Comments (1)
ikhlestov
commented on August 27, 2024
Hi, that may be a case, but the main reason I've used batch norm from contrib layers is that I've implemented this network few years ago when no alternatives exist. I may try those layers later sometimes. Or you may change them and provide PR.
Last but not least - I've made this code just as an export from authors caffe code to the tensorflow. And they used usual batch normalization. So maybe we should consider using at least tf.nn.batch_normalization method to be on par with the original code.
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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
- moving mean/variance update HOT 6
- Error while running SVHN Dataset
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