Compatibility between keras and tf.keras models - keras

Question

I am interested in training a model in tf.keras and then loading it with keras. I know this is not highly-advised, but I am interested in using tf.keras to train the model because
tf.keras is easier to build input pipelines
I want to take advantage of the tf.dataset API
and I am interested in loading it with keras because
I want to use coreml to deploy the model to ios.
I want to use coremltools to convert my model to ios, and coreml tools only works with keras, not tf.keras.
I have run into a few road-blocks, because not all of the tf.keras layers can be loaded as keras layers. For instance, I've had no trouble with a simple DNN, since all of the Dense layer parameters are the same between tf.keras and keras. However, I have had trouble with RNN layers, because tf.keras has an argument time_major that keras does not have. My RNN layers have time_major=False, which is the same behavior as keras, but keras sequential layers do not have this argument.
My solution right now is to save the tf.keras model in a json file (for the model structure) and delete the parts of the layers that keras does not support, and also save an h5 file (for the weights), like so:
model = # model trained with tf.keras
# save json
model_json = model.to_json()
with open('path_to_model_json.json', 'w') as json_file:
json_ = json.loads(model_json)
layers = json_['config']['layers']
for layer in layers:
if layer['class_name'] == 'SimpleRNN':
del layer['config']['time_major']
json.dump(json_, json_file)
# save weights
model.save_weights('path_to_my_weights.h5')
Then, I use the coremlconverter tool to convert from keras to coreml, like so:
with CustomObjectScope({'GlorotUniform': glorot_uniform()}):
coreml_model = coremltools.converters.keras.convert(
model=('path_to_model_json','path_to_my_weights.h5'),
input_names=#inputs,
output_names=#outputs,
class_labels = #labels,
custom_conversion_functions = { "GlorotUniform": tf.keras.initializers.glorot_uniform
}
)
coreml_model.save('my_core_ml_model.mlmodel')
My solution appears to be working, but I am wondering if there is a better approach? Or, is there imminent danger in this approach? For instance, is there a better way to convert tf.keras models to coreml? Or is there a better way to convert tf.keras models to keras? Or is there a better approach that I haven't thought of?
Any advice on the matter would be greatly appreciated :)

Answer 1

Your approach seems good to me!
In the past, when I had to convert tf.keras model to keras model, I did following:
Train model in tf.keras
Save only the weights tf_model.save_weights("tf_model.hdf5")
Make Keras model architecture using all layers in keras (same as the tf keras one)
load weights by layer names in keras: keras_model.load_weights(by_name=True)
This seemed to work for me. Since, I was using out of box architecture (DenseNet169), I had to very less work to replicate tf.keras network to keras.