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View Code? Open in Web Editor NEWEfficient Architecture Search by Network Transformation, in AAAI 2018
License: MIT License
Efficient Architecture Search by Network Transformation, in AAAI 2018
License: MIT License
Hi,
I like this work and hope to replicate it, and wish to confirm my understanding regarding this repo:
It seems the repo only provides the trained models and a small example, and there are no visualization code for the curve. In order to replicate the training process in the paper(for example plain CNN), I would also have to implement myself based on this code. Am I right?
Hi,
In order to use the default database cifar, you need to add nargs="?" in the main.py as
parser.add_argument("dataset", nargs="?", type=str, default="cifar10")
Cheers,
I want to replicate the experiment on a single GPU desktop workstation.
Is it possible to run it without the server client configuration ?
hi,
there are some good performance in this work. I want to pre-trained my dataset to test this method and code. may I have the pre-trained code for image classification?
Hi,
Thank you for the excellent work and sharing it online. I tried to repeat the experiment but got bad luck.
I tried to use the net2net_cifar10.py to do architecture search and got an error in code/tf_network/expconfig/wider_config.py:
return self._std_layer.init["kernel"]
KeyError: 'kernel'
The _std_layer.init is initialized as an empty dict.
I used the top_net1/config as the starting point, changed the placeholder
folder names, and created my own server_config as:
[
{
"remote":"localhost",
"gpuid":0,
"executive":"RL4AS_NetTrans-master/code/main.py"
}
]
Then I ran it as
python3 net2net_cifar10.py
Do I miss something in the configuration?
Thank you!
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