1. Data preparation
The data I used in this experiment are pictures of five kinds of flowers. The real pictures are as follows:

5 kinds of flowers are simply labeled with 0-4 tags. Training a good network model requires a lot of data. The number of samples in this experiment is shown in the table below:

2. vgg16 network structure
The typical feature of vgg_16 is to use 33 size convolution kernel stack to achieve the effect of 55 and 7 * 7. The network structure is as follows:

3. Code implementation
This experiment uses the keras framework, and the whole code of the experiment is as follows:

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K

from keras.models import Sequential
from keras.layers import Input,Dense,Conv2D,MaxPooling2D,UpSampling2D,Dropout,Flatten 
from keras.layers import BatchNormalization,AveragePooling2D,concatenate  
from keras.layers import ZeroPadding2D,add
from keras.layers import Dropout, Activation
from keras.models import Model,load_model
from keras.utils.np_utils import to_categorical
from keras.callbacks import TensorBoard
from keras import optimizers, regularizers # Optimizer, regularization item
from keras.optimizers import SGD, Adam



# dimensions of our images.
img_width, img_height = 224, 224

train_data_dir = '/home/p18301116/vgg/traindata/'
validation_data_dir = '/home/p18301116/vgg/vaildationdata/'
nb_train_samples = 2520
nb_validation_samples = 174
epochs = 100
batch_size = 20

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)
model = Sequential()  
model.add(Conv2D(64,(3,3),strides=(1,1),input_shape=input_shape,padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(Conv2D(64,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(MaxPooling2D(pool_size=(2,2)))  
model.add(Conv2D(128,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(Conv2D(128,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(MaxPooling2D(pool_size=(2,2)))  
model.add(Conv2D(256,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(Conv2D(256,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(Conv2D(256,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(MaxPooling2D(pool_size=(2,2)))  
model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(MaxPooling2D(pool_size=(2,2)))  
model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu',kernel_initializer='uniform'))  
model.add(MaxPooling2D(pool_size=(2,2)))  
model.add(Flatten())  
model.add(Dense(4096,activation='relu'))  
model.add(Dropout(0.5))  
model.add(Dense(4096,activation='relu'))  
model.add(Dropout(0.5))  
model.add(Dense(8,activation='softmax'))  
model.compile(loss='categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])  
model.summary()  


# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical') #Multi class

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical') #Multi class

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

4. Experimental results