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CondeseNet-182* on Cifar100 validation 1 error rate is 19.73% where in paper is 18.47% #28
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Hi there, You might need to set dropout rate to fully reproduce the results! |
Thanks for your replying, I will add the dropout rate and try again. |
@ShichenLiu Hi, I noticed that the dropout is placed before convolution layer, def forward(self, x):
self._check_drop()
x = self.norm(x)
x = self.relu(x)
if self.dropout_rate > 0:
x = self.drop(x)
### Masked output
weight = self.conv.weight * self.mask
return F.conv2d(x, weight, None, self.conv.stride,
self.conv.padding, self.conv.dilation, 1) |
After I add the dropout-rate-0.1, the final classification error rate is 19.2%, still higher than reported in the paper. |
Hi,
You could try larger dropout rate, e.g. 0.2, 0.3 and even 0.5.
…On Sat, Sep 14, 2019 at 8:58 PM lizhen ***@***.***> wrote:
After I add the dropout-rate-0.1, the final classification error rate is
19.2%, still higher than reported in the paper.
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Hi, I tried different dropout rate for condensenet-182 on cifar100, here is the result: |
Hi, I run CondenseNet-182* using command provided by issue 11 on Cfiar100
The result of the first run is 19.73%, the result in the second run is 19. 86%
The result in the paper is 18.47% (Table)
I just used all the default arguments in the code provide, do we need to make other changes?
Thanks in advance
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