使用ImageDataGenerator时的Keras分割列车测试集

Question

我有一个目录，其中包含图像的子文件夹（根据标签）。我想在Keras中使用ImageDataGenerator时将此数据拆分为训练集和测试集。虽然是模型。keras中的fit（）具有用于指定拆分的参数验证\u split，我找不到用于模型的相同参数。安装发电机（）。怎么做？

train_datagen = ImageDataGenerator(rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=32,
    class_mode='binary')

model.fit_generator(
    train_generator,
    samples_per_epoch=nb_train_samples,
    nb_epoch=nb_epoch,
    validation_data=??,
    nb_val_samples=nb_validation_samples)

我没有单独的验证数据目录，需要从训练数据中分离出来

Answer 1

如果我们在ImageDataGenerator中使用子集，那么相同的增强将应用于培训和验证。如果只想在培训集中应用增强功能，可以使用splitfolders软件包拆分文件夹，该软件包可以使用pip直接安装。

https://pypi.org/project/split-folders/

这将把数据集分为train、val和test目录，然后您可以为它们中的每一个创建单独的生成器。

Answer 2

例如，您有这样的文件夹

full_dataset
|--horse (40 images)
|--donkey (30 images)
|--cow ((50 images)
|--zebra (70 images)

第一条路

image_generator = ImageDataGenerator(rescale=1/255, validation_split=0.2)    

train_dataset = image_generator.flow_from_directory(batch_size=32,
                                                 directory='full_dataset',
                                                 shuffle=True,
                                                 target_size=(280, 280), 
                                                 subset="training",
                                                 class_mode='categorical')

validation_dataset = image_generator.flow_from_directory(batch_size=32,
                                                 directory='full_dataset',
                                                 shuffle=True,
                                                 target_size=(280, 280), 
                                                 subset="validation",
                                                 class_mode='categorical')

第二条路

import glob
horse = glob.glob('full_dataset/horse/*.*')
donkey = glob.glob('full_dataset/donkey/*.*')
cow = glob.glob('full_dataset/cow/*.*')
zebra = glob.glob('full_dataset/zebra/*.*')

data = []
labels = []

for i in horse:   
    image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
    target_size= (280,280))
    image=np.array(image)
    data.append(image)
    labels.append(0)
for i in donkey:   
    image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
    target_size= (280,280))
    image=np.array(image)
    data.append(image)
    labels.append(1)
for i in cow:   
    image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
    target_size= (280,280))
    image=np.array(image)
    data.append(image)
    labels.append(2)
for i in zebra:   
    image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
    target_size= (280,280))
    image=np.array(image)
    data.append(image)
    labels.append(3)

data = np.array(data)
labels = np.array(labels)

from sklearn.model_selection import train_test_split
X_train, X_test, ytrain, ytest = train_test_split(data, labels, test_size=0.2,
                                                random_state=42)

第一种方法的主要缺点是，你不能用来显示图片。如果你写validation_dataset[1]会出错。但如果我使用第一种方式：X_test[1]

Answer 3

Keras现在使用ImageDataGenerator从单个目录添加了序列/验证拆分：

train_datagen = ImageDataGenerator(rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    validation_split=0.2) # set validation split

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary',
    subset='training') # set as training data

validation_generator = train_datagen.flow_from_directory(
    train_data_dir, # same directory as training data
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='binary',
    subset='validation') # set as validation data

model.fit_generator(
    train_generator,
    steps_per_epoch = train_generator.samples // batch_size,
    validation_data = validation_generator, 
    validation_steps = validation_generator.samples // batch_size,
    epochs = nb_epochs)

https://keras.io/preprocessing/image/