import tensorflow as tf

            transform2 = tf.concat([padded2[:, :, :, 0], padded2[:, :, :, 1]], 1)
            stacked = tf.concat([img_orig, transform1, transform2], 2, 'viz')
            tf.summary.image('visualize',
                             tf.expand_dims(stacked, -1), max_outputs=30)

        sampled = tf.concat([sampled1, sampled2], 3, 'sampled_concat')
        logits = (LinearWrap(sampled)
                  .FullyConnected('fc1', out_dim=256, nl=tf.nn.relu)
                  .FullyConnected('fc2', out_dim=128, nl=tf.nn.relu)
                  .FullyConnected('fct', out_dim=19, nl=tf.identity)())
        tf.nn.softmax(logits, name='prob')

        cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
        cost = tf.reduce_mean(cost, name='cross_entropy_loss')

        wrong = tf.to_float(tf.logical_not(tf.nn.in_top_k(logits, label, 1)), name='incorrect_vector')
        summary.add_moving_summary(tf.reduce_mean(wrong, name='train_error'))

        wd_cost = tf.multiply(1e-5, regularize_cost('fc.*/W', tf.nn.l2_loss),
                              name='regularize_loss')
        summary.add_moving_summary(cost, wd_cost)
        self.cost = tf.add_n([wd_cost, cost], name='cost')

    def _get_optimizer(self):
        lr = tf.get_variable('learning_rate', initializer=5e-4, trainable=False)
        opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
