Read only mode in keras

user_7576115 0
Viewed 43271

I have cloned human pose estimation keras model from this link human pose estimation keras

When I try to load the model on google colab, I get the following error

code

from keras.models import load_model
model = load_model('model.h5')

error

ValueError                                Traceback (most recent call 

last)
<ipython-input-29-bdcc7d8d338b> in <module>()
      1 from keras.models import load_model
----> 2 model = load_model('model.h5')

/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py in load_model(filepath, custom_objects, compile)
    417     f = h5dict(filepath, 'r')
    418     try:
--> 419         model = _deserialize_model(f, custom_objects, compile)
    420     finally:
    421         if opened_new_file:

/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py in _deserialize_model(f, custom_objects, compile)
    219         return obj
    220 
--> 221     model_config = f['model_config']
    222     if model_config is None:
    223         raise ValueError('No model found in config.')

/usr/local/lib/python3.6/dist-packages/keras/utils/io_utils.py in __getitem__(self, attr)
    300             else:
    301                 if self.read_only:
--> 302                     raise ValueError('Cannot create group in read only mode.')
    303                 val = H5Dict(self.data.create_group(attr))
    304         return val

ValueError: Cannot create group in read only mode.

Can someone please help me understand this read-only mode? How do I load this model?

keras tensorflow deep-learning python
5 Answers
user_21034310

I had a similar issue and solved this way

store the graph\architecture in JSON format and weights in h5 format 

import json

# lets assume `model` is main model 
model_json = model.to_json()
with open("model_in_json.json", "w") as json_file:
    json.dump(model_json, json_file)

model.save_weights("model_weights.h5")

then need to load model first to create graph\architecture and load_weights in model

from keras.models import load_model
from keras.models import model_from_json
import json

with open('model_in_json.json','r') as f:
    model_json = json.load(f)

model = model_from_json(model_json)
model.load_weights('model_weights.h5')
user_66527510

I used callbacks.ModelCheckpoint to save the weights and I had a similar error. I found out that there is a parameter called save_weights_only

If I set save_weights_only=True, then when I use load_model() to load the model in another process, it will raise the 'Cannot create group in read only mode.' error.

If I set save_weights_only=False(which is the default), then I can use load_model() to load the model and use it to do prediction, without compiling the model first.

user_43768390

you can use model.save(model_path+'Model.h5') and then keras.models.load_model(model_path+'Model.h5'), this way, you will not need to build and compile the model before load the weights, as model.save creates the architecture in dict inside the .h5 file.

user_111458200

Easy solution is you have to load the save model in the same way it is saved

for example you had created a model like this

# Build the model

model = Sequential()
model.add(Dense(100, input_shape=(10,)))
model.add(Activation('relu'))

model.add(Dense(10))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

#fit the model
model.fit(x_train, y_train,
                    batch_size=10,
                    epochs=5,
                    verbose=1,
                    validation_split=0.1)

#save model
model.save("model.h5")

so if you want to load this model for prediction you have to follow the same steps

from keras.models import load_model
# Build the model

model = Sequential()
model.add(Dense(100, input_shape=(10,)))
model.add(Activation('relu'))

model.add(Dense(10))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# load model
model.load_weights('model.h5')

prediction = model.predict( ''' data to predict ''')
user_17318230
Related