Strange problem regarding model save_weights

I’m running keras on a remoter server with SSH, training LSTM, the training process works correctly, the model is like this:

model = Sequential()
model.add(Masking(mask_value=0., input_shape=(None, len(chars))))
model.add(LSTM(4000, return_sequences=True))
model.add(LSTM(4000, return_sequences=True))
model.add(TimeDistributed(Dense(len(chars))))
model.add(Activation('softmax'))

rmsprop=RMSprop(lr=0.006)
model.compile(loss='categorical_crossentropy', optimizer=rmsprop)

after I training, I saved the model:

json_string = model.to_json()    
open('my_model_architecture21.json', 'w').write(json_string)
model.save_weights('my_model_weights21.h5',overwrite=True)

but this part works abnormally, as when I reload weights after training, the model works like never been trained, the same script works correctly in my one laptop. this is really strange, anyone have an idea? thanks very much!

[HanyEjust]

why you don not use these lines of code:

1- save the model: model.save(path+‘\name.hdf5’)

2- load the model: from keras.models import load_model

model=load_model(path+‘\name.hdf5’)

On Wed, Jan 4, 2017 at 9:28 AM, MrWanter [e-mail] wrote:

The problem has been solved with changing the version of pythonand keras, previously I’m using python3andkeras 1.2.0, after reinstall python2 and keras 1.1.0, it works correctly.

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[dipanjannag]

Facing the same issue #4875 But for me it is working fine if it is in same session. Even model.save() doesn’t seem to be working in this matter.