io for neural networks
# IONN io-operations for artificial neural networks
IONN provides functionality to store, load and freeze neural networks and convert networks between different neural network frameworks. The current version provides
- Storing, loading, freezing of tensorflow models in google protobuf files (submodule tfpb)
- Dumping keras models as google protobuf files and loading them into a pure tensorflow environment (submodule k2tf)
## tfpb - freeze and store graphs
Tensorflow provides a graph freezing tool that works ok, but is hardly documented and not particularly modular. The tfpb module provides a simplified interface to storing frozen graphs. There are two main entrypoints, load_protobuf and save_protobuf. Furthermore, you can directly call tfpb to freeze stored graphs like this
tf-freeze <input_graph_file_name> <output_file_name> <checkpoint_file_name>
## k2tf - From keras to tensorflow
Keras is nice if we want to quickly draft out a neural network architecture. Unfortunately, it differs considerably in how it stores models and can therefore not well co-exist with tensorflow infrastructure. k2tf supports storing keras models in tensorflow protobuf files that can later be loaded without keras. There are currently two drawbacks though:
- Models have to be frozen, which isn’t exactly desirable because most of tensorflow’s strength is in tweaking the models during the learning phase.
- Models have to be reloaded in a separate process to avoid confusion about the tensorflow graph.
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