We're currently training various neural networks using Keras, which is ideal because it has a nice interface and is relatively easy to use, but we'd like to be able to apply them in our production environment.
Unfortunately the production environment is C++, so our plan is to:
- Use the TensorFlow backend to save the model to a protobuf
- Link our production code to TensorFlow, and then load in the protobuf
Unfortunately I don't know how to access the TensorFlow saving utilities from Keras, which normally saves to HDF5 and JSON. How do I save to protobuf?
In case you don't need to utilize a GPU in the environment you are deploying to, you could also use my library, called frugally-deep. It is available on GitHub and published under the MIT License: https://github.com/Dobiasd/frugally-deep
frugally-deep allows running forward passes on already-trained Keras models directly in C++ without the need to link against TensorFlow or any other backend.
This seems to be answered in "Keras as a simplified interface to TensorFlow: tutorial", posted on The Keras Blog by Francois Chollet.
In particular, section II, "Using Keras models with TensorFlow".
You can access TensorFlow backend by:
import keras.backend.tensorflow_backend as K
Then you can call any TensorFlow utility or function like:
K.tf.ConfigProto
Save your keras model as an HDF5 file.
You can then do the conversion with the following code:
from keras import backend as K
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import graph_io
weight_file_path = 'path to your keras model'
net_model = load_model(weight_file_path)
sess = K.get_session()
constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), 'name of the output tensor')
graph_io.write_graph(constant_graph, 'output_folder_path', 'output.pb', as_text=False)
print('saved the constant graph (ready for inference) at: ', osp.join('output_folder_path', 'output.pb'))
Here is my sample code which handles multiple input and multiple output cases:
https://github.com/amir-abdi/keras_to_tensorflow
Make sure you change the learning phase of keras backend to store proper values of the layers (like dropout or batch normalization). Here is a discussion about it.
