Comments (5)
Yes, maybe answer should be a little bit different - keep prob will not be set to 1.0 tf.cond
will just not apply dropout in case of test mode. It will return unchanged _input
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No, error mean prediction accuracy. Percent of how many classes were labeled incorrect. For example if we have 100 images and error 7% - it means that 93 images were classified correct, and 7 images receive another class.
from vision_networks.
how about the keep_prob in your code? it is set to 0.8.
doesnt it needs to change to 1 while testing
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Yes, it should be set to 1.0, so on lines https://github.com/ikhlestov/vision_networks/blob/master/models/dense_net.py#L298-L302 it will be disable, if network in test mode.
from vision_networks.
yes i can see that but i dont find any line which sets keep_prob=1
self.keep_prob is always 0.8
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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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