Before you begin

This tutorial assumes you already have a TensorFlow .pb model file using 32-bit floating point weights. If your model is in a different format

  • Keras
  • PyTorch
  • Caffe
  • MxNet
  • CNTK

If you want to deploy it using TensorFlow then you will need to use a tool to convert it to the TensorFlow format first.

There are various projects and resources building up around converting model formats. Both, MMdnn and Deep learning model converter are useful resources, and the ONNX format has potential to vastly simplify this in the future.

The most important preparation that you can do is to ensure that the size and complexity of your trained model is suitable for the device that you intend to run it on. To ensure this:

  • If you are using a pre-trained model for feature extraction or transfer learning, then you should consider using mobile-optimized versions such as MobileNet, TinyYolo, and so on.
  • If you designed the architecture yourself, then you should consider adapting the architecture for faster execution and smaller size. An example of this is using depth-separable convolutions where possible, as in MobileNet. This provides better performance and accuracy improvements than by post-processing the model file after training.

Please follow TensorFlow documentation to install Bazel and other dependencies, and download the TensorFlow source code. At the root of your TensorFlow source tree, run ./configure to configure the system build.

This tutorial uses TensorFlow's graph_transforms tool, which is built from the TensorFlow source with this command:

bazel build tensorflow/tools/graph_transforms:transform_graph

For more details on how to install and build TensorFlow, see the TensorFlow documentation.

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