Unable to use summary.merge in tensorboard for sep

I am trying to use tensorboard to watch the learning of a convolutional neural net. I am doing good with the tf.summary.merge_all function to create a merged summary. However, I would like to have tracking on accuracy and loss both for training and test data. This post is useful:Logging training and validation loss in tensorboard.

To make things easier to handle, I would like to merge my summaries into two merged summaries, one for training and one for validation.(I will add more stuff eventually, like images weights etc.) I tried to follow the description from tensorboard tf.summary.merge. I can't make it work and I am unable to find any working examples to help me understand where I am going wrong.

with tf.name_scope('accuracy'):
    correct_prediction = tf.equal(tf.argmax(y_logits, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
    tf.summary.scalar('accuracy', accuracy)
    tf.summary.scalar('train_accuracy', accuracy)

with tf.name_scope('Cost'):
    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(logits=y_logits, labels=y))
    opt = tf.train.AdamOptimizer()
    optimizer = opt.minimize(cross_entropy)
    grads = opt.compute_gradients(cross_entropy, [b_fc_loc2])
    tf.summary.scalar('cost', cross_entropy)
    tf.summary.scalar('train_cost', cross_entropy)


with tf.Session() as sess:
    writer = tf.summary.FileWriter('./logs/mnistlogs/1f', sess.graph)
    sess.run(tf.global_variables_initializer())
    merged = tf.summary.merge([cost, accuracy])

This results in the following error:

InvalidArgumentError (see above for traceback): Could not parse one of the summary inputs [[Node: Merge/MergeSummary = MergeSummary[N=2, _device="/job:localhost/replica:0/task:0/cpu:0"](Merge/MergeSummary/inputs_0, Merge/MergeSummary/inputs_1)]]

I would like to know why this doesn't work, and how I can find a solution, any working examples are appreciated.

I figured it out. I need to give the summaries names before merging. The code below solves the problem:

with tf.name_scope('Cost'):
    cross_entropy = tf.reduce_mean(
            tf.nn.softmax_cross_entropy_with_logits(logits=y_logits, labels=y))
    opt = tf.train.AdamOptimizer(learning_rate=0.000003)
    optimizer = opt.minimize(cross_entropy)
    grads = opt.compute_gradients(cross_entropy, [b_fc_loc2])
    cost_sum = tf.summary.scalar('val_cost', cross_entropy)
    training_cost_sum = tf.summary.scalar('train_cost', cross_entropy)


with tf.name_scope('accuracy'):
    correct_prediction = tf.equal(tf.argmax(y_logits, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
    train_accuracy = accuracy
    accuracy_sum = tf.summary.scalar('val_accuracy', accuracy)
    training_accuracy_sum = tf.summary.scalar('train_accuracy', accuracy)


with tf.Session() as sess:
    writer = tf.summary.FileWriter('./logs/{}/{}'.format(session_name, run_num), sess.graph)
    sess.run(tf.global_variables_initializer())
    train_merged = tf.summary.merge([training_accuracy_sum, training_cost_sum])