<p>I don't know if the approach you are using will actually give you any useful results since the way the network learns and what it exactly learns aren't known, I suggest you use a different kind of autoencoder, that automatically learns disentangled representations of the data in a latent space, this way you can be sure that all the parameters you find are actually contributing to the representation of your data. check this <a href="https://arxiv.org/abs/1606.05579" rel="nofollow noreferrer">article</a></p>
Autoencoders: Find the important neurons
<p>I have implemented Autoencoder using Keras that takes <code>112*112*3</code> neurons as input and <code>100</code> neurons as the compressed/encoded state. I want to find the neurons out of these 100 that learns the important features. So far i have calculated eigen values(e) and eigen vectors(v) using the following steps. And i found out that around first 30 values of (e) is greater than 0. Does that mean the first 30 modes are the important ones? Is there any other method that could find the important neurons?</p>

<p>Thanks in Advance</p>

<pre><code>x_enc = enc_model.predict(x_train, batch_size=BATCH_SIZE) # shape (3156,100)
x_mean = np.mean(x_enc, axis=0) # shape (100,)
x_stds = np.std(x_enc, axis=0) # shape (100,)
x_cov = np.cov((x_enc - x_mean).T) # shape (100,100)
e, v = np.linalg.eig(x_cov) # shape (100,) and (100,100) respectively
</code></pre>