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如题,我该如何将模型保存为RoBERTa-wwm那样包含index,meta和data三个文件的checkpoint格式呢?
目前尝试过save_weights和tf.keras.callbacks.ModelCheckpoint,但是最终都只会输出一个文件。
使用tf.keras.callbacks.ModelCheckpoint保存模型的代码如下:
checkpoint_path = "models/cp-{epoch:04d}.ckpt" checkpoint_dir = os.path.dirname(checkpoint_path) # Create a callback that saves the model's weights cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=False, verbose=1) model.fit_generator( DataGenerator(train_data_path,batch_size), epochs=epochs, callbacks=[evaluator, cp_callback], verbose = 1 )
最终只生成一个ckpt文件。
-------------------------问题2------------------- 还有一个很困惑的问题,模型使用的是Bert4Keras,那么创建出来的模型应该是bert4keras.bert.BertModel类型,但是我将最终模型的类型打印出来: print(type(model)) 输出为: <class 'keras.engine.training.Model'> 十分不解,恳请指教!
print(type(model))
<class 'keras.engine.training.Model'>
The text was updated successfully, but these errors were encountered:
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如题,我该如何将模型保存为RoBERTa-wwm那样包含index,meta和data三个文件的checkpoint格式呢?
目前尝试过save_weights和tf.keras.callbacks.ModelCheckpoint,但是最终都只会输出一个文件。
使用tf.keras.callbacks.ModelCheckpoint保存模型的代码如下:
最终只生成一个ckpt文件。
-------------------------问题2-------------------
还有一个很困惑的问题,模型使用的是Bert4Keras,那么创建出来的模型应该是bert4keras.bert.BertModel类型,但是我将最终模型的类型打印出来:
print(type(model))
输出为:
<class 'keras.engine.training.Model'>
十分不解,恳请指教!
The text was updated successfully, but these errors were encountered: