<p>I am currently designing a Model which takes Input 3D Grid and Model Output at <span class="math-container">$t-1$</span>. The model figure is described below</p>
<p><a href="https://i.sstatic.net/ThR6g.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ThR6g.png" alt="enter image description here" /></a></p>
<p>I have two thoughts in training the model for above situation.</p>
<ul>
<li><p>Feed output <span class="math-container">$t-1$</span> from ground truth with some noise. And, during testing feed output of the model as previous output at <span class="math-container">$t-1$</span>. Maybe we can fine tune with model output when training loss is sufficiently low.</p>
</li>
<li><p>Feed model output to next stage as <span class="math-container">$t-1$</span> output. But I am not sure if this works.</p>
</li>
</ul>
<p>The situation is similar to RNNs but I am using 3D CNN’s here. I don’t know if RNN can be used here. How can I train such a model.</p>

CC BY-SA 4.0
2021-07-26T14:26:51.353Z
What is the word looking like **shoref** in between the two 3D-Encoders?
hanugm
CC BY-SA 4.0
2021-07-26T14:42:16.977Z
Sorry, It is "shared" not shoref. My bad. These are shared convolutions.
Rajat
<deep-learning><convolutional-neural-networks><training><recurrent-neural-networks><pytorch>
Feeding the output back to input in 3D CNN model
Rajat
# Feeding the output back to input in 3D CNN model

(Asked by: username_0 on 2021-07-26)

I am currently designing a Model which takes Input 3D Grid and Model Output at $t-1$. The model figure is described below

[![enter image description here](https://i.sstatic.net/ThR6g.png)](https://i.sstatic.net/ThR6g.png)

I have two thoughts in training the model for above situation.

* Feed output $t-1$ from ground truth with some noise. And, during testing feed output of the model as previous output at $t-1$. Maybe we can fine tune with model output when training loss is sufficiently low.
* Feed model output to next stage as $t-1$ output. But I am not sure if this works.

The situation is similar to RNNs but I am using 3D CNN’s here. I don’t know if RNN can be used here. How can I train such a model.

**Tags:** deep-learning, convolutional-neural-networks, training, recurrent-neural-networks, pytorch

> **Comments:**
> **username_1**: What is the word looking like **shoref** in between the two 3D-Encoders?  
> **username_0**: Sorry, It is "shared" not shoref. My bad. These are shared convolutions.  

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## 0 answers

