Tensorflow: proper queueing/batching structure using training and validation set - tensorflow

Question

I am trying to replicate the structure used in the TensorBoard MNIST example from the recent 2017 dev summit (code found here). In it, feed_dict's are used to alternate between training and validation sets; however, they use the very non-transparent mnist.train.next_batch, which makes it really difficult to iterate your own off of.
Admittedly, this may also be because I'm struggling to understand the queueing implementation in Tensorflow, and explicit examples seem to be in short supply, especially for TF > v1.0.
I've made my own attempt at an image-classifying CNN based on various examples I stumbled across. Originally I had it working with just training data by storing the data in pre-loaded variables (its a small data set). I assumed it would be easier to get the train/valid swap working via feeding data from filenames so I tried to change it to that.
Between changing the format and trying to implement the feed_dict train/valid structure, I get the following -
Error: "You must feed a value for placeholder tensor 'input/Placeholder_2' with dtype string".
Any tips as to how to get it working or further explanation as to how the slicer/train.batch/QueueRunner actually work together would be of great help, as I have found the Tensorflow tutorial to be lacking in terms of explaining the basic workflow between them.
I have a feeling I have the train.batch in the completely wrong spot and that it should probably be in the feed_dict def, but no idea otherwise. Thanks!
import tensorflow as tf
from tensorflow.python.framework import dtypes
# Input - 216x216x1 images; ~900 training images, ~350 validation
# Want to do batches of 5 for training, 20 for validation
learn_rate = .0001
drop_keep = 0.9
train_batch = 5
test_batch = 20
epochs = 1
iterations = int((885/train_batch) * epochs)
#
#
# A BUNCH OF (graph-building) HELPER DEFINITIONS EXCLUDED FOR BREVITY
#
#
#x_init will be fed a list of .jpg filenames (ex: [/file0.jpg, /file1.jpg, ...])
#y_init will be fed an array of one-hot classes (ex: [[0,1,0], [1,0,0], ...])
sess = tf.InteractiveSession()
with tf.name_scope('input'):
batch_size = tf.placeholder(tf.int32)
keep_prob = tf.placeholder(tf.float32)
x_init = tf.placeholder(dtype=tf.string, shape=(None))
y_init = tf.placeholder(dtype=np.int32, shape=(None,3)) #3 classes
image, label = tf.train.slice_input_producer([x_init, y_init])
file = tf.read_file(image)
image = tf.image.decode_jpeg(file, channels=1)
image = tf.cast(image, tf.float32)
image.set_shape([216,216,1])
label = tf.cast(label, tf.int32)
images, labels = tf.train.batch([image, label], batch_size=batch_size)
conv1 = conv_layer(images, [5,5,1], 40, 'conv1')
#
#
# skip the rest of graph defining/functions (merged,train_step)
# very similar to what is found in the MNIST example.
#
#
tf.summary.scalar('accuracy', accuracy)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(OUTPUT_LOC + '/train',sess.graph)
test_writer = tf.summary.FileWriter(OUTPUT_LOC + '/test')
sess.run(tf.global_variables_initializer())
#xTrain, yTrain, xTest, yTest are the train/valid images/labels lists
def feed_dict(train=True):
if train:
batch = train_batch
keep = drop_keep
xval = xTrain
yval = yTrain
else:
batch = test_batch
keep = 1
xval = xTest
yval = yTest
return({x_init:xval, y_init:yval, batch_size:batch, keep_prob:keep})
#If I run "threads", I get the error. It works up until here.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess,coord=coord)
#Don't know what works here or what doesn't.
for i in range(iterations):
if i % 10 == 0:
summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
test_writer.add_summary(summary, i)
print('Accuracy at step %s: %s' % (i, acc))
else:
if i % 100 == 99:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True), options=run_options, run_metadata=run_metadata)
train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
train_writer.add_summary(summary, i)
print('Adding run metadata for', i)
else: # Record a summary
summary, _ = sess.run([merged, train_step],feed_dict=feed_dict(True))
train_writer.add_summary(summary, i)
coord.request_stop()
train_writer.close()
test_writer.close()
sess.close()