怎么把lstm模型换为wdcnnlstm模型

Question

这是用LSTM找预测股票价格的最优模型，怎么在这个代码的基础上使用WD_CNN_LSTM来寻找最优模型

import pandas as pd
def parse_date(date_string):
    return pd.Timestamp(date_string.replace('_', '-'))
df = pd.read_csv('D:/LSTMdata.csv', index_col='Date', parse_dates=True, date_parser=parse_date)
df.sort_index(inplace=True)
def Stock_Price_LSTM_Data_Precesing(df,mem_his_days,pre_days):
    df.dropna(inplace=True)
    df.sort_index(inplace=True)
    df['label']= df['Close'].shift(-pre_days)
    from sklearn.preprocessing import StandardScaler
    scaler = StandardScaler()
    sca_X=scaler.fit_transform(df.iloc[:,:-1])
    
    
    mem_his_days = 10
    
    from collections import deque
    deq = deque(maxlen=mem_his_days)
    
    X = []
    for i in sca_X:
        deq.append(list(i))
        if len(deq)==mem_his_days:
            X.append(list(deq))
    X_lately = X[-pre_days:]
    X = X[:-pre_days]
    y = df['label'].values[mem_his_days-1:-pre_days]
    
    
    import numpy as np
    X = np.array(X)
    y = np.array(y)
    return X,y,X_lately
X,y,X_lately = Stock_Price_LSTM_Data_Precesing(df,5,10)
print(len(X))
print(len(y))
print(len(X_lately))
pre_days = 10
mem_days=[5,10,15]
lstm_layers=[1,2,3]
dense_layers=[1,2,3]
units = [16,32]
# mem_days=[10]
# lstm_layers=[1]
# dense_layers=[1]
# units = [32]

from tensorflow.keras.callbacks import ModelCheckpoint
for the_mem_days in mem_days:
    for the_lstm_layers in lstm_layers:
        for the_dense_layers in dense_layers:
            for the_units in units:
                filepath=filepath=f"./theLSTMbestmodel1/{{val_mape:.2f}}{{epoch:02d}}men{the_mem_days}lstm{the_lstm_layers}dense{the_dense_layers}unit{the_units}.keras"
                checkpoint = ModelCheckpoint(
                    filepath=filepath,
                    save_weights_only=False,
                    monitor='val_mape',
                    mode='min',
                    save_best_only=True)
                X,y,X_lately = Stock_Price_LSTM_Data_Precesing(df,the_mem_days,pre_days)
                from sklearn.model_selection import train_test_split
                X_train,X_test,y_train,y_test = train_test_split(X,y,shuffle=False,test_size=0.1)
                import tensorflow as tf
                from tensorflow.keras.models import Sequential
                from tensorflow.keras.layers import LSTM,Dense,Dropout
                model = Sequential()
                model.add(LSTM(the_units,input_shape=X.shape[1:],activation='relu',return_sequences=True))
                model.add(Dropout(0.1))
                for i in range(the_lstm_layers):
                    model.add(LSTM(the_units,activation='relu',return_sequences=True))
                    model.add(Dropout(0.1))
                
                model.add(LSTM(the_units,activation='relu'))
                model.add(Dropout(0.1))
                for i in range(the_dense_layers):
                    model.add(Dense(the_units,activation='relu'))
                    model.add(Dropout(0.1))
                
                model.add(Dense(1))
                model.compile(optimizer='adam',
                             loss='mse',
                             metrics=['mape'])
                model.fit(X_train,y_train,batch_size=32,epochs=50,validation_data=(X_test,y_test),callbacks=[checkpoint])       

Answer 1

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要将LSTM模型替换为WD_CNN_LSTM（卷积门控循环单元）模型，首先我们需要了解WD_CNN_LSTM的基本结构。WD_CNN_LSTM结合了卷积神经网络（CNN）的局部感知能力和长短期记忆（LSTM）在网络中的时间序列处理优势。在WD_CNN_LSTM中，通常会先使用一个卷积层来捕获输入数据的局部特征，然后这些特征被传递给LSTM层进行时序建模。

