def feature_block(ch, image):
    with tf.name_scope('feature'):
        conv_layer = ConvolutionalLayer(n_output_chns=ch, kernel_size=5, with_bias=True, feature_normalization=None, acti_func='selu', w_initializer=self.initializers['w'], b_initializer=self.initializers['b'])
        d_h1s = conv_layer(image, is_training=is_training)
        d_h1r = convr(ch, d_h1s)
        return conv(ch, d_h1r, d_h1s)
def feature_block(ch, image):
    with tf.name_scope('feature'):
        conv_layer = ConvolutionalLayer(n_output_chns=ch, kernel_size=5, with_bias=True, feature_normalization=None, acti_func='selu', w_initializer=self.initializers['w'], b_initializer=self.initializers['b'])
        d_h1s = conv_layer(image, is_training=is_training)
        d_h1r = convr(ch, d_h1s)
        return conv(ch, d_h1r, d_h1s)