if (argc != 2) {

std::cout << "WARNING: Input Args missing" << std::endl;

return 0;

}

std::string model_path = argv[1]; // Model_path *.pb file

// TensorName pre-defined in python file, Need to extract values from tensors

std::string input_tensor_name = "inputs";

std::string output_tensor_name = "output_node";

// Create New Session

Session* session;

Status status = NewSession(SessionOptions(), &session);

if (!status.ok()) {

std::cout << status.ToString() << "\n";

return 0;

}

// Create prediction demo

tf_model::ANNModelLoader model; //Create demo for prediction

if (0 != model.load(session, model_path)) {

std::cout << "Error: Model Loading failed..." << std::endl;

return 0;

}

// Define Input tensor and Feature Adapter

// Demo example: [1.0, 1.0, 1.0, 1.0, 1.0] for Iris Example, including bias

int ndim = 5;

std::vector<double> input;

for (int i = 0; i < ndim; i++) {

input.push_back(1.0);

}

// New Feature Adapter to convert vector to tensors dictionary

tf_model::ANNFeatureAdapter input_feat;

input_feat.assign(input_tensor_name, &input); //Assign vec<double> to tensor

// Make New Prediction

double prediction = 0.0;

if (0 != model.predict(session, input_feat, output_tensor_name, &prediction)) {

std::cout << "WARNING: Prediction failed..." << std::endl;

}

std::cout << "Output Prediction Value:" << prediction << std::endl;

return 0;