RawData, Index[0])
    Volume = PNR.Parse_Network_Request(RawData, Index[1])
    Length = len(Closing)

    scalar = MinMaxScaler(feature_range=(0, 1))

    Closing_scaled = scalar.fit_transform(np.reshape(Closing[:-1], (-1, 1)))
    Volume_scaled = scalar.fit_transform(np.reshape(Volume[:-1], (-1, 1)))
    Labels_scaled = scalar.fit_transform(np.reshape(Closing[1:], (-1, 1)))

    Train_Closing = Closing_scaled[:int(0.9 * Length)]
    Train_Closing = np.reshape(Train_Closing, (Train_Closing.shape[0], 1, 1))

    Train_Volume = Volume_scaled[:int(0.9 * Length)]
    Train_Volume = np.reshape(Train_Volume, (Train_Volume.shape[0], 1, 1))

    Train_Labels = Labels_scaled[:int((0.9 * Length))]
    Train_Labels = np.reshape(Train_Labels, (Train_Labels.shape[0], 1))

    # -------------------------------------------------------------------------------------------#

    Test_Closing = Closing_scaled[int(0.9 * Length):(Length - 1)]
    Test_Closing = np.reshape(Test_Closing, (Test_Closing.shape[0], 1, 1))

    Test_Volume = Volume_scaled[int(0.9 * Length):(Length - 1)]
    Test_Volume = np.reshape(Test_Volume, (Test_Volume.shape[0], 1, 1))

    Test_Labels = Labels_scaled[int(0.9 * Length):(Length - 1)]
    Test_Labels = np.reshape(Test_Labels, (Test_Labels.shape[0], 1))

    Predict_Closing = Closing_scaled[-1]
    Predict_Closing = np.reshape(Predict_Closing, (Predict_Closing.shape[0], 1, 1))

    Predict_Volume = Volume_scaled[-1]
    Predict_Volume = np.reshape(Predict_Volume, (Predict_Volume.shape[0], 1, 1))

    Predict_Label = Labels_scaled[-1]
    Predict_Label = np.reshape(Predict_Label, (Predict_Label.shape[0], 1))

    model = buildModel()
    model.fit(
        [
            Train_Closing,
            Train_Volume
        ],
        [
            Train_Labels
        ],
        validation_data=(
            [
                Test_Closing,
                Test_Volume
            ],
            [
                Test_Labels
            ]
        ),
        epochs=10,
        batch_size=Length
    )

这是我运行时的输出。

Using TensorFlow backend.
2020-01-01 16:31:47.905012: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2199985000 Hz
2020-01-01 16:31:47.906105: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x49214f0 executing computations on platform Host. Devices:
2020-01-01 16:31:47.906137: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version
/home/martin/PycharmProjects/MarketPredictor/Model.py:26: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=[<tf.Tenso..., outputs=Tensor("de...)`
  _Model = Model(inputs=[_Price, _Volume], output=output)
Train on 4527 samples, validate on 503 samples
Epoch 1/10

4527/4527 [==============================] - 1s 179us/step - loss: 0.4716 - accuracy: 2.2090e-04 - val_loss: 0.6772 - val_accuracy: 0.0000e+00
Epoch 2/10

4527/4527 [==============================] - 0s 41us/step - loss: 0.4716 - accuracy: 2.2090e-04 - val_loss: 0.6772 - val_accuracy: 0.0000e+00
Epoch 3/10

4527/4527 [==============================] - 0s 42us/step - loss: 0.4716 - accuracy: 2.2090e-04 - val_loss: 0.6772 - val_accuracy: 0.0000e+00
Epoch 4/10

4527/4527 [==============================] - 0s 42us/step - loss: 0.4716 - accuracy: 2.2090e-04 - val_loss: 0.6772 - val_accuracy: 0.0000e+00
Epoch 5/10

4527/4527 [==============================] - 0s 43us/step - loss: 0.4716 - accuracy: 2.2090e-04 - val_loss: 0.6772 - val_accuracy: 0.0000e+00
Epoch 6/10

4527/4527 [==============================] - 0s 39us/step - loss: 0.4716 - accuracy: 2.2090e-04 - val_loss: 0.6772 - val_accuracy: 0.0000e+00
Epoch 7/10

4527/4527 [==============================] - 0s 42us/step - loss: 0.4716 - accuracy: 2.2090e-04 - val_loss: 0.6772 - val_accuracy: 0.0000e+00
Epoch 8/10

4527/4527 [==============================] - 0s 39us/step - loss: 0.4716 - accuracy: 2.2090e-04 - val_loss: 0.6772 - val_accuracy: 0.0000e+00
Epoch 9/10

4527/4527 [==============================] - 0s 42us/step - loss: 0.4716 - accuracy: 2.2090e-04 - val_loss: 0.6772 - val_accuracy: 0.0000e+00
Epoch 10/10

4527/4527 [==============================] - 0s 38us/step - loss: 0.4716 - accuracy: 2.2090e-04 - val_loss: 0.6772 - val_accuracy: 0.0000e+00

Process finished with exit code 0

损失高，准确率为0，请帮忙。

Answer 1

您正在使用为分类任务创建的激活函数和指标，而不是股票预测任务（具有连续目标）。

对于连续目标，您的最终激活层应该是linear 。 指标应该是mse或mae ，而不是accuracy 。

只有dji预测与实际价格完全相同，才能满足accuracy 。 由于dji至少有 7 位数字，这几乎是不可能的。

Answer 2

这是我的建议：

1. 使用更简单的网络：不确定您的数据集有多大，但有时会使用密集网络。 层没有帮助。 看起来中间层的权重根本没有变化。 尝试只有一个密集层的模型。
2. 减少 dropout ：尝试在Dropout(0.1)使用一个 dropout 层。
3. Adam defaults ：首先使用带有默认参数的adam优化器。
4. 指标选择：正如 Nicolas 的回答所提到的，使用回归指标而不是准确性。