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I am trying to perform a 10-fold cross-validation on a LSTM, the code is the following:
Predict Closing Prices using a 3 day window of previous closing prices.we use window_size = 4 for this.

    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import LSTM, Dense, Dropout
    # Build the LSTM model.
    # Define the LSTM RNN model.
    model = Sequential()
     
    number_units = 9  
    dropout_fraction = 0.5
     
    # Layer 1
    model.add(LSTM(
        units=number_units,
        return_sequences=True,
        input_shape=(X_train.shape[1], 1))
        )
       
    model.add(Dropout(dropout_fraction))
     
    # Layer 2
    # The return_sequences parameter needs to set to True every time we add a new LSTM layer, excluding the final layer.
    model.add(LSTM(units=number_units, return_sequences=True))
    model.add(Dropout(dropout_fraction))
     
    # Layer 3
    model.add(LSTM(units=number_units))
    model.add(Dropout(dropout_fraction))
     
    # Output layer
    model.add(Dense(1))
     
    # Compile the model
    model.compile(optimizer="adam", loss="mean_squared_error")
    # Summarize the model
    model.summary()
   
     
     
    # Train the model
    model.fit(X_train, y_train, epochs=500, shuffle=False, batch_size=5, verbose=1)
     
    # Evaluate the model for loss
    model.evaluate(X_test, y_test)
     
    # Make some predictions
    predicted = model.predict(X_test)
     
    import sklearn.metrics as metrics
    # Evaluating the model
    print('RMSD ( Root Mean Squared Error ) :', np.sqrt(metrics.mean_squared_error(y_test, predicted)))
    print('R-squared :', metrics.r2_score(y_test, predicted))
     
    # Recover the original prices instead of the scaled version
    predicted_prices = y_test_scaler.inverse_transform(predicted)
    real_prices = y_test_scaler.inverse_transform(y_test.reshape(-1, 1))
     
    # Create a DataFrame of Real and Predicted values
    stocks = pd.DataFrame({
        "Real": real_prices.ravel(),
        "Predicted": predicted_prices.ravel()
    }, index = dw.index[-len(real_prices): ])
    stocks.head(30)

The idea is to perform a 10-fold cross-validation and improve RMSE and R-squared results.

A note about my input:

    X, y = window_data(dw, window_size, feature_col_number1, feature_col_number2, feature_col_number3
                       , feature_col_number4 , feature_col_number5, feature_col_number6, target_col_number)
     
    # Use 70% of the data for training and the remaineder for testing
    X_split = int(0.7 * len(X))
    y_split = int(0.7 * len(y))
     
    X_train = X[: X_split]
    X_test = X[X_split:]
    y_train = y[: y_split]
    y_test = y[y_split:]
   
     
   
    #Scaling Data with MinMaxScaler
    from sklearn.preprocessing import MinMaxScaler
     
    # Use the MinMaxScaler to scale data between 0 and 1.
    x_train_scaler = MinMaxScaler()
    x_test_scaler = MinMaxScaler()
    y_train_scaler = MinMaxScaler()
    y_test_scaler = MinMaxScaler()
     
    # Fit the scaler for the Training Data
    x_train_scaler.fit(X_train)
    y_train_scaler.fit(y_train)
     
    # Scale the training data
    X_train = x_train_scaler.transform(X_train)
    y_train = y_train_scaler.transform(y_train)
     
    # Fit the scaler for the Testing Data
    x_test_scaler.fit(X_test)
    y_test_scaler.fit(y_test)
     
    # Scale the y_test data
    X_test = x_test_scaler.transform(X_test)
    y_test = y_test_scaler.transform(y_test)
     
    # Reshape the features for the model
    X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
    X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
     

How do I enter k_fold cross validation? Its code and where to put it?
Thanks.


What I have tried:

How do I enter k_fold cross validation? Its code and where to put it?
Thanks.
Posted
Updated 24-Dec-21 6:47am
v2

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