keras中的多标签分类

我试图 Build 一个模型，帮助我识别多标签分类问题的图像，例如，如果我有猫，狗和奶牛的图片 . 我运行了一个CNN模型，但它根本没有捕获（精度为33％） . 任何人都可以分享一个有效的模型（即使准确性合理）？在此先感谢大家！ [附上我上面提到的代码]

from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, 
BatchNormalization
from keras.callbacks import LearningRateScheduler
from keras.optimizers import adam, SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.vgg16 import VGG16


# 2 - Create network layers
image_width = 200
image_height = 200

model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(3,3), 
activation='relu',input_shape=( 
(image_width,image_height,3)))
model.add(BatchNormalization())
model.add(Conv2D(filters=16, kernel_size=(3,3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPool2D(strides=(2,2)))
model.add(Dropout(0.25))
# Stage II = make it more compex with 'filters = 32'
model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPool2D(strides=(2,2)))

model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))


# We'll Randomize the training set (shuffle), to avoid overfitting 
(augmentation)
datagen = ImageDataGenerator(zoom_range = 0.1,
                        height_shift_range = 0.1,
                        width_shift_range = 0.1,
                        rotation_range = 10)

model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=

['准确性']）

# automatically retrieve images and their classes for train and validation 
train_generator = datagen.flow_from_directory(
    train_dataset,
    target_size=(image_width, image_height),
    batch_size=32,
    class_mode='categorical')


validation_generator = datagen.flow_from_directory(
    validation_dataset,
    target_size=(image_width, image_height),
    batch_size=32,
    class_mode='categorical')

# Now let's fit the model on the validation set

model.fit_generator(
    train_generator,
    steps_per_epoch=50,
    epochs=500,
    validation_data=validation_generator,
    validation_steps=15)