How to convert a retrained model to tflite format?

问题

I have retrained an image classifier model on MobileNet, I have these files.

Further, I used toco to compress the retrained model to convert the model to .lite format, but I need it in .tflite format. Is there anyway I can get to tflite format from existing files?

回答1:

You can rename the .lite model to .tflite and it should work just fine. Alternatively, with toco, you can rename the output as it is created :

toco \
  --input_file=tf_files/retrained_graph.pb \
  --output_file=tf_files/optimized_graph.lite \ //change this to tflite
  --input_format=TENSORFLOW_GRAPHDEF \
  --output_format=TFLITE \
  --input_shape=1,224,224,3 \
  --input_array=input \
  --output_array=final_result \
  --inference_type=FLOAT \
  --input_data_type=FLOAT

回答2:

In order to convert TensorFlow checkpoints and GraphDef to a TensorFlow Lite FlatBuffer:

1. Freeze the checkpoints and graph using freeze_graph.py
2. Convert the frozen graph to a TensorFlow Lite FlatBuffer using TOCO.

Your freeze_graph.py command will look similar to the following:

freeze_graph -- \
--input_graph=output_graph.pb \
--input_binary=true \
--input_checkpoint=checkpoint \
--output_graph=frozen_graph.pb \
--output_node_names= MobilenetV1/Predictions/Softmax

You can use either TocoConverter (Python API) or tflite_convert (command line tool) with your model. TocoConverter accepts a tf.Session, frozen graph def, SavedModel directory or a Keras model file. tflite_convert accepts the later three formats.

When using TOCO, specify the output_file parameter with a .tflite extension.

来源：https://stackoverflow.com/questions/51220978/how-to-convert-a-retrained-model-to-tflite-format