如何在Kaggle内核(或使用TF2的Colab )中设置Tensorboard？(为了调整超参数)

import tensorflow as tf
from tensorboard.plugins import projector

# 创建Tensorboard回调函数
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir='./logs')

model.fit(x_train, y_train, epochs=10, callbacks=[tensorboard_callback])

model.save_weights('model_weights.h5')

!mkdir logs
!cp model_weights.h5 logs/

# 生成metadata.tsv文件
metadata_file = open('logs/metadata.tsv', 'w')
metadata_file.write('\t'.join(['Label']) + '\n')
for label in labels:
    metadata_file.write('\t'.join([str(label)]) + '\n')
metadata_file.close()

# 创建Tensorboard配置文件
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = 'dense_1/kernel:0'  # 替换为你想要可视化的张量名称
embedding.metadata_path = 'metadata.tsv'  # 如果有metadata.tsv文件，指定路径

# 保存Tensorboard配置文件
projector.visualize_embeddings(tensorboard_callback.log_dir, config)

# 启动Tensorboard服务器
%load_ext tensorboard
%tensorboard --logdir logs