Comments (1)
Hi!
Really this is default intention of the TensorFlow. If you want to use multi GPU training, the code itself should be adjusted like this one. Unfortunately, there was no reason to implement this when I was developing this model. If you want you may create fork and add all required modification and later even send pull request back/
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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
- 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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