bert4keras bert模型微调再训练保存；ckpt模型结构读取tensorboard展示

1、bert4keras bert模型微调再训练保存

参考：https://github.com/bojone/oppo-text-match/issues/5

## build_transformer_model为自定义bert模型层， keras.models.Model为创建整体keras模型
bert = build_transformer_model(
    config_path,
    checkpoint_path,
    with_pool='linear',
    application='unilm',
    keep_tokens=keep_tokens,  # 只保留keep_tokens中的字，精简原字表
    return_keras_model=False,
)

encoder = keras.models.Model(bert.model.inputs, bert.model.outputs[0])
seq2seq = keras.models.Model(bert.model.inputs, bert.model.outputs[1])

outputs = TotalLoss([2, 3])(bert.model.inputs + bert.model.outputs)
model = keras.models.Model(bert.model.inputs, outputs)

AdamW = extend_with_weight_decay(Adam, 'AdamW')
optimizer = AdamW(learning_rate=2e-6, weight_decay_rate=0.01)
model.compile(optimizer=optimizer)
model.summary()

## 保存训练的bert模型
bert = build_transformer_model(xxx, return_keras_model=False)
model1 = bert.model
model1.load_weights(xxxxxx)
bert.save_weights_as_checkpoint(xxxxx)

## 训练后直接保存
model.fit_generator(
        train_generator.forfit(),
        steps_per_epoch=steps_per_epoch,
        epochs=epochs,
        # callbacks=[evaluator]
    )
 
bert.save_weights_as_checkpoint("***.ckpt")

***完全重开时预训练参考：https://github.com/bojone/bert4keras/tree/master/pretraining

2、ckpt模型结构读取tensorboard展示

参考：https://blog.csdn.net/Mmagic1/article/details/106071818

import tensorflow as tf
from tensorflow.summary import FileWriter
 
sess = tf.Session()
tf.train.import_meta_graph("./model.ckpt.meta")
FileWriter("__tb", sess.graph)

生成：

在__tb文件夹中，按住shitf键，右键打开powershell；在powershell中输入：tensorboard --logdir=./ --host=127.0.0.15