def conv_bn_relu_x3(input, output_chn, kernel_size, stride, use_bias, is_training, name):
    with tf.variable_scope(name):
        z = conv_bn_relu(input, output_chn, kernel_size, stride, use_bias, is_training, 'dense1')
        z_out = conv_bn_relu(z, output_chn, kernel_size, stride, use_bias, is_training, 'dense2')
        z_out = conv_bn_relu(z_out, output_chn, kernel_size, stride, use_bias, is_training, 'dense3')
        return (z + z_out)
def conv_bn_relu_x3(input, output_chn, kernel_size, stride, use_bias, is_training, name):
    with tf.variable_scope(name):
        z = conv_bn_relu(input, output_chn, kernel_size, stride, use_bias, is_training, 'dense1')
        z_out = conv_bn_relu(z, output_chn, kernel_size, stride, use_bias, is_training, 'dense2')
        z_out = conv_bn_relu(z_out, output_chn, kernel_size, stride, use_bias, is_training, 'dense3')
        return (z + z_out)