dna.py 文件源码

def _res_unit(self, inputs, nb_filter, size=3, stride=1, stage=1, block=1):

        name = '%02d-%02d/' % (stage, block)
        id_name = '%sid_' % (name)
        res_name = '%sres_' % (name)

        # Residual branch

        # 1x1 down-sample conv
        x = kl.BatchNormalization(name=res_name + 'bn1')(inputs)
        x = kl.Activation('relu', name=res_name + 'act1')(x)
        kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
        x = kl.Conv1D(nb_filter[0], 1,
                      name=res_name + 'conv1',
                      subsample_length=stride,
                      kernel_initializer=self.init,
                      kernel_regularizer=kernel_regularizer)(x)

        # LxL conv
        x = kl.BatchNormalization(name=res_name + 'bn2')(x)
        x = kl.Activation('relu', name=res_name + 'act2')(x)
        kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
        x = kl.Conv1D(nb_filter[1], size,
                      name=res_name + 'conv2',
                      border_mode='same',
                      kernel_initializer=self.init,
                      kernel_regularizer=kernel_regularizer)(x)

        # 1x1 up-sample conv
        x = kl.BatchNormalization(name=res_name + 'bn3')(x)
        x = kl.Activation('relu', name=res_name + 'act3')(x)
        kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
        x = kl.Conv1D(nb_filter[2], 1,
                      name=res_name + 'conv3',
                      kernel_initializer=self.init,
                      kernel_regularizer=kernel_regularizer)(x)

        # Identity branch
        if nb_filter[-1] != inputs._keras_shape[-1] or stride > 1:
            kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
            identity = kl.Conv1D(nb_filter[2], 1,
                                 name=id_name + 'conv1',
                                 subsample_length=stride,
                                 kernel_initializer=self.init,
                                 kernel_regularizer=kernel_regularizer)(inputs)
        else:
            identity = inputs

        x = kl.merge([identity, x], name=name + 'merge', mode='sum')

        return x