Comments (2)
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Regarding bias - I was inherited my code from initial Caffe implementation - https://github.com/liuzhuang13/DenseNetCaffe/blob/master/make_densenet.py#L11 - and there are no bias in conv layers
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Regarding OOM. All variables that exist in this network received from
get_variable
method, so they are already shared. This network should work as it is, and have no any other optimization approaches. But it's up to you to create memory optimized version. You may provide pull request if you want. You can take inspiration from authors torch implementation. They are create customized densely connected layer.
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@ikhlestov Thanks a lot!!!
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Related Issues (20)
- can you provide your trained models? HOT 5
- SVHN normalization issue HOT 8
- How about performance for flower dataset? HOT 1
- The order of image normalization and augmentation seems to be the same as the original implementation? HOT 5
- Problem regarding the number of features generated at the initial convolution layer HOT 1
- requirements issue HOT 1
- How to use 2 gpus? HOT 1
- Problem about the running time. HOT 2
- l2 regularization for bias too.. is it necessary? HOT 1
- Train on custom dataset HOT 1
- Train on images on my desktop HOT 1
- Choose GPU device HOT 9
- ResourceExhaustedError: OOM when allocating tensor HOT 2
- Why did you remove the max pooling layer after the initial convolution? HOT 1
- 'by_chanels' normalization issue between different splits HOT 3
- Pre-trained weights HOT 1
- Is there some way to get the softmax (probability values) from the saved models using the repos code. HOT 1
- batch_norm in dense_net.py HOT 1
- moving mean/variance update HOT 6
- Error while running SVHN Dataset
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