Extracting last layers of keras model as a submodel

This is not the nicest solution, but it works:

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten

def cnn():
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),
                     activation='relu',
                     input_shape=(28, 28, 1), name='l_01'))
    model.add(Conv2D(64, (3, 3), activation='relu', name='l_02'))
    model.add(MaxPooling2D(pool_size=(2, 2), name='l_03'))
    model.add(Dropout(0.25, name='l_04'))
    model.add(Flatten(name='l_05'))
    model.add(Dense(128, activation='relu', name='l_06'))
    model.add(Dropout(0.5, name='l_07'))
    model.add(Dense(10, activation='softmax', name='l_08'))
    return model

def predictor(input_shape):
    model = Sequential()
    model.add(Flatten(name='l_05', input_shape=(12, 12, 64)))
    model.add(Dense(128, activation='relu', name='l_06'))
    model.add(Dropout(0.5, name='l_07'))
    model.add(Dense(10, activation='softmax', name='l_08'))
    return model

cnn_model = cnn()
cnn_model.save('/tmp/cnn_model.h5')

predictor_model = predictor(cnn_model.output.shape)
predictor_model.load_weights('/tmp/cnn_model.h5', by_name=True)