This document provides instructions for converting a TensorFlow model to a Core ML model using the tf-coreml converter library. It outlines the steps to export the TensorFlow model to a .pb file, get the model input and output details, install and use tf-coreml to convert the file to a .mlmodel, and notes some limitations of the conversion process. It also provides references for using pre-trained models with Core ML on iOS.

1. Tensor Flow to Core
ML model
Abby Lai
2019/08/19

2. How to convert TensorFlow
to Core ML?
1. Use Tensor Flow to export
fi
le to xxx.pb
2. Get model Input/Output
3. Use tf-coreml to convert and get .mlmodel
fi
le
4. Import to Xcode project

3. – TensorFlow (TF) to CoreML Converter
https://github.com/tf-coreml/tf-coreml
tf-coreml

4. Install tf-coreml
• Clone
• $ git clone https://github.com/tf-coreml/tf-coreml.git
• With pip
• $ pip install tensor
fl
ow —user
• $ pip install coremltools —user
• $ pip install -e .
• Without pip
• $ sudo python setup.py bdist_wheel
• Make sure you installed coremltools, then
$ pip install -U tfcoreml==0.3.0 —user
Version 0.3.0 is suitable, 0.4.0 will get some error.
Tf-coreml Dependencies
tensor
fl
ow >= 1.5.0
coremltools >= 0.8
numpy >= 1.6.2
protobuf >= 3.1.0
six >= 1.10.0

5. If you don’t know the input/out of model, you can get the
information from .pb
Get Model Input/Output

6. Get Input/Output from .pb
• $cd tf-coreml
• $cd utils
• $python inspect_pb.py inception.pb(.pb name)
inceptionInfo.text(info
fi
le name)
• You will get inceptionInfo.text in the same folder
• Sample
• https://blog.csdn.net/weixin_41713230/article/details/
84146087
• https://www.jianshu.com/p/80d930394282

7. inceptionInfo.text
• Find
• input:0
• Softmax2:0

8. Convert

9. Run python convert.py
import tfcoreml as tf_converter
import tensor
fl
ow as tf
from tensor
fl
ow.python.framework import ops
ops.reset_default_graph()
coreml_model =
tf_converter.convert(tf_model_path="tensor
fl
ow_inception_graph.pb",
mlmodel_path='inception.mlmodel',
output_feature_names=['softmax2:0'], image_input_names=
['image'], input_name_shape_dict={'input:0': [1, 416, 416, 3]},
is_bgr=True, image_scale=1/255.0)

10. Limitation
• Run
• coreml_model = tf_converter.convert(tf_model_path="deeplab.pb",
mlmodel_path='deeplab.mlmodel',
output_feature_names=['SemanticPredictions:0'],
input_name_shape_dict={'ImageTensor:0':[1, 512, 512, 3]})
• Result
• NotImplementedError: Unsupported Ops of type: Shape,Pack
• Some layer is not support
• Custom layer - Convert Your Network to Core ML
https://developer.apple.com/documentation/coreml/core_ml_api/
creating_a_custom_layer

11. Reference
• facial landmark localization by deep learning port to ios with
coreml
• 机器学习Tensor
fl
ow笔记4：iOS通过Core ML使⽤Tensor
fl
ow训
练模型
• 把 Darknet 模型轉換成 CoreML 模型
• Download Pre-trained Models (.pb)
• Deeplab
• Inception