Comments (1)
There were no documentations for the tf.contrib.layers.variance_scaling_initializer() when I wrote the code. Basically the variance scaling initializer is a more customizable version of Xavier. They are based on the same idea that adjusts the initialization with the input channels. They should perform similarly in resnet. (You may change the code and play with it.)
from resnet-in-tensorflow.
Related Issues (20)
- The result of the resnet-32
- _read_one_batch HOT 1
- Custom dataset
- Start working on resnet
- Whole validation accuracy using provided model_110.ckpt-79999 is extremely low HOT 1
- Why conv1 in first block is not followed by batch norm and relu
- Want to validate once before training.
- want to finetune the fc layer HOT 1
- Error "no such file or directory" while training the model using the uploaded checkpoint
- If I would like to get the ’Training curve‘ HOT 1
- set step of lr decay HOT 1
- train the model with gpu HOT 2
- Working on python 2.7 HOT 2
- all the input arrays must have same number of dimensions HOT 1
- Why does the validation error fluctuate so much? HOT 1
- The validation loss is so big?
- why i just get validation top1 error,but without train top1error and validation loss?thank you very much!
- DuplicateFlagError: The flag 'version' is defined twice. HOT 1
- UnrecognizedFlagError: Unknown command line flag 'f'
- What about results on cifar100
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from resnet-in-tensorflow.