Hi all,
I have a .json and a .params file of a pre-trained model which I want to use.
For demonstration let's assume the network consists of named-layers and looks
like this:
**A -> B -> C -> D -> E**
My goal is to split the network in two parts.
The first part should be **A -> B -> C** and the second part should be **C -> D
-> E**.
This means, that the output of the first part is C after activation functions
of B -> C are applied.
The input for the second part is the direct output of layer C but before
weights of C -> D and activation functions are applied.
It works to get the first part, as I can specify C as the ouput layer of the
network.
But I can not achieve to create the second part of the network, as creating the
symbol
> E = sym.get_internals()['E']
> second_network = mx.gluon.nn.SymbolBlock(outputs=E, inputs=[mx.sym.var('C')])
> second_network.collect_params().load(params_file, ignore_extra=True)
does not work. It throws an error that the input needs to be bound to data, I
can not set C to the input.
Any help getting this to work is appreciated.
Please note, that simply setting the output of the network to **[C, E]** is not
sufficient. I need to get C first and then put it into the second network.
Thank you very much.
Best,
Jan
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