Answers.Body: <p>After the first Conv layer, the size reduced to 20x20
  for the primary caps which is convolutional caps layer
  n final = (n + 2p -f )/s + 1
  which gives a 6x6 output with 256 channels</p>
  
  <p>6x6x256 is further encoded into capsules of 8 dimensions by reshaping the channels 
  i.e. 256/8 = 32
  which gives 6x6x32 = 1152 capsules</p>
  
  <p>try experimenting with the same hyperparameters first and then try to encode higher-level features by making suitable changes to the hyperparameters. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-08-13T17:25:38.693Z
Answers.LastActivityDate: 2019-08-13T17:25:38.693Z
Answers.UserDisplayName: Bhavya Shah
Body: <p>I want to experiment with capsule networks on facial expression recognition (FER). For now, I am using fer2013 Kaggle dataset.</p>
  
  <p>One thing that I didn't understand in capsule networks was in the first convolution layer, the size was reduced to 20x20 - having input image as 28x28 and filters as 9x9 with 1 stride. But, in the capsules, the size reduces to 6x6. </p>
  
  <p>How did this happen? Because with the input size as 20x20 and filters as 9x9 and 2 strides, I couldn't get 6x6. Maybe I missed something.</p>
  
  <p>For my experiment, the input size image is 48x48. Should I use the same hyperparameters for the start or are there any suggested hyperparameters that I can use?</p>
  
Tags: <neural-networks><machine-learning><deep-learning><convolutional-neural-networks><capsule-neural-network>
Title: Why does the size reduce to $6 \times 6$ in the capsule networks?
UserDisplayName: nirvair
ThreadText: # Why does the size reduce to $6 \times 6$ in the capsule networks?
  
  (Asked by: username_0 on 2018-05-14)
  
  I want to experiment with capsule networks on facial expression recognition (FER). For now, I am using fer2013 Kaggle dataset.
  
  One thing that I didn't understand in capsule networks was in the first convolution layer, the size was reduced to 20x20 - having input image as 28x28 and filters as 9x9 with 1 stride. But, in the capsules, the size reduces to 6x6.
  
  How did this happen? Because with the input size as 20x20 and filters as 9x9 and 2 strides, I couldn't get 6x6. Maybe I missed something.
  
  For my experiment, the input size image is 48x48. Should I use the same hyperparameters for the start or are there any suggested hyperparameters that I can use?
  
  **Tags:** neural-networks, machine-learning, deep-learning, convolutional-neural-networks, capsule-neural-network
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-08-13. Score: 1
  
  After the first Conv layer, the size reduced to 20x20
  for the primary caps which is convolutional caps layer
  n final = (n + 2p -f )/s + 1
  which gives a 6x6 output with 256 channels
  
  6x6x256 is further encoded into capsules of 8 dimensions by reshaping the channels
  i.e. 256/8 = 32
  which gives 6x6x32 = 1152 capsules
  
  try experimenting with the same hyperparameters first and then try to encode higher-level features by making suitable changes to the hyperparameters.
  
  
  ---
  
  