<p>Very good question, I have heard it refered to all the 3 ways you described. I am not sure if there is a purely objective answer so I will answer with what I understand by "depth" generally:</p>

<ul>
<li>If speaking of model architecture: the number of layers of the model.</li>
<li>If speaking of CNN layers: the number of filters (not to be confused with the size of the kernel).</li>
<li>If speaking of RNN layers: the number of time steps (temporal "depth").</li>
</ul>

<p>On the other hand I have never heard about depth regarding to FC layers. As I said the term "depth" is very subjective depending on the speakers. It should not be this way, and maybe theoretically is not, but I speak from what I heard in my experience. </p>

CC BY-SA 4.0
2020-02-18T08:57:00.923Z
2020-02-18T08:57:00.923Z
JVGD
<p>Is it</p>

<ol>
<li>number of units in a layer </li>
<li>number of layers</li>
<li>overall complexity of the network (both 1 and 2)</li>
</ol>

CC BY-SA 4.0
2020-02-18T09:33:42.950Z
Usually it refers to the number of layers that are sequential (i.e. one after the other). However, it isn't used much like a quantity (e.g. this network has a depth of 8), rather a qualitative characteristic of the network (e.g. this network is deeper than the other).
Djib2011
<neural-networks>
In neural networks, what does the term depth generally mean?
Sharath
# In neural networks, what does the term depth generally mean?

(Asked by: username_0 on 2020-02-18)

Is it

1. number of units in a layer
2. number of layers
3. overall complexity of the network (both 1 and 2)

**Tags:** neural-networks

> **Comments:**
> **username_1**: Usually it refers to the number of layers that are sequential (i.e. one after the other). However, it isn't used much like a quantity (e.g. this network has a depth of 8), rather a qualitative characteristic of the network (e.g. this network is deeper than the other).  

---

## 1 answers

### Answer by username_2 on 2020-02-18. Score: 0 | Accepted answer

Very good question, I have heard it refered to all the 3 ways you described. I am not sure if there is a purely objective answer so I will answer with what I understand by "depth" generally:

* If speaking of model architecture: the number of layers of the model.
* If speaking of CNN layers: the number of filters (not to be confused with the size of the kernel).
* If speaking of RNN layers: the number of time steps (temporal "depth").

On the other hand I have never heard about depth regarding to FC layers. As I said the term "depth" is very subjective depending on the speakers. It should not be this way, and maybe theoretically is not, but I speak from what I heard in my experience.


---

