Not found: FeedInputs: unable to find feed output TensorFlow - c++

Question

I was trying this example of using Tensorflow saved model in c++ in this website:
https://medium.com/jim-fleming/loading-a-tensorflow-graph-with-the-c-api-4caaff88463f#.ji310n4zo
It works well. But it does not save the values of the variables a and b as it only saves the graph not the variables. I tried to replace the following line:
tf.train.write_graph(sess.graph_def, 'models/', 'graph.pb', as_text=False)
with
saver.save(sess, 'models/graph', global_step=0)
of course after creating the saver object. It does not work and it outputs:
Not found: FeedInputs: unable to find feed output a
I checked the nodes the Nodes that are loaded and they are only:
_SOURCE
_SINK
while in the write_graph function and then load the model in C++, I got the following nodes loaded:
_SOURCE
_SINK
save/restore_slice_1/shape_and_slice
save/restore_slice_1/tensor_name
save/restore_slice/shape_and_slice
save/restore_slice/tensor_name
save/save/shapes_and_slices
save/save/tensor_names
save/Const
save/restore_slice_1
save/restore_slice
b
save/Assign_1
b/read
b/initial_value
b/Assign
a
save/Assign
save/restore_all
save/save
save/control_dependency
a/read
c
a/initial_value
a/Assign
init
Tensor
and even the graph file that is created by saver.save() is much smaller, 165B, compared to the one created by write_graph, 1.9KB.

Answer 1

I'm not sure if that is the best way of solving the problem but at least it solves it.
As write_graph can also store the values of the constants, I added the following code to the python just before writing the graph with write_graph function:
for v in tf.trainable_variables():
vc = tf.constant(v.eval())
tf.assign(v, vc, name="assign_variables")
This creates constants that store variables' values after being trained and then create tensors "assign_variables" to assign them to the variables. Now, when you call write_graph, it will store the variables' values in the file.
The only remaining part is to call these tensors "assign_variables" in the c code to make sure that your variables are assigned with the constants values that are stored in the file. Here is a one way to do it:
Status status = NewSession(SessionOptions(), &session);
std::vector<tensorflow::Tensor> outputs;
for(int i = 0;status.ok(); i++) {
char name[100];
if (i==0)
sprintf(name, "assign_variables");
else
sprintf(name, "assign_variables_%d", i);
status = session->Run({}, {name}, {}, &outputs);
}

Answer 2

There is another way of restoring the variables, by calling the save/restore_all operation, that should be present in the graph:
std::vector<tensorflow::Tensor> outputs;
Tensor checkpoint_filepath(DT_STRING, TensorShape());
checkpoint_filepath.scalar<std::string>()() = "path to the checkpoint file";
status = session->Run( {{ "save/Const", checkpoint_filepath },},
{}, {"save/restore_all"}, &outputs);