机器学习实验（六）：用特征值衰减正则化方法进行深度学习实验_1

#This code implements a light version of the Eigenvalue Decay regularizer for
#the Keras deep learning library, approximating the dominant eigenvalue by a
#soft function given by the power method. (It only works with Theano backend)

import numpy as np
from keras import backend as K
from keras.regularizers import Regularizer

class EigenvalueRegularizer(Regularizer):
"""This class implements the Eigenvalue Decay regularizer.

Args:
The constant that controls the regularization on the current layer
( see Section 3 of https://arxiv.org/abs/1604.06985 )
Returns:
The regularized loss (for the training data) and
the original loss (for the validation data).

"""
def __init__(self, k):
self.k = k
self.uses_learning_phase = True

def set_param(self, p):
self.p = p

def __call__(self, loss):
power = 9  # number of iterations of the power method
W = self.p
WW = K.dot(K.transpose(W), W)
dim1, dim2 = K.eval(K.shape(WW))
k = self.k
o = np.ones(dim1)  # initial values for the dominant eigenvector

# power method for approximating the dominant eigenvector:
domin_eigenvect = K.dot(WW, o)
for n in range(power - 1):
domin_eigenvect = K.dot(WW, domin_eigenvect)

WWd = K.dot(WW, domin_eigenvect)
domin_eigenval = K.dot(WWd, domin_eigenvect) / K.dot(domin_eigenvect, domin_eigenvect)  # the corresponding dominant eigenvalue
regularized_loss = loss + (domin_eigenval ** 0.5) * self.k  # multiplied by the given regularization gain
return K.in_train_phase(regularized_loss, loss)

def get_config(self):
return {"name": self.__class__.__name__,
"k": self.k}

Using Theano backend.