将主数据目录拆分为训练/验证/测试集

Question

我正在研究 X 射线图像分类，我的数据存储在 1 个目录中，我需要将其分为训练集、验证集和测试集。我确实使用 ImagedDataGenerator 设法分离了训练集和验证集，但是在分离测试集时遇到了麻烦。这是我的代码。

import split

# Path
Images = 'data_processed_cropped_32'
data_set = os.path.join(r'C:\Users\320067835\Desktop\Thesis\Data\png', Images)

#split.ratio('data_processed_cropped_32', output="output", seed=1337, ratio=(0.8, 0.1,0.1))

# Image size
img_width = 32
img_height = 32

# Data augmentation
data_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1/255, horizontal_flip = True,
                                                            rotation_range = 0,validation_split=0.2)

train_set = data_gen.flow_from_directory(data_set, target_size = (img_width, img_height), color_mode = 'grayscale',
                                        class_mode = 'categorical', batch_size = 32, interpolation = 'nearest',
                                        subset ='training')

validation_set = data_gen.flow_from_directory(data_set, target_size= (img_width,img_height), color_mode='grayscale',
                                              batch_size=32, class_mode='categorical', interpolation= 'nearest',
                                              subset='validation')
# Build a model
cnn = Sequential()

cnn.add(keras.Input(shape = (32,32,1)))
cnn.add(Conv2D(16,(3,3), padding = 'same', activation = 'relu', input_shape= (img_width,img_height,1)))
cnn.add(MaxPooling2D(2,2))
cnn.add(Conv2D(32,(3,3), padding = 'same',activation = 'relu', input_shape= (img_width, img_height,1)))
cnn.add(MaxPooling2D(2,2))

cnn.add(Flatten())

cnn.add(Dense(units = 100, activation = 'relu'))
cnn.add(Dense(units = 50, activation = 'relu'))
cnn.add(Dense(units=23, activation = 'softmax'))
cnn.summary()
cnn.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

cnn.fit(train_set,validation_data = validation_set,epochs = 20)

我厌倦了使用拆分文件夹，但它没有用。我认为大多数情况下都没有正确使用它，因为我不知道在拆分数据后如何访问 3 个文件夹。 或者有没有其他方法可以拆分我的测试集？