dist.py 文件源码

def register(self, info_tensor, param_tensor):
        self.info_tensor = info_tensor #(128,1)

        if self.stddev_fix:
            self.param_tensor = param_tensor

            mean = K.clip(param_tensor[:, 0].dimshuffle(0, 'x'), self.min, self.max) 
            std  = 1.0
        else:
            self.param_tensor = param_tensor # 2 

            mean = K.clip(param_tensor[:, 0].dimshuffle(0, 'x'), self.min, self.max) 
          # std  = K.maximum( param_tensor[:, 1].dimshuffle(0, 'x'), 0)
            std  = K.sigmoid( param_tensor[:, 1].dimshuffle(0, 'x') )

        e = (info_tensor-mean)/(std + K.epsilon())
        self.log_Q_c_given_x = \
            K.sum(-0.5*np.log(2*np.pi) -K.log(std+K.epsilon()) -0.5*(e**2), axis=1) * self.lmbd

#       m = Sequential([ Activation('softmax', input_shape=(self.n,)), Lambda(lambda x: K.log(x), lambda x: x) ])
        return K.reshape(self.log_Q_c_given_x, (-1, 1))