这是我花了2分钟写出的代码，那位码神能看懂这写的什么，代码质量怎么样
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
from tensorflow.keras.applications.vgg16 import convert_variables_to_constants
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation, Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.callbacks import TensorBoard
def generate_video_pairs(num_videos, difficulty, classifier, videos_dir, save_dir):
# Generate training and validation sets
training_data = list()
validation_data = list()
for _ in range(num_videos):
training_videos = list()
validation_videos = list()
training_videos.append(list(classifier.predict(training_data))[0])
training_videos.append(list(classifier.predict(validation_data))[0])
if difficulty == 'Medium':
validation_videos.append(list(classifier.predict(validation_data))[1])
else:
validation_videos.append(list(classifier.predict(training_data))[0])
training_data.extend(training_videos)
validation_data.extend(validation_videos)
return training_data, validation_data
# Generate model data
num_videos = 10000
difficulty = 'Medium'
videos_dir = 'videos_dir'
save_dir = 'save_dir'
training_data, validation_data = generate_video_pairs(num_videos, difficulty, classifier, videos_dir, save_dir)
# Prepare image data generator
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