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tensorflowjs_convertor fails during inverse transformation

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tensorflow==1.8.0 Keras==2.1.4 @tensorflow/tfjs": “0.12.0” Google Chrome, Version 67.0.3396.99 (Official Build) (64-bit)

I’ve tried to convert yolo model to tensorflow.js and used keras-yolo3 script which build keras model from darknet cfg and weight files. And It works pretty well with keras. Then I convert [.h5] model to tensorflow.js using tenserflowjs_converter and got a strange error:

Error: A Concatenate layer requires inputs with matching shapes except for the concat axis. Got input shapes: [[null,0,0,128],[null,null,null,256]]

Seems that tfjs_converter set 0 shapes to UpSampling2D layer. I also investigated that tfjs_converters fails during inverse transformation with:

tensorflow.python.framework.errors_impl.InvalidArgumentError: Shapes must be equal rank, but are 1 and 4 for ‘Assign_3’ (op: ‘Assign’) with input shapes: [16], [3,3,16,32].

I think It should be some consistency between this two processes.

To reproduce download yolo-tiny.cfg and yolo-tiny.weight :

1. git clone https://github.com/qqwweee/keras-yolo3
2. Run python convert.py yolo-tiny.cfg yolo-tiny.weights model_data/yolo-tiny.h5
3. tensorflowjs_converter --input_format=keras --output_format=tensorflowjs model_data/yolo-tiny.h5 model_data/tfjs
4. You could try load model with tfjs and you'll get my first error...
5. tensorflowjs_converter --input_format=tensorflowjs --output_format=keras model_data/tfjs/model.json model_data/smth.h5