I use the high level API to train an estimator (specifically, tf.contrib.learn.DNNRegressor) in Python. This project has been tested on OSX and Linux.
So here we go, 1. Training TensorFlow models in C. Python is the primary language in which TensorFlow models are typically developed and trained.

Saving a fully-functional model is very useful—you can load them in TensorFlow.js (Saved Model, HDF5) and then train and run them in web browsers, or convert them to run on mobile devices using TensorFlow Lite (Saved Model, HDF5) *Custom objects (e.g. With ML.NET and related NuGet packages for TensorFlow you can currently do the following:. Besides the checkpoint file, which simply contains pointers to the most recent checkpoints of the model, this creates the following 3 files in the current path: my_model_name.meta; my_model_name.index; my_model_name.data-00000-of-00001; I wonder what each of these files contains. There are a few rules/guidelines to follow when exposing native C++ code to other .NET languages: I'd like to load this model in C++ and run the inference. TensorFlow version (use command below): 1.1.0; Bazel version (if compiling from source): 0.4.5; Describe the problem. Having this repo, you will not need TensorFlow-Serving. In Java, you'll use the Interpreter class to load a model and drive model inference. TensorFlow prediction using its C++ API. Build a simpliest model using Python & Tensorflow and export it to tf model that can be read by C API; Build a simple C code and compile it with gcc and run it like a normal execution file. It shows how you can take an existing model built with a deep learning framework and use that to build a TensorRT engine using the provided parsers. The non core C++ TF code lives in /tensorflow/cc, this is where we will create our model files, we also need a BUILD file so that bazel can build model.cc. As far as i know, there are 2 ways to get those C API header. Getting the Tensorflow C API. mkdir /path/tensorflow/model cd /path/tensorflow/model touch model.cc touch BUILD We add the bazel instructions into the BUILD file: The Java API for running an inference with TensorFlow Lite is primarily designed for use with Android, so it's available as an Android library dependency: org.tensorflow:tensorflow-lite. Now it’s time to create a wrapper layer around the TensorFlow API.

As we created a Windows Runtime Component, the layer will be written in C++/CX. tensorflow-predictor-cpp. Contains two examples: simple model c = a * b; an industrial deep model for large scale click through rate prediction; Covered knowledge points: save model and checkpoint I then use export_savedmodel to save it to protobuf. subclassed models or layers) require special attention when saving and loading. Setup import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers When to use a Sequential model.