def ResCNN(self, x):
        """
            repeat of two conv
        :param x: tensor, input shape
        :return: tensor, result of two conv of resnet
        """
        # pre-activation
        # x = PReLU()(x)
        x = Conv1D(self.filters_num,
                                kernel_size=1,
                                padding='SAME',
                                kernel_regularizer=l2(self.l2),
                                bias_regularizer=l2(self.l2),
                                activation=self.activation_conv,
                                )(x)
        x = BatchNormalization()(x)
        #x = PReLU()(x)
        x = Conv1D(self.filters_num,
                                kernel_size=1,
                                padding='SAME',
                                kernel_regularizer=l2(self.l2),
                                bias_regularizer=l2(self.l2),
                                activation=self.activation_conv,
                                )(x)
        x = BatchNormalization()(x)
        # x = Dropout(self.dropout)(x)
        x = PReLU()(x)
        return x

            repeat of two conv
        :param x: tensor, input shape
        :return: tensor, result of two conv of resnet
        
keras_textclassification/m07_TextDPCNN/graph.py