Comments (3)
CapsNet is said to perform better for scaled images does not mean it can perform well without training. In my opinion, it just has the ability of "finding " it is a scaled picture of original picture, so it is possible if you train capsnet on image with rotation angle 0, pi/2, pi than you test it on pi/3, it will perform well (pi is 3.1415...)
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Kumar — where’s your code? What did you do? 10% accuracy means you’ve potentially also messed up somewhere in your code.
from capsnet-tensorflow.
i trained the network with a training set...
i tested the network with test_data_original to get nearly 99% accuracy..
i rotated the test_data_original with 0.5degree and 1degree
to get another test_data_rotated0p5 and test_data_rotated1
i tested the network against test_data_rotated0p5 and test_data_rotated1 to get the test accuracies around 10%.....
I cloned this repo and feed my datasets..
no clue whats going over here
from capsnet-tensorflow.
Related Issues (20)
- reshape question
- a question HOT 1
- Question about num_outputs
- Train on different dataset HOT 7
- Using Caps-Net to Handle High-dimensional and Small-sample Problems HOT 1
- I have question about loss function. something wrong, i think....? HOT 3
- Baidu Link Not Working Correctly: Mirror?
- Cannot handle rotation ??? HOT 1
- English Version of Blog HOT 1
- Medical Imaging HOT 7
- Vector Length HOT 3
- About the routing biases HOT 2
- TypeError: 'numpy.ndarray' object is not callable!
- a quesion HOT 1
- about tensorflow c++
- about
- about a problem in gpu_version
- How to save model in SavedModel or frozen_graph format?
- Cap
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