Training Inception Model

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Revision as of 21:25, 31 July 2017 by Canming (talk | contribs)
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Training your custom inception model

Follow this tensorflow tutorial to retrain a new inception model.

You can use the flower data from the tutorial, or you can create your own training data by replacing the data folder structures with your own. If you follows the tutorial for retraining, you should now have two files: /tmp/output_graph.pb and /tmp/output_labels.txt

Optimize the graph for inference

We would like to optimized the inception graph for inference. To do that, we first build the optimize_for_inference module as follows:

bazel build tensorflow/python/tools:optimize_for_inference 

Now we optimized our graph

bazel-bin/tensorflow/python/tools/optimize_for_inference \
--input=/tmp/output_graph.pb \
--output=/tmp/optimized_graph.pb \
--input_names=Mul \

An inference optimized graph optimized_graph.pb will be generated. We can use it along with the output_lablels.txt file to recognize flowers.

Deploying the model

Emgu.TF v1.3 includes an InceptionObjectRecognition demo project. We can modify the project to use our custom trained model.

We can either include the trained model with our application, or, in our case, upload the trained model to internet for the app to download it, to reduce the application size. We have uploaded our two trained model to github, under the url: 


Source Code

We comment out the code that download the standard inception v3 model, and uncomment the code that use our custom trained model to recognize flowers.

using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using Emgu.TF;
using Emgu.TF.Models;


        public void Recognize(String fileName)
            fileNameTextBox.Text = fileName;
            pictureBox.ImageLocation = fileName;

            //Use the following code for the full inception model
            //Inception inceptionGraph = new Inception();
            //Tensor imageTensor = ImageIO.ReadTensorFromImageFile(fileName, 224, 224, 128.0f, 1.0f / 128.0f);

            //Uncomment the following code to use a retrained model to recognize followers, downloaded from the internet
            Inception inceptionGraph = new Inception(null, new string[] {"optimized_graph.pb", "output_labels.txt"}, "", "Mul", "final_result");
            Tensor imageTensor = ImageIO.ReadTensorFromImageFile(fileName, 299, 299, 128.0f, 1.0f / 128.0f);

            //Uncomment the following code to use a retrained model to recognize followers, if you deployed the models with the application
            //For ".pb" and ".txt" bundled with the application, set the url to null
            //Inception inceptionGraph = new Inception(null, new string[] {"optimized_graph.pb", "output_labels.txt"}, null, "Mul", "final_result");
            //Tensor imageTensor = ImageIO.ReadTensorFromImageFile(fileName, 299, 299, 128.0f, 1.0f / 128.0f);

            float[] probability = inceptionGraph.Recognize(imageTensor);
            String resStr = String.Empty;
            if (probability != null)
                String[] labels = inceptionGraph.Labels;
                float maxVal = 0;
                int maxIdx = 0;
                for (int i = 0; i < probability.Length; i++)
                    if (probability[i] > maxVal)
                        maxVal = probability[i];
                        maxIdx = i;
                resStr = String.Format("Object is {0} with {1}% probability.", labels[maxIdx], maxVal * 100);
            messageLabel.Text = resStr;