keras_to_tensorflow | keras models to tensorflow .pb format | Machine Learning library

I have trained model for semantic segmentation using this repo, got good results and tried to use this net in small library writen with tensorflow c API. I turned my keras model into protobuf file using this repo and run session using this code:

I'm new to tensorflow and I have one question, My project has two majors part, first written in NodeJs that train my model from dataset and save model to local storage, so I have two files:

1. model.json
2. wights.bin

The second part is written in c++, After couple of days I could build tensorflow with bazel and add it to my OpenCv project, so here is my question :

I want to train my model in NodeJs part and use these models in my C++ part. Is this possible ? also I saw tjs converter but it converts models to use in NodeJs not vice versa.

Update : After searching a lot I figured out that I should convert my models to protobuff file, but tfjs-Converter does not support this type of conversion and another point is that I want to use my model with opencv library.

Update 2 Finally I could change my model to .pb file, first I use tfjs_converter to convert to keras model(.h5 file) and after that use this python script to convert to .pb file and opencv can successfully load model. But I got this error in using model :

libc++abi.dylib: terminating with uncaught exception of type cv::Exception: OpenCV(4.1.0) /tmp/opencv-20190505-12101-14vk1fh/opencv-4.1.0/modules/dnn/src/dnn.cpp:524: error: (-2:Unspecified error) Can't create layer "flatten_Flatten1/Shape" of type "Shape" in function 'getLayerInstance'

Any help ?

thanks

I have a problem with making inference on a batchsize greater than 1 using the c++ tensorflow api. The network input planes are 8x8x13 and the output is a single float. When I try to infer on multiple samples as follows, the result is correct only for the first sample. I used keras2tensorflow tool for converting the graph to .pb format.

I am trying to implement a Faster-RCNN model for object detection written by Yinghan Xu. After I have trained and saved the model with model_all.save('filename.h5'), I am trying to freeze the Keras model as TensorFlow graph (as .pb) for inference using keras_to_tensorflow.py written by Amir Abdi. But when I try to convert it, I get a ValueError: Unknown layer: roipoolingconv due to a custom RoiPoolingConv layer:

I have created an image classifier in Keras, later I saved the model in pb format to use it in android.

However, in the python code, it can classify the image properly. But in android whatever image I gave as input the output is always the same .

This is how I have trained my model

I'm having differences of the outputs when comparing a model with its stored protobuf version (via this conversion script). For debugging I'm comparing both layers respectively. For the weights and the actual layer output during a test sequence I receive the identical outputs, thus I'm not sure how to access the hidden layers.

Here is how I load the layers

I am trying to run a Tensorflow model on my Android application, but the same trained model gives different results (wrong inference) compared to when it is run on Python on desktop.

The model is a simple sequential CNN to recognize characters, much like this number plate recognition network, minus the windowing, as my model has the characters already cropped into place.

I have:

• Model saved in protobuf (.pb) file - modeled and trained in Keras on Python/Linux + GPU
• The inference was tested on a different computer on pure Tensorflow, to make sure Keras was not the culprit. Here, the results were as expected.
• Tensorflow 1.3.0 is being used on Python and Android. Installed from PIP on Python and jcenter on Android.
• The results on Android do not resemble the expected outcome.
• The input is a 129*45 RGB image, so a 129*45*3 array, and the output is a 4*36 array (representing 4 characters from 0-9 and a-z).

I used this code to save the Keras model as a .pb file.

Python code, this works as expected: