如何在Analytics Zoo中包含目录结构

from zoo.pipeline.api.keras.models import Sequential
from zoo.pipeline.api.keras.layers import Dense
from zoo.pipeline.api.keras.optimizers import Adam

model = Sequential()

model.add(Dense(64, activation='relu', input_dim=100))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))

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

data_dir = "/path/to/data"
train_dir = data_dir + "/train"
val_dir = data_dir + "/val"
test_dir = data_dir + "/test"

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='categorical')

val_generator = val_datagen.flow_from_directory(
    val_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='categorical')

test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(150, 150),
    batch_size=32,
    class_mode='categorical')

model.fit_generator(
    train_generator,
    steps_per_epoch=2000,
    epochs=50,
    validation_data=val_generator,
    validation_steps=800)

score = model.evaluate_generator(test_generator, steps=800)
print("Test loss:", score[0])
print("Test accuracy:", score[1])