MNIST implementation of A Discriminative Feature Learning Approach for Deep Face Recognition.
There are two main implementation of center loss by keras online. One takes use of Embedding Layer, automatically computing the centers. Another takes use of self-defined layer, computing and updating the centers with self-defined equations strictly based on what the paper purposes in 'A Discriminative Feature Learning Approach for Deep Face Recognition'. So I implement both to compare the performance. Here are the details.
input_=Input(shape=(1,))
centers=Embedding(10,2)(input_)
intra_loss=Lambda(lambda x:K.sum(K.square(x[0]-x[1][:,0]),1,keepdims=True))([out1,centers])
model_center_loss=Model([inputs,input_],[out2,intra_loss])
model_center_loss.compile(optimizer="sgd",
loss=["categorical_crossentropy",lambda y_true,y_pred:y_pred],
loss_weights=[1,lambda_c/2.],
metrics=["acc"])
More details, please refer to the code in this repo.
class CenterLossLayer(Layer):
def __init__(self,alpha=0.5,**kwargs):
super().__init__(**kwargs)
self.alpha=alpha
def build(self,input_shape):
self.centers=self.add_weight(name="centers",
shape=(10,2),
initializer="uniform",
trainable=False)
super().build(input_shape)
def call(self,x,mask=None):
#x[0]:N*2 x[1]:N*10 centers:10*2
delta_centers=K.dot(K.transpose(x[1]),K.dot(x[1],self.centers)-x[0])
centers_count=K.sum(K.transpose(x[1]),axis=-1,keepdims=True)+1
delta_centers/=centers_count
new_centers=self.centers-self.alpha*delta_centers
self.add_update((self.centers,new_centers),x)
self.result=x[0]-K.dot(x[1],self.centers)
self.result=K.sum(self.result**2,axis=1,keepdims=True)
return self.result
def compute_output_shape(self,input_shape):
return K.int_shape(self.result)
input_=Input((10,))
center_loss=CenterLossLayer()([out1,input_])
model_center_loss=Model([inputs,input_],[out2,center_loss])
model_center_loss.compile(optimizer="sgd",
loss=["categorical_crossentropy",lambda y_true,y_pred:y_pred],
loss_weights=[1,lambda_c/2.],
metrics=["acc"])
More details, please refer to the code in this repo.
