December 2018
Intermediate to advanced
274 pages
7h 46m
English
Content preview from Machine Learning for Mobile
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Optimizing the model file
Once we have the frozen graph, we can further optimize the file for inference-only purposes by removing the parts of the graph that are only needed during training. According to the documentation, this includes:
- Removing training-only operations, such as checkpoint saving
- Stripping out parts of the graph that are never reached
- Removing debug operations, such as CheckNumerics
- Folding batch normalization ops into the pre-calculated weights
- Fusing common operations into unified versions
TensorFlow provides optimize_for_inference_lib in tensorflow.python.tools for this purpose:
# Optimize for inference input_graph_def = tf.GraphDef() with tf.gfile.Open(output_frozen_graph_name, "r") as f: data = f.read() input_graph_def.ParseFromString(data) ...
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