Keras LSTM 模型未學習

Question

幾天前我寫了這段代碼，我有一些錯誤，但在一些幫助下，我能夠修復它們。 模型不是在學習。 我嘗試了不同的批次大小、不同的時期數、不同的激活函數，並多次檢查我的數據以查找我無法找到的缺陷。 一個學校項目將在一周左右的時間內到期。 任何幫助都將受到高度重視。

這是代碼。

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
    )

這是我運行時的輸出。

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

損失高，准確率為0，請幫忙。

Answer 1

您正在使用為分類任務創建的激活函數和指標，而不是股票預測任務（具有連續目標）。

對於連續目標，您的最終激活層應該是linear 。 指標應該是mse或mae ，而不是accuracy 。

只有dji預測與實際價格完全相同，才能滿足accuracy 。 由於dji至少有 7 位數字，這幾乎是不可能的。

Answer 2

這是我的建議：

1. 使用更簡單的網絡：不確定您的數據集有多大，但有時會使用密集網絡。 層沒有幫助。 看起來中間層的權重根本沒有變化。 嘗試只有一個密集層的模型。
2. 減少 dropout ：嘗試在Dropout(0.1)使用一個 dropout 層。
3. Adam defaults ：首先使用帶有默認參數的adam優化器。
4. 指標選擇：正如 Nicolas 的回答所提到的，使用回歸指標而不是准確性。