bpnn.py 文件源码

def back_propagation(self,gradient):

        gradient_activation = self.cal_gradient() # i * i ?
        gradient = np.asmatrix(np.dot(gradient.T,gradient_activation))

        self._gradient_weight = np.asmatrix(self.xdata)
        self._gradient_bias = -1
        self._gradient_x = self.weight

        self.gradient_weight = np.dot(gradient.T,self._gradient_weight.T)
        self.gradient_bias = gradient * self._gradient_bias
        self.gradient = np.dot(gradient,self._gradient_x).T
        # ----------------------upgrade
        # -----------the Negative gradient direction --------
        self.weight = self.weight - self.learn_rate * self.gradient_weight
        self.bias = self.bias - self.learn_rate * self.gradient_bias.T

        return self.gradient