[GitHub] tz-hmc opened a new issue #8591: How do I make a siamese network with pretrained models (esp. keeping the weights the same?)

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tz-hmc opened a new issue #8591: How do I make a siamese network with pretrained models (esp. keeping the weights the same?)
URL: https://github.com/apache/incubator-mxnet/issues/8591
 
 
   ## Description
   How do I ensure the weights are kept the same? Can I unpack the internal layers somehow and set the weights of each to the same variable? My apologies, I'm new to MXNet. Would really appreciate the help, thanks!
   
   ``
   sym1, arg_params1, aux_params1 = mx.model.load_checkpoint('resnet-152', 0)
   sym2, arg_params2, aux_params2 = mx.model.load_checkpoint('resnet-152', 0)
   layer1 = sym1.get_internals()
   layer2 = sym2.get_internals()
   for i in range(len(layer1)): # will something like this work?
       arg_params1[i] = arg_params2[i]
   ``
   
   Relevant answers, but not specific enough to my particular problem:
   https://github.com/apache/incubator-mxnet/issues/772    siamese networks
   https://github.com/apache/incubator-mxnet/issues/6791     extract layers as variables
   https://github.com/apache/incubator-mxnet/issues/557     set weights to be same

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