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在C#下使用TensorFlow.NET训练自己的数据集

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2023-04-18 16:07:56 时间

今天，我结合代码来详细介绍如何使用 SciSharp STACK 的 TensorFlow.NET 来训练CNN模型，该模型主要实现 图像的分类 ，可以直接移植该代码在 CPU 或 GPU 下使用，并针对你们自己本地的图像数据集进行训练和推理。TensorFlow.NET是基于 .NET Standard 框架的完整实现的TensorFlow，可以支持 .NET Framework 或 .NET CORE , TensorFlow.NET 为广大.NET开发者提供了完美的机器学习框架选择。

SciSharp STACK：https://github.com/SciSharp

什么是TensorFlow.NET?

TensorFlow.NET 是 SciSharp STACK

开源社区团队的贡献，其使命是打造一个完全属于.NET开发者自己的机器学习平台，特别对于C#开发人员来说，是一个“0”学习成本的机器学习平台，该平台集成了大量API和底层封装，力图使TensorFlow的Python代码风格和编程习惯可以无缝移植到.NET平台，下图是同样TF任务的Python实现和C#实现的语法相似度对比，从中读者基本可以略窥一二。

由于TensorFlow.NET在.NET平台的优秀性能，同时搭配SciSharp的NumSharp、SharpCV、Pandas.NET、Keras.NET、Matplotlib.Net等模块，可以完全脱离Python环境使用，目前已经被微软ML.NET官方的底层算法集成，并被谷歌写入TensorFlow官网教程推荐给全球开发者。

· SciSharp 产品结构

· 微软 ML.NET底层集成算法

· 谷歌官方推荐.NET开发者使用

URL: https://www.tensorflow.org/versions/r2.0/api_docs

项目说明

本文利用TensorFlow.NET构建简单的图像分类模型，针对工业现场的印刷字符进行单字符OCR识别，从工业相机获取原始大尺寸的图像，前期使用OpenCV进行图像预处理和字符分割，提取出单个字符的小图，送入TF进行推理，推理的结果按照顺序组合成完整的字符串，返回至主程序逻辑进行后续的生产线工序。

实际使用中，如果你们需要训练自己的图像，只需要把训练的文件夹按照规定的顺序替换成你们自己的图片即可。支持GPU或CPU方式，该项目的完整代码在GitHub如下：

https://github.com/SciSharp/SciSharp-Stack-Examples/blob/master/src/TensorFlowNET.Examples/ImageProcessing/CnnInYourOwnData.cs

模型介绍

本项目的CNN模型主要由 2个卷积层&池化层 和 1个全连接层 组成，激活函数使用常见的Relu，是一个比较浅的卷积神经网络模型。其中超参数之一"学习率"，采用了自定义的动态下降的学习率，后面会有详细说明。具体每一层的Shape参考下图：

数据集说明

为了模型测试的训练速度考虑，图像数据集主要节选了一小部分的OCR字符（X、Y、Z），数据集的特征如下：

· 分类数量：3 classes 【X/Y/Z】

· 图像尺寸：Width 64 × Height 64

· 图像通道：1 channel（灰度图）

· 数据集数量：

o train：X - 384pcs ；Y - 384pcs ；Z - 384pcs

o validation：X - 96pcs ；Y - 96pcs ；Z - 96pcs

o test：X - 96pcs ；Y - 96pcs ；Z - 96pcs

· 其它说明：数据集已经经过 随机 翻转/平移/缩放/镜像 等预处理进行增强

· 整体数据集情况如下图所示：

在这里插入图片描述

在这里插入图片描述

代码说明

环境设置

· .NET 框架：使用.NET Framework 4.7.2及以上，或者使用.NET CORE 2.2及以上

· CPU 配置：Any CPU 或 X64 皆可

· GPU 配置：需要自行配置好CUDA和环境变量，建议 CUDA v10.1，Cudnn v7.5

类库和命名空间引用

1. 从NuGet安装必要的依赖项，主要是SciSharp相关的类库，如下图所示：

注意事项：尽量安装最新版本的类库，CV须使用 SciSharp 的 SharpCV 方便内部变量传递

<PackageReference Include="Colorful.Console" Version="1.2.9" />
<PackageReference Include="Newtonsoft.Json" Version="12.0.3" />
<PackageReference Include="SciSharp.TensorFlow.Redist" Version="1.15.0" />
<PackageReference Include="SciSharp.TensorFlowHub" Version="0.0.5" />
<PackageReference Include="SharpCV" Version="0.2.0" />
<PackageReference Include="SharpZipLib" Version="1.2.0" />
<PackageReference Include="System.Drawing.Common" Version="4.7.0" />
<PackageReference Include="TensorFlow.NET" Version="0.14.0" />

