Answers.Body: <p><a href="https://i.sstatic.net/MAArI.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/MAArI.jpg" alt="enter image description here"></a></p>
  
  <p>In this case, each kernel has the same depth as the depth of the input <a href="https://en.wikipedia.org/wiki/Cuboid" rel="nofollow noreferrer">cuboid</a>. In the architecture above, we have an (input) cuboid of dimension <span class="math-container">$14 \times 14 \times 6$</span>, where <span class="math-container">$6$</span> is the depth, which is followed by an (output) cuboid of dimension <span class="math-container">$10 \times 10 \times 16$</span>, where <span class="math-container">$16$</span> is the depth, which is the number of (output) feature maps (or channels) and it is also equal to the number of kernels applied to the (input) cuboid of dimensions <span class="math-container">$14 \times 14 \times 6$</span>. Each of these <span class="math-container">$16$</span> kernels has a depth of <span class="math-container">$6$</span>. So, each of these <span class="math-container">$16$</span> kernels has a shape of <span class="math-container">$5\times 5 \times 6$</span>.</p>
  
  <p>In general, the depth of the input cuboid can be different than the depth of the output cuboid, which is often equal to the number of kernels applied to the input cuboid (but this is not always the case). Furthermore, the depth of <em>each</em> kernel (applied to the input cuboid) has (often) the same depth as the input cuboid.</p>
  
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Answers.CreationDate: 2019-05-17T13:25:35.710Z
Answers.Text: Source of the image: [https://harangdev.github.io/deep-learning/convolutional-neural-networks/25/](https://harangdev.github.io/deep-learning/convolutional-neural-networks/25/).
Answers.UserDisplayName: nbro
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Answers.LastActivityDate: 2019-05-17T13:40:47.743Z
Answers.LastEditDate: 2019-05-17T13:40:47.743Z
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Answers.Body: <p>The # of kernels will be the channel length, looking at the image you posted in your comment from <a href="https://medium.com/@shahariarrabby/lenet-5-alexnet-vgg-16-from-deeplearning-ai-2a4fa5f26344" rel="nofollow noreferrer">post</a>, I do not understand where you see the inconsistency.</p>
  
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Body: <p>In LeNet 5's first layer, the number of feature maps is equal to the number of kernels. However, the second convolutional layer has a depth different from the 3rd layer. Does the filter size dictate the number of feature maps?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-05-16T10:01:46.450Z
Comments.Text: Which LeNet5's picture are you looking at?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
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Comments.Text: https://medium.com/@shahariarrabby/lenet-5-alexnet-vgg-16-from-deeplearning-ai-2a4fa5f26344
  
  in short my doubt is this. does the filter size dictates the number of feature maps or the vice versa?
Comments.UserDisplayName: Stephen Philip
Tags: <deep-learning><convolutional-neural-networks><features>
Title: Is the number of feature maps equal to the number of kernels in the LeNet 5 architecture?
UserDisplayName: Stephen Philip
ThreadText: # Is the number of feature maps equal to the number of kernels in the LeNet 5 architecture?
  
  (Asked by: username_0 on 2019-05-16)
  
  In LeNet 5's first layer, the number of feature maps is equal to the number of kernels. However, the second convolutional layer has a depth different from the 3rd layer. Does the filter size dictate the number of feature maps?
  
  **Tags:** deep-learning, convolutional-neural-networks, features
  
  > **Comments:**
  > **username_1**: Which LeNet5's picture are you looking at?  
  > **username_0**: https://medium.com/@shahariarrabby/lenet-5-alexnet-vgg-16-from-deeplearning-ai-2a4fa5f26344  in short my doubt is this. does the filter size dictates the number of feature maps or the vice versa?  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2019-05-17. Score: 1 | Accepted answer
  
  [![enter image description here](https://i.sstatic.net/MAArI.jpg)](https://i.sstatic.net/MAArI.jpg)
  
  In this case, each kernel has the same depth as the depth of the input [cuboid](https://en.wikipedia.org/wiki/Cuboid). In the architecture above, we have an (input) cuboid of dimension $14 \times 14 \times 6$, where $6$ is the depth, which is followed by an (output) cuboid of dimension $10 \times 10 \times 16$, where $16$ is the depth, which is the number of (output) feature maps (or channels) and it is also equal to the number of kernels applied to the (input) cuboid of dimensions $14 \times 14 \times 6$. Each of these $16$ kernels has a depth of $6$. So, each of these $16$ kernels has a shape of $5\times 5 \times 6$.
  
  In general, the depth of the input cuboid can be different than the depth of the output cuboid, which is often equal to the number of kernels applied to the input cuboid (but this is not always the case). Furthermore, the depth of *each* kernel (applied to the input cuboid) has (often) the same depth as the input cuboid.
  
  > **Comments:**
  > **username_1**: Source of the image: [https://harangdev.github.io/deep-learning/convolutional-neural-networks/25/](https://harangdev.github.io/deep-learning/convolutional-neural-networks/25/).  
  
  ---
  
  ### Answer by username_2 on 2019-05-17. Score: 0
  
  The # of kernels will be the channel length, looking at the image you posted in your comment from [post](https://medium.com/@shahariarrabby/lenet-5-alexnet-vgg-16-from-deeplearning-ai-2a4fa5f26344), I do not understand where you see the inconsistency.
  
  
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