Making predictions with TensorFlow trained model and C API - c++

Question

I have built the C API by building the libtensorflow.so target. I want to load a pre-trained model with and run inference on it to make predictions. I was told I can do this by including the 'c_api.h' header file (along with copying that file plus 'libtensorflow.so' to the appropriate place), however, I had no luck finding any examples on that on the web. All I could find are examples which use the Bazel build system whereas I want to use another build system and use TensorFlow as a library. Can somebody help me with an example on how to import either a) a meta graph file; b) a protobuf graph file plus a checkpoint file, to make predictions? A C++ equivalent of the Python file below and built with g++?
#!/usr/bin/env python
import tensorflow as tf
import numpy as np
with tf.Session() as sess:
saver = tf.train.import_meta_graph('./metagraph.meta')
saver.restore(sess, './checkpoint.ckpt')
x = tf.get_collection("x")[0]
yhat = tf.get_collection("yhat")[0]
print sess.run(yhat, feed_dict={x : np.array([[2, 3], [4, 5]])})
Thanks in Advance!
p.s.: For the sake of completeness I have did the following to build the files:
#!/usr/bin/env python
import tensorflow as tf
import numpy as np
x = tf.placeholder(tf.float32, shape=[None, 2], name='x')
tf.add_to_collection("x", x)
y = tf.placeholder(tf.float32, shape=[None, 1], name='y')
w = tf.Variable(np.array([[10.0], [100.0]]), dtype=tf.float32, name='w')
b = tf.Variable(0.0, dtype=tf.float32, name='b')
yhat = tf.add(tf.matmul(x, w), b)
tf.add_to_collection("yhat", yhat)
mse_loss = tf.sqrt(tf.reduce_mean(tf.square(tf.sub(y, yhat))))
step_size = tf.constant(0.01)
optimizer = tf.train.GradientDescentOptimizer(step_size)
init_op = tf.initialize_all_variables()
train_op = optimizer.minimize(mse_loss)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init_op)
for i in xrange(10000):
train_x = np.random.random([100, 2]) * 10
train_y = np.dot(train_x, np.array([[100.0], [10.0]])) + 1.0
sess.run(train_op, feed_dict={x : train_x, y : train_y})
print sess.run(w)
print sess.run(b)
saver.save(sess, './checkpoint.ckpt')
saver.export_meta_graph('./metagraph.meta')
tf.train.write_graph(sess.graph_def, './', 'graph')

Answer 1

I used Eclipse and added c_api.h to my project file and libtensorflow.so to /usr/local/bin. I then added the reference to the libtensorflow shared object to libraries on my GCC C++ Linker, finally created a simple program.
#include <iostream>
#include "c_api.h"
using namespace std;
int main() {
cout << TF_Version();
return 0;
}
This then allowed me to compile and use Tensorflow functions, including those that you want.