Answers.Body: <p>Conventionally, CNN layers downsample over and over, which enables them to capture details at different levels of abstractions. Usually, it is observed that the initial layers do nothing more than detecting edges, or filtering color channels; the combinations of these edges are what we perceive as 'features'.</p>
  
  <p>If you reverse the order, you essentially are changing sampling modes down the line.
  CNNs detect by 'downsampling' the inputs and therefore 'extracting' features.</p>
  
  <p>It may not work as expected!</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-04-23T15:18:40.130Z
Answers.LastActivityDate: 2019-04-23T15:18:40.130Z
Answers.UserDisplayName: Karan Shah
Body: <p>Could changing the order of convolution layers in a CNN improve accuracy or training time?</p>
  
Tags: <neural-networks><machine-learning><deep-learning><convolutional-neural-networks>
Title: Does changing the order of the convolution layers in a CNN have any impact?
UserDisplayName: AMEL BENAIDA
ThreadText: # Does changing the order of the convolution layers in a CNN have any impact?
  
  (Asked by: username_0 on 2019-04-23)
  
  Could changing the order of convolution layers in a CNN improve accuracy or training time?
  
  **Tags:** neural-networks, machine-learning, deep-learning, convolutional-neural-networks
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-04-23. Score: 0 | Accepted answer
  
  Conventionally, CNN layers downsample over and over, which enables them to capture details at different levels of abstractions. Usually, it is observed that the initial layers do nothing more than detecting edges, or filtering color channels; the combinations of these edges are what we perceive as 'features'.
  
  If you reverse the order, you essentially are changing sampling modes down the line.
  CNNs detect by 'downsampling' the inputs and therefore 'extracting' features.
  
  It may not work as expected!
  
  
  ---
  
  