Center Loss in Keras

Question

I want to implement center Loss explained in [http://ydwen.github.io/papers/WenECCV16.pdf] in Keras

I started to create a network with 2 outputs such as :

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])

First of all, doing like this, is it the good way to proceed?

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.

Thank you for your help

Answer 1

For me, you can implement this layer following the steps:

1. write a custom layer ComputeCenter that

   • takes two inputs: i). the groudtruth labels y_true (not one-hot encoded, but just integers) and ii). predicted membership y_pred

   • contains a look-up table W of size num_classes x num_feats array as trainable weights (refer to BatchNormalization Layer), and W[j] is the place holder for the moving average for the jth class feature.

   • computes the center loss as specified in the paper.

   • outputs the resulting distance array D

2. To compute the center loss, you need to

   • i). update W[j] using y_pred[k] according to y_true[k]=j,
   • ii). retrieve the center feature c_true[k]=W[j] for sample y_pred[k] whose y_true[k]=j
   • iii) compute the distance between y_pred and c_true.
   • Here c_true[k] = W[j], and k is the sample index, and j is the ground truth label of y_pred[k].

3. use model.add_loss() to compute this loss. Note, don't add this loss in model.compile( loss = ... ).

Finally, you may add some loss coefficient to the center-loss if needed.