Running Tensorflow model test data after closing session - python-3.x

Question

I have a Convnet I am trying to replicate (not my original code) that was able to run test dataset into the trained model only when I trained and tested in the same sitting. I tweaked only a few lines of the code to make it run test data after said sitting so I am not sure what might be going on. I noticed that "logits_out" was a dataflow edge rather than node in tensorboard, so is it that because edges aren't saved in checkpoints automatically, in conjunction with the fact that it is not saved as a node or in any other form intentionally in original code, that it can't be called after the first sitting closes?
This is the general structure of the training phase:
tf.reset_default_graph()
graph = tf.Graph()
with graph.as_default():
with tf.name_scope('1st_pool'):
#first layer
#subsequent layers
with graph.as_default():
#flattening, dropout, optimization, etc...
#some summary.scalar for loss analyses
logits_out = tf.layers.dense(flat, 1) #flat is the flattened array
saved_1 = tf.train.Saver()
trained_event = tf.summary.FileWriter('./CNN/train', graph=graph)
test_event = tf.summary.FileWriter('./CNN/test', graph=graph)
merged = tf.summary.merge_all()
with tf.Session(graph=graph) as sess:
#training and "validating"
sess.run(tf.global_variables_initializer())
#running train summaries
if step = test_round:
#running test summaries
saved_1.save(sess, './CNN/model_1.ckpt')
(EDITED:code pasted incorrectly)
This code ran successfully during the continuous sitting with graph still open:
with tf.Session(graph=graph) as sess:
saved_1.restore(sess, tf.train.latest_checkpoint('./CNN'))
#
pred = sess.run(logits_out, feed_dict={some inputs for placeholders})
#
Only tweaked 2 lines pretty much (shown below) to load meta files in a new graph on the next day but gave the error "name 'logits_out' is not defined" when I try to run in a separate sitting (in fact, other variables I tried to sess.run gave the same error):
with tf.Session(graph=tf.get_default_graph()) as sess:
saved_1 = tf.train.import_meta_graph('./CNN/model_1.ckpt.meta')
saved_1.restore(sess, tf.train.latest_checkpoint('./CNN'))
pred = sess.run(logits_out, feed_dict={some inputs for placeholders})
#
EDITED:I'm thinking it might be because I am missing a scope - or misunderstanding how tensorflow names stuff - after restoring the session/graph the next day, but I can't see how - the only thing that had been named were the pool.

Answer 1

I was able to run data through the model by just creating the graph by running this section of code today:
tf.reset_default_graph()
graph = tf.Graph()
with graph.as_default():
with tf.name_scope('1st_pool'):
#first layer
#subsequent layers
with graph.as_default():
#flattening, dropout, optimization, etc...
#some summary.scalar for loss analyses
logits_out = tf.layers.dense(flat, 1) #flat is the flattened array
saved_1 = tf.train.Saver()
trained_event = tf.summary.FileWriter('./CNN/train', graph=graph)
test_event = tf.summary.FileWriter('./CNN/test', graph=graph)
merged = tf.summary.merge_all()
with tf.Session(graph=graph) as sess:
#training and "validating"
sess.run(tf.global_variables_initializer())
#running train summaries
if step = test_round:
#running test summaries
saved_1.save(sess, './CNN/model_1.ckpt')
and then running
the code without the edited 2 lines:
with tf.Session(graph=graph) as sess:
saved_1.restore(sess, tf.train.latest_checkpoint('./CNN'))
#
pred = sess.run(logits_out, feed_dict={some inputs for placeholders})
#
So the gist of all this entire post on SO was that I did not have to use tf.train.import_meta_graph, but what I don't understand is what is the use of tf.train.import_meta_graph? I thought it imports the graph and it's metadata saved in ".meta" file so I could avoid having to rebuild the graph from the source code?
(note: I will remove this postscript question once I figure it out)