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I've implemented an home-brewed ZFNet (prototxt) for my research. After 20k iterations with the definition, the test accuracy stays at ~0.001 (i.e., 1/1000), the test loss at ~6.9, and training loss at ~6.9, which seems that the net keeps playing guessing games among the 1k classes. I've thoroughly checked the whole definition and tried to change some of the hyper-parameters to start a new training, but of no avail, same results' shown on the screen....
Could anyone show me some light? Thanks in advance!
The hyper-parameters in the prototxt are derived from the paper [1]. All the inputs and outputs of the layers seems correct as Fig. 3 in the paper suggests.
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Hi,
I've implemented an home-brewed ZFNet (prototxt) for my research. After 20k iterations with the definition, the test accuracy stays at ~0.001 (i.e., 1/1000), the test loss at ~6.9, and training loss at ~6.9, which seems that the net keeps playing guessing games among the 1k classes. I've thoroughly checked the whole definition and tried to change some of the hyper-parameters to start a new training, but of no avail, same results' shown on the screen....
Could anyone show me some light? Thanks in advance!
The hyper-parameters in the prototxt are derived from the paper [1]. All the inputs and outputs of the layers seems correct as Fig. 3 in the paper suggests.
The tweaks are:
crop
-s of the input for both training and testing are set to225
instead of224
as discussed in Implement the model that won the classification task of ImageNet 2013 #33;conv3
,conv4
, andconv5
to make the sizes of the blobs consistent [1];constant
in [1] togaussian
withstd: 0.01
;weight_decay
: changing from0.0005
to0.00025
as suggested by @sergeyk in PR Implement the model that won the classification task of ImageNet 2013 #33;[1] Zeiler, M. and Fergus, R. Visualizing and Understanding Convolutional Networks, ECCV 2014.
Related Issue: #32, and PR: #33.
P.S.: For the poor part of the log, plz refer to here.
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