python中CNN交叉验证

在Python中，可以使用Keras库来实现CNN交叉验证。以下是一个基本的CNN交叉验证示例：

首先，导入必要的库和数据集：

import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from sklearn.model_selection import KFold

# Load MNIST dataset
(X_train, y_train), (X_test, y_test) = mnist.load_data()

然后，定义CNN模型：

# Define CNN model
def create_model():
    model = Sequential()
    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
    model.add(MaxPooling2D((2, 2)))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))
    model.add(Flatten())
    model.add(Dense(64, activation='relu'))
    model.add(Dense(10, activation='softmax'))
    return model

model = create_model()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

接下来，定义交叉验证的参数和循环训练模型：

# Define cross-validation parameters
n_splits = 5
batch_size = 64
epochs = 10

# Perform cross-validation
kf = KFold(n_splits=n_splits, shuffle=True)
for train_idx, val_idx in kf.split(X_train):
    train_X, train_y = X_train[train_idx], y_train[train_idx]
    val_X, val_y = X_train[val_idx], y_train[val_idx]
    
    # Reshape data for CNN
    train_X = train_X.reshape(train_X.shape[0], 28, 28, 1)
    val_X = val_X.reshape(val_X.shape[0], 28, 28, 1)
    test_X = X_test.reshape(X_test.shape[0], 28, 28, 1)
    
    # Convert labels to one-hot encoding
    train_y = np.eye(10)[train_y]
    val_y = np.eye(10)[val_y]
    test_y = np.eye(10)[y_test]
    
    # Train model
    model.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_data=(val_X, val_y))
    
    # Evaluate model
    score = model.evaluate(test_X, test_y, verbose=0)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])

在每个交叉验证折叠中，我们将训练数据分成训练集和验证集，然后将它们输入模型进行训练。在每个折叠完成后，我们使用测试集来评估模型的性能。最终输出的是每个交叉验证折叠的测试准确率和损失。