Creating a dynamic link library (DLL) from TensorFlow saved_model (.pb)

By : Amirali R. Davoudpour

To use TensorFlow in a Visual Studio environment, you have a couple of options for installing the TensorFlow libraries. The specific steps depend on your project requirements and whether you plan to use TensorFlow with CPU or GPU support. Here are the general steps for installing TensorFlow libraries in Visual Studio:

  1. Using pre-built TensorFlow libraries:
    • Download the pre-built TensorFlow libraries for your desired version and configuration (CPU or GPU) from the TensorFlow website (https://www.tensorflow.org/install).
    • Extract the downloaded archive to a location on your system.
    • Open your Visual Studio project and go to "Project Properties" -> "Configuration Properties" -> "Linker" -> "General" -> "Additional Library Directories".
    • Add the path to the extracted TensorFlow libraries to the "Additional Library Directories" field.
    • Go to "Linker" -> "Input" -> "Additional Dependencies" and add the necessary TensorFlow library file names (e.g., tensorflow.lib for CPU or tensorflow_gpu.lib for GPU).
  2. Building TensorFlow from source:
    • If you need to customize TensorFlow or want to build it from source, you can follow the TensorFlow build instructions provided by the TensorFlow community (https://www.tensorflow.org/install/source).
    • Once you have built TensorFlow, you will obtain the necessary library files.
    • Configure your Visual Studio project to link against the built TensorFlow libraries by adding the library directories and file names as described in the previous step.

Note that the specific configuration steps may vary depending on your TensorFlow version, Visual Studio version, and project requirements. Additionally, TensorFlow GPU support requires additional setup, including installing the appropriate GPU drivers and CUDA toolkit.

It's worth mentioning that installing the TensorFlow libraries in Visual Studio is not mandatory if you plan to use TensorFlow solely for running inference in your MQL4 EA. Instead, you can build a separate DLL that contains the TensorFlow integration logic and use it within your MQL4 project. This way, you can avoid directly integrating TensorFlow into your Visual Studio project and keep the MQL4 and TensorFlow components separate.

Overall, the choice between installing TensorFlow libraries in Visual Studio or building a separate DLL depends on your specific requirements and project setup. Consider the complexity of your project, performance requirements, and the need for customization when making this decision.

To build a DLL from a TensorFlow .pb (frozen) model, you can follow these general steps:

  1. Prepare the C++ project:
    • Set up a C++ project in your preferred development environment, such as Visual Studio or Code::Blocks.
    • Configure the project to target the appropriate architecture and compiler settings.
    • Include the necessary dependencies, such as the TensorFlow C++ library, in your project. Make sure to link against the necessary libraries and set the appropriate include paths.
  2. Load and use the TensorFlow model in C++:
    • Include the required TensorFlow C++ headers in your C++ code. For example, tensorflow/core/public/session.h for session management.
    • Create a tensorflow::Session object to manage the TensorFlow session.
    • Load the frozen TensorFlow model from the .pb file using tensorflow::ReadBinaryProto or tensorflow::ReadBinaryProtoFromPath.
    • Run the necessary operations or make predictions using the loaded model and the session.
  3. Build the project as a DLL:
    • Configure your project to build as a DLL (Dynamic Link Library).
    • Set the appropriate build settings to generate a DLL file.
  4. Build the project:
    • Build the project in your development environment. This will compile your C++ code and generate the DLL file.

Once the DLL is built, you can use it in your desired environment, such as integrating it with an MQL4 EA. Ensure that you understand the integration requirements of your specific environment and the necessary steps to import and use the DLL within that environment.

Please note that the exact steps and commands can vary depending on your development environment, compiler, and specific project requirements. Additionally, building a DLL from a TensorFlow model involves advanced C++ programming skills and knowledge of the TensorFlow C++ API.

It's recommended to consult the TensorFlow C++ API documentation and the documentation of your chosen development environment for detailed instructions and examples specific to your setup.

