<p>My autoencoder give latent vectors with many zeroes components like:</p>
<pre><code>[3.0796502  2.9488854  0.9002177  0.         0.         0.
 0.         0.         0.         1.0181859  0.         0.68507403
 0.         0.6128702  0.         0.         0.         0.
 0.         1.763725   0.         0.         0.         1.0947669
 0.         1.5330162  0.         0.         0.         0.
 0.         1.7434856  0.         0.         1.8942142  2.0379465
 0.         0.         1.2500542  0.         0.         0.
 0.         0.         0.         2.7917862  0.         0.
 2.2105153  0.         0.         0.         1.5798858  0.
 0.         3.7405093  0.8692952  0.01490922 0.         0.
 2.8320081  0.         0.         0.        ]
</code></pre>
<p>Certain components are always zero, another not always. Why might this be happening? How can I figure this out?</p>

CC BY-SA 4.0
2021-05-31T15:58:09.500Z
Ok, there was ReLu() activation on last layer of decoder.
Dmitry  Sokolov
CC BY-SA 4.0
2021-05-31T16:07:04.573Z
Hello. Welcome to AI SE. If you solved your problem or you have the answer to your question, feel free to write a formal answer below ;)
nbro
<neural-networks><autoencoders>
Why would an auto-encoder produce latent vectors with many zeros?
Dmitry  Sokolov
# Why would an auto-encoder produce latent vectors with many zeros?

(Asked by: username_0 on 2021-05-31)

My autoencoder give latent vectors with many zeroes components like:
[3.0796502  2.9488854  0.9002177  0.         0.         0.
 0.         0.         0.         1.0181859  0.         0.68507403
 0.         0.6128702  0.         0.         0.         0.
 0.         1.763725   0.         0.         0.         1.0947669
 0.         1.5330162  0.         0.         0.         0.
 0.         1.7434856  0.         0.         1.8942142  2.0379465
 0.         0.         1.2500542  0.         0.         0.
 0.         0.         0.         2.7917862  0.         0.
 2.2105153  0.         0.         0.         1.5798858  0.
 0.         3.7405093  0.8692952  0.01490922 0.         0.
 2.8320081  0.         0.         0.        ]

Certain components are always zero, another not always. Why might this be happening? How can I figure this out?

**Tags:** neural-networks, autoencoders

> **Comments:**
> **username_0**: Ok, there was ReLu() activation on last layer of decoder.  
> **username_1**: Hello. Welcome to AI SE. If you solved your problem or you have the answer to your question, feel free to write a formal answer below ;)  

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## 0 answers

