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I am getting the following error:

I am trying to use R keras to combine a built-in network architecture with a custom output layer. Specifically, I want a regression output for an architecture that was originally built for classification.

Here is a simple example of what I would want:

I Am Doing Implementation Of Yolo v3 for multi class object detection

yolo is algorithm based region proposal and the region proposal with max confidence treated as prediction of yolo for more you can read it about here

for this particular task i refer to this murtuza tutorial that guided me from scratch

now as the complex network architecture requires hours of training i prefer to use transfer learning as using pretrained network and weighs(parameters) both of this link you can find here
Architecture configuration:cfg
Network Parameters(weights):weights

i used here yolov3 tiny as i required higher frame rates to process a video but afterall it not giving promising result as the tutorial shows i don't where i am lacking but even changing the network cfg and weight file to the orignal yolov3(320)does not giving true result as i am getting all 5 spatial data as coordinate and confidence [cx,cy,h,w,confidence] but all 80 classes probality is still zero vector[0.0,0.0,0.0---0.0] even changing the video source and choosing another video resulting into zero vector which was in tutorial working fine

Implementation Code:

I'm using tensorflow 1.15.0.

I build my own model with tf.keras. But when I trying to save my model with Saver:

I tried to reinstall one of my old vue projects on my new computer (on Windows 10) with npm but I came across this error :

I just coded a model similar to this:

and this is the code: