我想用CNN来规范CNN的层次
|(W^T * W - I)|
我怎么能在Keras那样做？
来自文档：
Any function that takes in a weight matrix and returns a loss
contribution tensor can be used as a regularizer
以下是实施的示例：
from keras import backend as K
def l1_reg(weight_matrix):
return 0.01 * K.sum(K.abs(weight_matrix))
model.add(Dense(64,input_dim=64,kernel_regularizer=l1_reg)
您的帖子中的损失将是：
from keras import backend as K
def fro_norm(w):
return K.sqrt(K.sum(K.square(K.abs(w))))
def cust_reg(w):
m = K.dot(K.transpose(w),w) - np.eye(w.shape)
return fro_norm(m)
这是一个最小的例子：
import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense,Activation
X = np.random.randn(100,100)
y = np.random.randint(2,size=(100,1))
model = Sequential()
# apply regularization here. applies regularization to the
# output (activation) of the layer
model.add(Dense(32,input_shape=(100,),activity_regularizer=fro_norm))
model.add(Dense(1))
model.add(Activation('softmax'))
model.compile(loss="binary_crossentropy",optimizer='sgd',metrics=['accuracy'])
model.fit(X,y,epochs=1,batch_size=32)
下面不会像@ Marcin的评论所暗示的那样LA.norm不起作用,因为正规化者必须返回Tensor LA.norm()不会.
def orth_norm(w)
m = K.dot(k.transpose(w),w) - np.eye(w.shape)
return LA.norm(m,'fro')
from keras import backend as K
import numpy as np
def orth_norm(w)
m = K.dot(k.transpose(w),'fro')
