convolution_neural_network.py 文件源码

def __init__(self,conv1_get,size_p1,bp_num1,bp_num2,bp_num3,rate_w=0.2,rate_t=0.2):
        '''
        :param conv1_get: [a,c,d]?size, number, step of convolution kernel
        :param size_p1: pooling size
        :param bp_num1: units number of flatten layer
        :param bp_num2: units number of hidden layer
        :param bp_num3: units number of output layer
        :param rate_w: rate of weight learning
        :param rate_t: rate of threshold learning
        '''
        self.num_bp1 = bp_num1
        self.num_bp2 = bp_num2
        self.num_bp3 = bp_num3
        self.conv1 = conv1_get[:2]
        self.step_conv1 = conv1_get[2]
        self.size_pooling1 = size_p1
        self.rate_weight = rate_w
        self.rate_thre = rate_t
        self.w_conv1 = [np.mat(-1*np.random.rand(self.conv1[0],self.conv1[0])+0.5) for i in range(self.conv1[1])]
        self.wkj = np.mat(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5)
        self.vji = np.mat(-1*np.random.rand(self.num_bp2, self.num_bp1)+0.5)
        self.thre_conv1 = -2*np.random.rand(self.conv1[1])+1
        self.thre_bp2 = -2*np.random.rand(self.num_bp2)+1
        self.thre_bp3 = -2*np.random.rand(self.num_bp3)+1