我使用tensorflow keras创建一个模型,并定义了一个回调以在每个时期后保存模型。它可以正常工作并以pb
格式保存模型,但是我无法再次将其加载到keras中,因为keras只接受h5
格式。
我有两个问题:
- 除了tensorflow服务之外,我如何将保存的模型加载到keras / tensorflow中?
- 如何在每个时期之后以
h5
格式保存keras模型?
我的回调并保存模型:
from tensorflow.keras.callbacks import ModelCheckpoint
cp_callback = ModelCheckpoint(filepath=checkpoint_path,save_freq= 'epoch',verbose=1 )
regressor.compile(optimizer = 'adam',loss = 'mean_squared_error')
regressor.fit(X_train,y_train,epochs = 10,batch_size = 32,callbacks=[cp_callback])
我保存的模型结构:
saved_trained_10_epochs
├── assets
├── saved_model.pb
└── variables
├── variables.data-00000-of-00001
└── variables.index
更新
我尝试如下使用latest_checkpoint
,但出现以下错误:
from tensorflow.train import latest_checkpoint
loaded_model = latest_checkpoint(checkpoint_path)
loaded_model.summary()
错误:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-57-76a8ebe4f259> in <module>
----> 1 loaded_model.summary()
AttributeError: 'NoneType' object has no attribute 'summary'
在重新创建模型之后:
loaded_regressor = Sequential()
loaded_regressor.add(LSTM(units = 180,return_sequences = True,input_shape = (X_train.shape[1],3)))
loaded_regressor.add(Dropout(0.2))
loaded_regressor.add(LSTM(units = 180,return_sequences = True))
loaded_regressor.add(Dropout(0.2))
loaded_regressor.add(LSTM(units = 180,return_sequences = True))
loaded_regressor.add(Dropout(0.2))
loaded_regressor.add(LSTM(units = 180))
loaded_regressor.add(Dropout(0.2))
loaded_regressor.add(Dense(units = 1))
loaded_regressor.compile(optimizer = 'adam',loss = 'mean_squared_error')
loaded_regressor.load_weights(latest_checkpoint(checkpoint_path))
错误:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-30-c344f1759d01> in <module>
22
23 loaded_regressor.compile(optimizer = 'adam',loss = 'mean_squared_error')
---> 24 loaded_regressor.load_weights(latest_checkpoint(checkpoint_path))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in load_weights(self,filepath,by_name)
160 raise ValueError('Load weights is not yet supported with TPUStrategy '
161 'with steps_per_run greater than 1.')
--> 162 return super(Model,self).load_weights(filepath,by_name)
163
164 @trackable.no_automatic_dependency_tracking
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in load_weights(self,by_name)
1375 format.
1376 """
-> 1377 if _is_hdf5_filepath(filepath):
1378 save_format = 'h5'
1379 else:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in _is_hdf5_filepath(filepath)
1670
1671 def _is_hdf5_filepath(filepath):
-> 1672 return (filepath.endswith('.h5') or filepath.endswith('.keras') or
1673 filepath.endswith('.hdf5'))
1674
AttributeError: 'NoneType' object has no attribute 'endswith'