<p>Refer to <a href="https://arxiv.org/abs/1512.00567" rel="nofollow noreferrer">InceprtionV3 paper</a>.</p>
<p>You can see that the mixed layers are made of four parallel connections with single input and we get the output by concatenating all parallel outputs into one. Note that to contatenate all the outputs, all parallel feature maps have to have identical first two dimensions (number of feature maps can differ) and this is achieved by strides and pooling.</p>
<p><a href="https://i.stack.imgur.com/H4pFo.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/H4pFo.png" alt="Inception layer" /></a></p>
What are mixed layers in tf.keras.applications.InceptionV3?
<p>I am currently trying to understand the architecture of Inseption v3 as implemented in <code>tf.keras.applications.InceptionV3</code>.</p>
<p>I am looking at the list of names in the model's layers:</p>
<pre class="lang-py prettyprint-override"><code>print([layer.name for layer in model.layers])

#Outputs:
['input_1',
 'conv2d',
 'batch_normalization',
 'activation',
 'conv2d_1',
 'batch_normalization_1',
 'activation_1',
 'conv2d_2',
 ...
]

</code></pre>
<p>I understand how batch normalization, pooling and conv layers transform inputs, but deeper we have layers named <code>mixed1, mixed2, ...</code> and so on. I am trying to understand how they (mixed layers) are transforming their inputs.</p>
<p>So far, I couldn't find any information about them.
How does a mixed layer work? What does it do?</p>