How to Use KFold CrossValidation in a Neural Network

Question

GeeksforGeeks · Accepted Answer

To use K-Fold Cross-Validation in a neural network, you need to perform K-Fold Cross-Validation splits the dataset into K subsets or "folds," where each fold is used as a validation set while the remaining folds are used as training sets. This helps in understanding how the model performs across different subsets of the data and avoids overfitting.Here&rsquo;s how you can implement K-Fold Cross-Validation in Python with a neural network using Keras and Scikit-Learn.Data Preparation: Load, flatten, and normalize the MNIST dataset.K-Fold Cross-Validation Setup: Define KFold with the specified number of splits (n_splits=k), shuffle=True to randomize the dataset, and a fixed random_state for reproducibility.Model Definition: Use the create_model() function to define a neural network with two hidden layers. Compile it using the Adam optimizer and sparse categorical cross-entropy as the loss function.Training and Evaluation: For each fold:Train the model on the training data.Evaluate on the validation data and store the accuracy.Results Summary: Calculate and print the average accuracy across all folds.Python# Import necessary libraries from sklearn.model_selection import KFold from sklearn.metrics import accuracy_score import numpy as np from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.optimizers import Adam from tensorflow.keras.datasets import mnist # Example dataset # Load dataset (X_train, y_train), (X_test, y_test) = mnist.load_data() # Preprocess data: Flatten and normalize X_train = X_train.reshape((X_train.shape[0], -1)) / 255.0 X_test = X_test.reshape((X_test.shape[0], -1)) / 255.0 # Set up K-Fold Cross-Validation k = 5 # Number of folds kf = KFold(n_splits=k, shuffle=True, random_state=42) # Function to create and compile the model def create_model(): model = Sequential([ Dense(128, activation='relu', input_shape=(X_train.shape[1],)), Dense(64, activation='relu'), Dense(10, activation='softmax') # Output layer for classification ]) model.compile(optimizer=Adam(), loss='sparse_categorical_crossentropy', metrics=['accuracy']) return model # List to store accuracy for each fold accuracy_per_fold = [] # K-Fold Cross-Validation for fold, (train_index, val_index) in enumerate(kf.split(X_train)): print(f'Fold {fold + 1}') # Split the data into training and validation sets for this fold X_train_fold, X_val_fold = X_train[train_index], X_train[val_index] y_train_fold, y_val_fold = y_train[train_index], y_train[val_index] # Create a new instance of the model for each fold model = create_model() # Train the model on the training fold model.fit(X_train_fold, y_train_fold, epochs=5, batch_size=32, verbose=0) # Evaluate the model on the validation fold val_predictions = np.argmax(model.predict(X_val_fold), axis=1) accuracy = accuracy_score(y_val_fold, val_predictions) # Store the accuracy for this fold accuracy_per_fold.append(accuracy) print(f'Accuracy for fold {fold + 1}: {accuracy * 100:.2f}%') # Calculate the average accuracy across all folds average_accuracy = np.mean(accuracy_per_fold) print(f' Average Accuracy Across {k} Folds: {average_accuracy * 100:.2f}%') Output: Fold 1375/375 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/stepAccuracy for fold 1: 97.22%Fold 2375/375 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/stepAccuracy for fold 2: 97.54%Fold 3375/375 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/stepAccuracy for fold 3: 97.32%Fold 4375/375 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/stepAccuracy for fold 4: 97.04%Fold 5375/375 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/stepAccuracy for fold 5: 97.09%Average Accuracy Across 5 Folds: 97.24%

How to Use K-Fold Cross-Validation in a Neural Network

Similar Reads