下面是修改后的代码，将原始LSTM模型替换为WD_CNN_LSTM模型：

import pandas as pd

def parse_date(date_string):
    return pd.Timestamp(date_string.replace('_', '-'))

df = pd.read_csv('D:/LSTMdata.csv', index_col='Date', parse_dates=True, date_parser=parse_date)
df.sort_index(inplace=True)

def Stock_Price_LSTM_Data_Precesing(df, mem_his_days, pre_days):
    df.dropna(inplace=True)
    df.sort_index(inplace=True)
    df['label'] = df['Close'].shift(-pre_days)
    from sklearn.preprocessing import StandardScaler
    scaler = StandardScaler()
    sca_X = scaler.fit_transform(df.iloc[:, :-1])

    mem_his_days = 10

    from collections import deque
    deq = deque(maxlen=mem_his_days)

    X = []
    for i in sca_X:
        deq.append(list(i))
        if len(deq) == mem_his_days:
            X.append(list(deq))
    X_lately = X[-pre_days:]
    X = X[:-pre_days]
    y = df['label'].values[mem_his_days - 1:-pre_days]

    import numpy as np
    X = np.array(X)
    y = np.array(y)
    return X, y, X_lately

X, y, X_lately = Stock_Price_LSTM_Data_Precesing(df, 5, 10)
print(len(X))
print(len(y))
print(len(X_lately))

pre_days = 10
mem_days = [5, 10, 15]
wd_cnn_lstm_layers = [1, 2, 3]
dense_layers = [1, 2, 3]
units = [16, 32]

# mem_days=[10]
# wd_cnn_lstm_layers=[1]
# dense_layers=[1]
# units = [32]

from tensorflow.keras.callbacks import ModelCheckpoint
for the_mem_days in mem_days:
    for the_wd_cnn_lstm_layers in wd_cnn_lstm_layers:
        for the_dense_layers in dense_layers:
            for the_units in units:
                filepath = f"./theLSTMbestmodel1/{{val_mape:.2f}}{{epoch:02d}}men{the_mem_days}wdclstm{the_wd_cnn_lstm_layers}dense{the_dense_layers}unit{the_units}.keras"
                checkpoint = ModelCheckpoint(
                    filepath=filepath,
                    save_weights_only=False,
                    monitor='val_mape',
                    mode='min',
                    save_best_only=True)
                X, y, X_lately = Stock_Price_LSTM_Data_Precesing(df, the_mem_days, pre_days)
                from sklearn.model_selection import train_test_split
                X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=False, test_size=0.1)
                import tensorflow as tf
                from tensorflow.keras.models import Sequential
                from tensorflow.keras.layers import Conv1D, LSTM, Dense, Dropout

                # 使用卷积层作为输入层
                model = Sequential()
                model.add(Conv1D(filters=the_units, kernel_size=3, activation='relu', input_shape=X.shape[1:]))
                model.add(MaxPooling1D(pool_size=2))
                model.add(Flatten())

                # 添加WD_CNN_LSTM层
                for i in range(the_wd_cnn_lstm_layers):
                    model.add(WDCLSTM(units=the_units, return_sequences=True))  # 假设WDCLSTM是定义好的WD_CNN_LSTM层
                    model.add(Dropout(0.1))

                model.add(WDCLSTM(units=the_units))
                model.add(Dropout(0.1))

                # 添加全连接层
                for i in range(the_dense_layers):
                    model.add(Dense(units=the_units, activation='relu'))
                    model.add(Dropout(0.1))

                model.add(Dense(1))
                model.compile(optimizer='adam',
                              loss='mse',
                              metrics=['mape'])

                model.fit(X_train, y_train, batch_size=32, epochs=50, validation_data=(X_test, y_test), callbacks=[checkpoint])

# 注意：这里我们假设了一个名为WDCLSTM的层，你需要自己实现这个WD_CNN_LSTM层。
# 可以参考Keras官方文档或其他库实现类似功能的层，例如tf.keras.layers.CuDNNGRU等。
# 或者使用预训练的模块，如TF-Hub的模块。

在这个代码中，我做了以下更改：

1. 将LSTM层替换为WDCLSTM层，这是一个假设的层，你需要根据实际情况实现它。
2. 在输入层添加了一个卷积层（Conv1D），用于提取局部特征。
3. 修改了文件路径中的模型名称，以便区分WD_CNN_LSTM模型。

记得在运行这段代码之前，确保你已经实现了WDCLSTM层或者找到了可用的预训练模块。此外，由于WD_CNN_LSTM可能不是标准的Keras层，因此你可能需要自行搜索或构建这样的层。如果找不到现成的实现，可以尝试将一个卷积层与LSTM层相结合，创建一个自定义的混合层来模拟WD_CNN_LSTM的功能。