Question

由于神经网络的运行时间很长，似乎没有认真对待网络中的k折交叉验证。我有一个小数据集，我有兴趣使用给定here的示例进行k折交叉验证。可能吗？感谢。

Answer 1

如果您正在使用带有数据生成器的图像，这里是使用Keras和scikit-learn进行10倍交叉验证的一种方法。策略是根据每个折叠将文件复制到training，validation和test子文件夹。

import numpy as np
import os
import pandas as pd
import shutil
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# used to copy files according to each fold
def copy_images(df, directory):
    destination_directory = "{path to your data directory}/" + directory
    print("copying {} files to {}...".format(directory, destination_directory))

    # remove all files from previous fold
    if os.path.exists(destination_directory):
        shutil.rmtree(destination_directory)

    # create folder for files from this fold
    if not os.path.exists(destination_directory):
        os.makedirs(destination_directory)

    # create subfolders for each class
    for c in set(list(df['class'])):
        if not os.path.exists(destination_directory + '/' + c):
            os.makedirs(destination_directory + '/' + c)

    # copy files for this fold from a directory holding all the files
    for i, row in df.iterrows():
        try:
            # this is the path to all of your images kept together in a separate folder
            path_from = "{path to all of your images}"
            path_from = path_from + "{}.jpg"
            path_to = "{}/{}".format(destination_directory, row['class'])

            # move from folder keeping all files to training, test, or validation folder (the "directory" argument)
            shutil.copy(path_from.format(row['filename']), path_to)
        except Exception, e:
            print("Error when copying {}: {}".format(row['filename'], str(e)))

# dataframe containing the filenames of the images (e.g., GUID filenames) and the classes
df = pd.read_csv('{path to your data}.csv')
df_y = df['class']
df_x = df
del df_x['class']

skf = StratifiedKFold(n_splits = 10)
total_actual = []
total_predicted = []
total_val_accuracy = []
total_val_loss = []
total_test_accuracy = []

for i, (train_index, test_index) in enumerate(skf.split(df_x, df_y)):
    x_train, x_test = df_x.iloc[train_index], df_x.iloc[test_index]
    y_train, y_test = df_y.iloc[train_index], df_y.iloc[test_index]

    train = pd.concat([x_train, y_train], axis=1)
    test = pd.concat([x_test, y_test], axis = 1)

    # take 20% of the training data from this fold for validation during training
    validation = train.sample(frac = 0.2)

    # make sure validation data does not include training data
    train = train[~train['filename'].isin(list(validation['filename']))]

    # copy the images according to the fold
    copy_images(train, 'training')
    copy_images(validation, 'validation')
    copy_images(test, 'test')

    print('**** Running fold '+ str(i))

    # here you call a function to create and train your model, returning validation accuracy and validation loss
    val_accuracy, val_loss = create_train_model();

    # append validation accuracy and loss for average calculation later on
    total_val_accuracy.append(val_accuracy)
    total_val_loss.append(val_loss)

    # here you will call a predict() method that will predict the images on the "test" subfolder 
    # this function returns the actual classes and the predicted classes in the same order
    actual, predicted = predict()

    # append accuracy from the predictions on the test data
    total_test_accuracy.append(accuracy_score(actual, predicted))

    # append all of the actual and predicted classes for your final evaluation
    total_actual = total_actual + actual
    total_predicted = total_predicted + predicted

    # this is optional, but you can also see the performance on each fold as the process goes on
    print(classification_report(total_actual, total_predicted))
    print(confusion_matrix(total_actual, total_predicted))

print(classification_report(total_actual, total_predicted))
print(confusion_matrix(total_actual, total_predicted))
print("Validation accuracy on each fold:")
print(total_val_accuracy)
print("Mean validation accuracy: {}%".format(np.mean(total_val_accuracy) * 100))

print("Validation loss on each fold:")
print(total_val_loss)
print("Mean validation loss: {}".format(np.mean(total_val_loss)))

print("Test accuracy on each fold:")
print(total_test_accuracy)
print("Mean test accuracy: {}%".format(np.mean(total_test_accuracy) * 100))

在您的predict（）函数中，如果您使用的是数据生成器，那么我在测试时使用batch_size 1来保持预测的相同顺序的唯一方法是：

generator = ImageDataGenerator().flow_from_directory(
        '{path to your data directory}/test',
        target_size = (img_width, img_height),
        batch_size = 1,
        color_mode = 'rgb',
        # categorical for a multiclass problem
        class_mode = 'categorical',
        # this will also ensure the same order
        shuffle = False)

使用此代码，我能够使用数据生成器进行10倍交叉验证（因此我不必将所有文件保留在内存中）。如果您有数百万张图片，这可能会很多工作，如果您的测试集很大，batch_size = 1可能会成为瓶颈，但对于我的项目来说这很有效。

K使用keras折叠交叉验证

1 个答案: