Init c++ example for simple net

CMakeLists.txt

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+cmake_minimum_required(VERSION 3.5)
+project(tensorflow_cpp)
+
+set(CMAKE_CXX_STANDARD 11)
+
+find_package(OpenCV 3.0 QUIET)
+if(NOT OpenCV_FOUND)
+    find_package(OpenCV 2.4.3 QUIET)
+    if(NOT OpenCV_FOUND)
+        message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
+    endif()
+endif()
+
+set(TENSORFLOW_INCLUDES
+        /usr/local/include/tf/
+        /usr/local/include/tf/bazel-genfiles
+        /usr/local/include/tf/tensorflow/
+        /usr/local/include/tf/tensorflow/third-party)
+
+set(TENSORFLOW_LIBS
+        /usr/local/lib/libtensorflow_cc.so
+        /usr/local/lib//libtensorflow_framework.so)
+
+
+include_directories(
+        ${TENSORFLOW_INCLUDES}
+        ${PROJECT_SOURCE_DIR}/third_party/eigen3
+)
+
+add_executable(load_simple_net simple/load_simple_net.cpp)
+target_link_libraries(load_simple_net
+        ${TENSORFLOW_LIBS}
+        ${OpenCV_LIBS}
+        )

simple/load_simple_net.cpp

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+#include "tensorflow/core/public/session.h"
+#include "tensorflow/core/platform/env.h"
+
+using namespace tensorflow;
+
+/**
+ * @brief simple model for click through rate prediction
+ * @details [long description]
+ *
+ * @param argv[1] graph protobuf
+ *
+ * @return [description]
+ */
+int main(int argc, char* argv[]) {
+    // Initialize a tensorflow session
+    Session* session;
+    Status status = NewSession(SessionOptions(), &session);
+    if (!status.ok()) {
+        std::cerr << status.ToString() << std::endl;
+        return 1;
+    } else {
+        std::cout << "Session created successfully" << std::endl;
+    }
+
+    if (argc != 2)
+    {
+        std::cerr << std::endl << "Usage: ./project path_to_graph.pb" << std::endl;
+        return 1;
+    }
+
+    // Load the protobuf graph
+    GraphDef graph_def;
+    std::string graph_path = argv[1];
+    status = ReadBinaryProto(Env::Default(), graph_path, &graph_def);
+    if (!status.ok()) {
+        std::cerr << status.ToString() << std::endl;
+        return 1;
+    } else {
+        std::cout << "Load graph protobuf successfully" << std::endl;
+    }
+
+    // Add the graph to the session
+    status = session->Create(graph_def);
+    if (!status.ok()) {
+        std::cerr << status.ToString() << std::endl;
+        return 1;
+    } else {
+        std::cout << "Add graph to session successfully" << std::endl;
+    }
+
+    // Setup inputs and outputs:
+
+    // Our graph doesn't require any inputs, since it specifies default values,
+    // but we'll change an input to demonstrate.
+    Tensor a(DT_FLOAT, TensorShape());
+    a.scalar<float>()() = 2.0;
+
+    Tensor b(DT_FLOAT, TensorShape());
+    b.scalar<float>()() = 3.0;
+
+    std::vector<std::pair<string, tensorflow::Tensor>> inputs = {
+            { "a:0", a },
+            { "b:0", b },
+    };
+
+    // The session will initialize the outputs
+    std::vector<tensorflow::Tensor> outputs;
+
+    // Run the session, evaluating our "c" operation from the graph
+    status = session->Run(inputs, {"c:0"}, {}, &outputs);
+    if (!status.ok()) {
+        std::cerr << status.ToString() << std::endl;
+        return 1;
+    } else {
+        std::cout << "Run session successfully" << std::endl;
+    }
+
+    // Grab the first output (we only evaluated one graph node: "c")
+    // and convert the node to a scalar representation.
+    auto output_c = outputs[0].scalar<float>();
+
+    // (There are similar methods for vectors and matrices here:
+    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/public/tensor.h)
+
+    // Print the results
+    std::cout << outputs[0].DebugString() << std::endl; // Tensor<type: float shape: [] values: 30>
+    std::cout << "output value: " << output_c() << std::endl; // 30
+
+    // Free any resources used by the session
+    session->Close();
+
+    return 0;
+}

