Tensorflow Model not Restoring and Running Properly - tensorflow

Question

I have built and successfully trained a convolutional model, however I cannot manage to restore the model and run my evaluation on it. The program throws a bunch of errors without giving an answer.
My code for the evaluation is here:
import tensorflow as tf
import main
import Process
import Input
eval_dir = "/Users/Zanhuang/Desktop/NNP/model.ckpt-98"
checkpoint_dir = "/Users/Zanhuang/Desktop/NNP/checkpoint"
def evaluate():
with tf.Graph().as_default() as g:
images, labels = Process.eval_inputs()
forward_propgation_results = Process.forward_propagation(images)
init_op = tf.initialize_all_variables()
saver = tf.train.Saver()
top_k_op = tf.nn.in_top_k(forward_propgation_results, labels, 1)
with tf.Session(graph = g) as sess:
sess.run(init_op)
tf.train.start_queue_runners(sess=sess)
saver.restore(sess, eval_dir)
for i in range(100):
print(sess.run(top_k_op))
def main(argv = None):
evaluate()
if __name__ == '__main__':
tf.app.run()
My output to the eval looks looks like this: but without the program running. It just stays stuck there.
E tensorflow/core/client/tensor_c_api.cc:485] /Users/Zanhuang/Desktop/NNP/Prostate_Cancer_Data1.bin
[[Node: ReaderRead = ReaderRead[_class=["loc:#FixedLengthRecordReader", "loc:#input_producer"], _device="/job:localhost/replica:0/task:0/cpu:0"](FixedLengthRecordReader, input_producer)]]
ERROR:tensorflow:Exception in QueueRunner: /Users/Zanhuang/Desktop/NNP/Prostate_Cancer_Data1.bin
[[Node: ReaderRead = ReaderRead[_class=["loc:#FixedLengthRecordReader", "loc:#input_producer"], _device="/job:localhost/replica:0/task:0/cpu:0"](FixedLengthRecordReader, input_producer)]]
The following is the main part of my program where I save the model and the checkpoint files.
import Input
import Process
import time
import numpy as np
import tensorflow as tf
from datetime import datetime
FLAGS = tf.app.flags.FLAGS
def train():
with tf.Session() as sess:
images, labels = Process.inputs()
forward_propgation_results = Process.forward_propagation(images)
train_loss, cost = Process.error(forward_propgation_results, labels)
image_summary_t = tf.image_summary(images.name, images, max_images = 2)
summary_op = tf.merge_all_summaries()
init = tf.initialize_all_variables()
saver = tf.train.Saver()
sess.run(init)
saver = tf.train.Saver(tf.all_variables())
tf.train.start_queue_runners(sess = sess)
train_dir = "/Users/Zanhuang/Desktop/NNP/model.ckpt"
summary_writer = tf.train.SummaryWriter(train_dir, sess.graph)
for step in range(100):
start_time = time.time()
print(sess.run([train_loss, cost]))
duration = time.time() - start_time
if step % 1 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, (%.1f examples/sec; %.3f ''sec/batch)')
print (format_str % (datetime.now(), step, examples_per_sec, sec_per_batch))
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
if step % 2 == 0:
checkpoint_path = train_dir
saver.save(sess, checkpoint_path, global_step = step)
def main(argv = None):
train()
if __name__ == '__main__':
tf.app.run()