Center Loss in Keras
<p>I want to implement center Loss explained in [<a href="http://ydwen.github.io/papers/WenECCV16.pdf]" rel="noreferrer">http://ydwen.github.io/papers/WenECCV16.pdf]</a> in Keras</p>

<p>I started to create a network with 2 outputs such as : </p>

<pre><code>inputs = Input(shape=(100,100,3))
...
fc = Dense(100)(#previousLayer#)
softmax = Softmax(fc)
model = Model(input, output=[softmax, fc])
model.compile(optimizer='sgd', 
              loss=['categorical_crossentropy', 'center_loss'],
              metrics=['accuracy'], loss_weights=[1., 0.2])
</code></pre>

<p>First of all, doing like this, is it the good way to proceed? </p>

<p>Secondly, I don't know how to implement the center_loss in keras. Center_loss looks like mean square error but instead of comparing values to fixed labels, 
it compares values to data updated at each iteration. </p>

<p>Thank you for your help</p>