simple/load_simple_net.py

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+#!/usr/bin/env python
+
+import tensorflow as tf
+import numpy as np
+
+import numpy as np
+from tensorflow.python.platform import gfile
+
+# Initialize a tensorflow session
+with tf.Session() as sess:
+    # Load the protobuf graph
+    with gfile.FastGFile("model/simple.pb",'rb') as f:
+        graph_def = tf.GraphDef()
+        graph_def.ParseFromString(f.read())
+        # Add the graph to the session
+        tf.import_graph_def(graph_def, name='')
+
+    # Get graph
+    graph = tf.get_default_graph()
+
+    # Get tensor from graph
+    c = graph.get_tensor_by_name("c:0")
+
+    # Run the session, evaluating our "c" operation from the graph
+    res = sess.run(c, feed_dict={'a:0': 2.0, 'b:0': 3.0})
+
+    print("res = ", res)
+
+

simple/simple_net.py

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+#!/usr/bin/env python
+
+import tensorflow as tf
+import numpy as np
+
+with tf.Session() as sess:
+
+    a=tf.placeholder(tf.float32,shape=None, name='a')
+    b=tf.placeholder(tf.float32,shape=None, name='b')
+    c = tf.multiply(a, b, name='c')
+
+    sess.run(tf.global_variables_initializer())
+
+    tf.train.write_graph(sess.graph_def, 'model/', 'simple.pb', as_text=False)
+
+    res = sess.run(c, feed_dict={'a:0': 2.0, 'b:0': 3.0})
+    print("res = ", res)

third_party/eigen3/.hgeol

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+[patterns]
+*.sh = LF
+*.MINPACK = CRLF
+scripts/*.in = LF
+debug/msvc/*.dat = CRLF
+debug/msvc/*.natvis = CRLF
+unsupported/test/mpreal/*.* = CRLF
+** = native
+
+[repository]
+native = LF

third_party/eigen3/.hgignore

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+syntax: glob
+qrc_*cxx
+*.orig
+*.pyc
+*.diff
+diff
+*.save
+save
+*.old
+*.gmo
+*.qm
+core
+core.*
+*.bak
+*~
+*build*
+*.moc.*
+*.moc
+ui_*
+CMakeCache.txt
+tags
+.*.swp
+activity.png
+*.out
+*.php*
+*.log
+*.orig
+*.rej
+log
+patch
+a
+a.*
+lapack/testing
+lapack/reference

third_party/eigen3/.hgtags

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+2db9468678c6480c9633b6272ff0e3599d1e11a3 2.0-beta3
+375224817dce669b6fa31d920d4c895a63fabf32 2.0-beta1
+3b8120f077865e2a072e10f5be33e1d942b83a06 2.0-rc1
+19dfc0e7666bcee26f7a49eb42f39a0280a3485e 2.0-beta5
+7a7d8a9526f003ffa2430dfb0c2c535b5add3023 2.0-beta4
+7d14ad088ac23769c349518762704f0257f6a39b 2.0.1
+b9d48561579fd7d4c05b2aa42235dc9de6484bf2 2.0-beta6
+e17630a40408243cb1a51ad0fe3a99beb75b7450 before-hg-migration
+eda654d4cda2210ce80719addcf854773e6dec5a 2.0.0
+ee9a7c468a9e73fab12f38f02bac24b07f29ed71 2.0-beta2
+d49097c25d8049e730c254a2fed725a240ce4858 after-hg-migration
+655348878731bcb5d9bbe0854077b052e75e5237 actual-start-from-scratch
+12a658962d4e6dfdc9a1c350fe7b69e36e70675c 3.0-beta1
+5c4180ad827b3f869b13b1d82f5a6ce617d6fcee 3.0-beta2
+7ae24ca6f3891d5ac58ddc7db60ad413c8d6ec35 3.0-beta3
+c40708b9088d622567fecc9208ad4a426621d364 3.0-beta4
+b6456624eae74f49ae8683d8e7b2882a2ca0342a 3.0-rc1
+a810d5dbab47acfe65b3350236efdd98f67d4d8a 3.1.0-alpha1
+304c88ca3affc16dd0b008b1104873986edd77af 3.1.0-alpha2
+920fc730b5930daae0a6dbe296d60ce2e3808215 3.1.0-beta1
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+bf4cb8c934fa3a79f45f1e629610f0225e93e493 3.1.0-rc2
+da195914abcc1d739027cbee7c52077aab30b336 3.2-beta1
+a8e0d153fc5e239ef8b06e3665f1f9e8cb8d49c8 before-evaluators
+09a8e21866106b49c5dec1d6d543e5794e82efa0 3.3-alpha1
+ce5a455b34c0a0ac3545a1497cb4a16c38ed90e8 3.3-beta1
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