From c577f2c6a3b4ddb6ba87a882723c53a248afbeba Mon Sep 17 00:00:00 2001
From: telsoa01 <telmo.soares@arm.com>
Date: Fri, 31 Aug 2018 09:22:23 +0100
Subject: Release 18.08

---
 samples/CMakeLists.txt   |  4 +++
 samples/SimpleSample.cpp | 68 ++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 72 insertions(+)
 create mode 100644 samples/CMakeLists.txt
 create mode 100644 samples/SimpleSample.cpp

(limited to 'samples')

diff --git a/samples/CMakeLists.txt b/samples/CMakeLists.txt
new file mode 100644
index 0000000000..3009ac9a67
--- /dev/null
+++ b/samples/CMakeLists.txt
@@ -0,0 +1,4 @@
+if(BUILD_SAMPLE_APP)
+    add_executable(SimpleSample SimpleSample.cpp)
+    target_link_libraries(SimpleSample armnn pthread)
+endif()
diff --git a/samples/SimpleSample.cpp b/samples/SimpleSample.cpp
new file mode 100644
index 0000000000..43cd93f432
--- /dev/null
+++ b/samples/SimpleSample.cpp
@@ -0,0 +1,68 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+#include <iostream>
+#include "armnn/ArmNN.hpp"
+
+/// A simple example of using the ArmNN SDK API. In this sample, the users single input number is multiplied by 1.0f
+/// using a fully connected layer with a single neuron to produce an output number that is the same as the input.
+int main()
+{
+    using namespace armnn;
+
+    float number;
+    std::cout << "Please enter a number: " << std::endl;
+    std::cin >> number;
+
+    // Construct ArmNN network
+    armnn::NetworkId networkIdentifier;
+    INetworkPtr myNetwork = INetwork::Create();
+
+    armnn::FullyConnectedDescriptor fullyConnectedDesc;
+    float weightsData[] = {1.0f}; // Identity
+    TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32);
+    armnn::ConstTensor weights(weightsInfo, weightsData);
+    IConnectableLayer *fullyConnected = myNetwork->AddFullyConnectedLayer(fullyConnectedDesc, weights,
+                                                                          "fully connected");
+
+    IConnectableLayer *InputLayer = myNetwork->AddInputLayer(0);
+    IConnectableLayer *OutputLayer = myNetwork->AddOutputLayer(0);
+
+    InputLayer->GetOutputSlot(0).Connect(fullyConnected->GetInputSlot(0));
+    fullyConnected->GetOutputSlot(0).Connect(OutputLayer->GetInputSlot(0));
+
+    // Create ArmNN runtime
+    IRuntime::CreationOptions options; // default options
+    IRuntimePtr run = IRuntime::Create(options);
+
+    //Set the tensors in the network.
+    TensorInfo inputTensorInfo(TensorShape({1, 1}), DataType::Float32);
+    InputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
+
+    TensorInfo outputTensorInfo(TensorShape({1, 1}), DataType::Float32);
+    fullyConnected->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+    // Optimise ArmNN network
+    armnn::IOptimizedNetworkPtr optNet = Optimize(*myNetwork, {Compute::CpuRef}, run->GetDeviceSpec());
+
+    // Load graph into runtime
+    run->LoadNetwork(networkIdentifier, std::move(optNet));
+
+    //Creates structures for inputs and outputs.
+    std::vector<float> inputData{number};
+    std::vector<float> outputData(1);
+
+
+    armnn::InputTensors inputTensors{{0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0),
+                                                            inputData.data())}};
+    armnn::OutputTensors outputTensors{{0, armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0),
+                                                         outputData.data())}};
+
+    // Execute network
+    run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
+
+    std::cout << "Your number was " << outputData[0] << std::endl;
+    return 0;
+
+}
-- 
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