Answers.Body: <p>Each feature map (or kernel) is independent of each other. If you had <span class="math-container">$3$</span> of these filters, your output shape would be <span class="math-container">$(28, 28, 3)$</span> (given the appropriate amount of padding and stride) with a total of <span class="math-container">$75*3=225$</span> trainable weights.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-17T12:48:59.957Z
Answers.LastActivityDate: 2019-05-17T13:48:14.760Z
Answers.LastEditDate: 2019-05-17T13:48:14.760Z
Answers.UserDisplayName: mshlis
Body: <p>Assume I have an input of size <span class="math-container">$32 \times 32 \times 3$</span> and pass it to a convolution layer. Now, if my kernel size were to be <span class="math-container">$5 \times 5 \times 3$</span> and the depth of my convolution layer were to be 1, only one feature map would be produced for the image. Here, each <a href="https://ai.stackexchange.com/q/18785/2444">neuron</a> would have <span class="math-container">$5 \times 5 \times 3 = 75$</span> weights (+1 bias).</p>
  <p>If I wanted to calculate multiple feature maps in this layer, say 3, is each local section (in this example, <span class="math-container">$5 \times 5 \times 3$</span>) of the image looked on by three different <a href="https://ai.stackexchange.com/q/18785/2444">neurons</a> and each of their weights trained individually? And what would be the output volume of this layer?</p>
  
Tags: <convolutional-neural-networks><features><convolutional-layers><filters>
Title: If I wanted to calculate multiple feature maps in a convolutional layer, should the filters be trained individually?
UserDisplayName: Dishant Sheth
ThreadText: # If I wanted to calculate multiple feature maps in a convolutional layer, should the filters be trained individually?
  
  (Asked by: username_0 on 2019-05-17)
  
  Assume I have an input of size $32 \times 32 \times 3$ and pass it to a convolution layer. Now, if my kernel size were to be $5 \times 5 \times 3$ and the depth of my convolution layer were to be 1, only one feature map would be produced for the image. Here, each [neuron](https://ai.stackexchange.com/q/18785/2444) would have $5 \times 5 \times 3 = 75$ weights (+1 bias).
  
  If I wanted to calculate multiple feature maps in this layer, say 3, is each local section (in this example, $5 \times 5 \times 3$) of the image looked on by three different [neurons](https://ai.stackexchange.com/q/18785/2444) and each of their weights trained individually? And what would be the output volume of this layer?
  
  **Tags:** convolutional-neural-networks, features, convolutional-layers, filters
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-05-17. Score: 2 | Accepted answer
  
  Each feature map (or kernel) is independent of each other. If you had $3$ of these filters, your output shape would be $(28, 28, 3)$ (given the appropriate amount of padding and stride) with a total of $75\*3=225$ trainable weights.
  
  
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