batch_norm.py 文件源码

def __init__(self, x, size, selectTrain, sess, toTarget=None, ts=0.001):

        self.sess = sess
        self.mean_x_train, self.variance_x_train = moments(x, [0])

        #self.mean_x_ma, self.variance_x_ma = moments(self.x_splh, [0])

        self.mean_x_ma = tf.Variable(tf.zeros([size]))
        self.variance_x_ma = tf.Variable(tf.ones([size]))


        self.update = tf.tuple([self.variance_x_ma.assign(0.95*self.variance_x_ma+ 0.05*self.variance_x_train)] , control_inputs=[self.mean_x_ma.assign(0.95*self.mean_x_ma+ 0.05*self.mean_x_train)])[0]
        self.mean_x_ma_update = tf.tuple([self.mean_x_train] , control_inputs=[])[0]
        self.printUp = tf.Print(self.mean_x_ma_update, [selectTrain], message="selectTrain value : ")
        self.variance_x_ma_update = tf.tuple([self.variance_x_train], control_inputs=[])[0]

        def getxmau(): return self.mean_x_ma_update
        def getxma(): return self.mean_x_ma    

        def getvxmau(): return self.variance_x_ma_update
        def getvxma(): return self.variance_x_ma

        self.mean_x = tf.cond(selectTrain, getxmau, getxma)
        self.variance_x = tf.cond(selectTrain, getvxmau, getvxma)

        self.beta = tf.Variable(tf.zeros([size]))
        self.gamma = tf.Variable(tf.ones([size]))

        #tfs.tfs.session.run(tf.initialize_variables([self.beta, self.gamma]))#, self.mean_x_ma, self.variance_x_ma]))
        self.xNorm = tf.reshape(tf.nn.batch_norm_with_global_normalization(tf.reshape(x, [-1, 1, 1, size]), self.mean_x, self.variance_x, self.beta, self.gamma, 0.01, True), [-1, size])

        if toTarget!=None:
            self.isTracking = toTarget
            self.updateBeta = self.beta.assign(self.beta*(1-ts)+self.isTracking.beta*ts)
            self.updateGamma = self.gamma.assign(self.gamma*(1-ts)+self.isTracking.gamma*ts)
            self.updateTarget = tf.group(self.updateBeta, self.updateGamma)