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License: Other
MatConvNet Implementation for Deep Residual Networks
License: Other
Hi all.I have a question about the
opts.learningRate = [0.01ones(1,3) 0.1ones(1,80) 0.01ones(1,10) 0.001ones(1,20)] ;
As I known,if the learning rate is small,it is easy to overfit.So I think the
opts.learningRate = [0.01ones(1,3) 0.1ones(1,80) 0.01ones(1,20) 0.001ones(1,10)] ; is better.
val: epoch 01: 79/ 79: 26.5 (27.3) Hz error: 0.68100 objective: 1.80163
Error using checkArgsForHandleToPrint
Handle input argument contains nonhandle values.
Error in checkArgsForHandleToPrint
Error in print>LocalCreatePrintJob (line 216)
handles = checkArgsForHandleToPrint(0, varargin{:});
Error in print (line 38)
[pj, inputargs] = LocalCreatePrintJob(varargin{:});
Error in cnn_train_dag (line 142)
print(1, modelFigPath, '-dpdf') ;
Error in cnn_cifar (line 79)
[net, info] = cnn_train_dag(net, imdb, getBatch(opts), ...
any suggestions...
Hi Bazilas, thanks for sharing us the great work. May I know if any script used to predict a new image using the trained reset model is provided? Thanks
Hi,
Thank you for the implementation. This helps a lot.
Is there pretrained model with a mat file, for both cifar 10 and 100?
It will be good to save the computation cost.
Thank you.
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