Here's an example code that demonstrates the process of loading a frozen TensorFlow model (.pb file) and making predictions using the TensorFlow C++ API in Visual Studio:

<tensorflow/core/public/session.h>
#include <tensorflow/core/platform/env.h>

int main()
{
// Create a TensorFlow session
tensorflow::Session* session;

// Load the frozen model
tensorflow::Status status = tensorflow::NewSession(tensorflow::SessionOptions(), &session);
if (!status.ok()) {
    std::cerr << "Failed to create TensorFlow session: " << status.error_message() << std::endl;
    return 1;
}

std::string model_path = "path/to/your/model.pb"; // Replace with the path to your .pb file
tensorflow::GraphDef graph_def;
status = tensorflow::ReadBinaryProto(tensorflow::Env::Default(), model_path, &graph_def);
if (!status.ok()) {
    std::cerr << "Failed to load TensorFlow model: " << status.error_message() << std::endl;
    return 1;
}

// Add the graph definition to the session
status = session->Create(graph_def);
if (!status.ok()) {
    std::cerr << "Failed to add graph to TensorFlow session: " << status.error_message() << std::endl;
    return 1;
}

// Make predictions using the loaded model
tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({1, 2})); // Adjust the shape according to your input
auto input_tensor_mapped = input_tensor.tensor<float, 2>();
input_tensor_mapped(0, 0) = 1.0; // Adjust the input values
input_tensor_mapped(0, 1) = 2.0;

std::vector<tensorflow::Tensor> output_tensors;
status = session->Run({{ "input_tensor_name", input_tensor }}, { "output_tensor_name" }, {}, &output_tensors); // Replace input_tensor_name and output_tensor_name with the actual tensor names
if (!status.ok()) {
    std::cerr << "Failed to run TensorFlow session: " << status.error_message() << std::endl;
    return 1;
}

// Process the output tensors
tensorflow::Tensor output_tensor = output_tensors[0];
auto output_tensor_mapped = output_tensor.tensor<float, 2>();

// Perform further operations with the output

// Close the TensorFlow session
status = session->Close();
if (!status.ok()) {
    std::cerr << "Failed to close TensorFlow session: " << status.error_message() << std::endl;
    return 1;
}

return 0;

}

Replace "path/to/your/model.pb" with the actual path to your .pb file. Adjust the input and output tensor names according to your TensorFlow model. Make sure to link against the necessary TensorFlow libraries and set the include paths in your Visual Studio project settings.

Using TensorFlow libraries in Visual Studio follow the instructions:

To use TensorFlow in a Visual Studio environment, you have a couple of options for installing the TensorFlow libraries. The specific steps depend on your project requirements and whether you plan to use TensorFlow with CPU or GPU support. Here are the general steps for installing TensorFlow libraries in Visual Studio:

  1. Using pre-built TensorFlow libraries:
    • Download the pre-built TensorFlow libraries for your desired version and configuration (CPU or GPU) from the TensorFlow website (https://www.tensorflow.org/install).
    • Extract the downloaded archive to a location on your system.
    • Open your Visual Studio project and go to "Project Properties" -> "Configuration Properties" -> "Linker" -> "General" -> "Additional Library Directories".
    • Add the path to the extracted TensorFlow libraries to the "Additional Library Directories" field.
    • Go to "Linker" -> "Input" -> "Additional Dependencies" and add the necessary TensorFlow library file names (e.g., tensorflow.lib for CPU or tensorflow_gpu.lib for GPU).
  2. Building TensorFlow from source:
    • If you need to customize TensorFlow or want to build it from source, you can follow the TensorFlow build instructions provided by the TensorFlow community (https://www.tensorflow.org/install/source).
    • Once you have built TensorFlow, you will obtain the necessary library files.
    • Configure your Visual Studio project to link against the built TensorFlow libraries by adding the library directories and file names as described in the previous step.

Note that the specific configuration steps may vary depending on your TensorFlow version, Visual Studio version, and project requirements. Additionally, TensorFlow GPU support requires additional setup, including installing the appropriate GPU drivers and CUDA toolkit.

It's worth mentioning that installing the TensorFlow libraries in Visual Studio is not mandatory if you plan to use TensorFlow solely for running inference in your MQL4 EA. Instead, you can build a separate DLL that contains the TensorFlow integration logic and use it within your MQL4 project. This way, you can avoid directly integrating TensorFlow into your Visual Studio project and keep the MQL4 and TensorFlow components separate.

Overall, the choice between installing TensorFlow libraries in Visual Studio or building a separate DLL depends on your specific requirements and project setup. Consider the complexity of your project, performance requirements, and the need for customization when making this decision.

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