2. 引用命名空间，包括 NumSharp、Tensorflow 和 SharpCV ;

 using NumSharp;
 using NumSharp.Backends;
 using NumSharp.Backends.Unmanaged;
 using SharpCV;
 using System;
 using System.Collections;
 using System.Collections.Generic;
using System.Diagnostics;
using System.IO;
using System.Linq;
using System.Runtime.CompilerServices;
using Tensorflow;
using static Tensorflow.Binding;
using static SharpCV.Binding;
using System.Collections.Concurrent;
using System.Threading.Tasks;

主逻辑结构

主逻辑：

1. 准备数据

2. 创建计算图

3. 训练

4. 预测

public bool Run()
{
    PrepareData();
    BuildGraph();
 
   using (var sess = tf.Session())
   {
       Train(sess);
       Test(sess);
   }

   TestDataOutput();

   return accuracy_test > 0.98;
}

数据集载入

数据集下载和解压

· 数据集地址：

https://github.com/SciSharp/SciSharp-Stack-Examples/blob/master/data/data_CnnInYourOwnData.zip

· 数据集下载和解压代码 ( 部分封装的方法请参考 GitHub完整代码 )：

·       string url = "https://github.com/SciSharp/SciSharp-Stack-Examples/blob/master/data/data_CnnInYourOwnData.zip";
·       Directory.CreateDirectory(Name);
·       Utility.Web.Download(url, Name, "data_CnnInYourOwnData.zip");
·       Utility.Compress.UnZip(Name + "\data_CnnInYourOwnData.zip", Name);

字典创建

读取目录下的子文件夹名称，作为分类的字典，方便后面One-hot使用

 private void FillDictionaryLabel(string DirPath)
 {
     string[] str_dir = Directory.GetDirectories(DirPath, "*", SearchOption.TopDirectoryOnly);
     int str_dir_num = str_dir.Length;
     if (str_dir_num > 0)
     {
         Dict_Label = new Dictionary<Int64, string>();
         for (int i = 0; i < str_dir_num; i++)
         {
             string label = (str_dir[i].Replace(DirPath + "\", "")).Split('\').First();
             Dict_Label.Add(i, label);
             print(i.ToString() + " : " + label);
         }
         n_classes = Dict_Label.Count;
     }
 }

文件List读取和打乱

从文件夹中读取train、validation、test的list，并随机打乱顺序。

  • 读取目录
ArrayFileName_Train = Directory.GetFiles(Name + "\train", "*.*", SearchOption.AllDirectories);
ArrayLabel_Train = GetLabelArray(ArrayFileName_Train);
 
ArrayFileName_Validation = Directory.GetFiles(Name + "\validation", "*.*", SearchOption.AllDirectories);
ArrayLabel_Validation = GetLabelArray(ArrayFileName_Validation);
 
ArrayFileName_Test = Directory.GetFiles(Name + "\test", "*.*", SearchOption.AllDirectories);
ArrayLabel_Test = GetLabelArray(ArrayFileName_Test);
  • 获得标签
private Int64[] GetLabelArray(string[] FilesArray)
{
    Int64[] ArrayLabel = new Int64[FilesArray.Length];
    for (int i = 0; i < ArrayLabel.Length; i++)
    {
        string[] labels = FilesArray[i].Split('\');
        string label = labels[labels.Length - 2];
        ArrayLabel[i] = Dict_Label.Single(k => k.Value == label).Key;
    }
    return ArrayLabel;
}
  • 随机乱序
public (string[], Int64[]) ShuffleArray(int count, string[] images, Int64[] labels)
{
    ArrayList mylist = new ArrayList();
    string[] new_images = new string[count];
    Int64[] new_labels = new Int64[count];
    Random r = new Random();
    for (int i = 0; i < count; i++)
    {
        mylist.Add(i);
    }
 
    for (int i = 0; i < count; i++)
    {
        int rand = r.Next(mylist.Count);
        new_images[i] = images[(int)(mylist[rand])];
        new_labels[i] = labels[(int)(mylist[rand])];
        mylist.RemoveAt(rand);
    }
    print("shuffle array list：" + count.ToString());
    return (new_images, new_labels);
}

部分数据集预先载入

Validation/Test数据集和标签一次性预先载入成NDArray格式。

private void LoadImagesToNDArray()
{
    //Load labels
    y_valid = np.eye(Dict_Label.Count)[new NDArray(ArrayLabel_Validation)];
    y_test = np.eye(Dict_Label.Count)[new NDArray(ArrayLabel_Test)];
    print("Load Labels To NDArray : OK!");
 
    //Load Images
    x_valid = np.zeros(ArrayFileName_Validation.Length, img_h, img_w, n_channels);
    x_test = np.zeros(ArrayFileName_Test.Length, img_h, img_w, n_channels);
    LoadImage(ArrayFileName_Validation, x_valid, "validation");
    LoadImage(ArrayFileName_Test, x_test, "test");
    print("Load Images To NDArray : OK!");
}

private void LoadImage(string[] a, NDArray b, string c)
{
    for (int i = 0; i < a.Length; i++)
    {
        b[i] = ReadTensorFromImageFile(a[i]);
        Console.Write(".");
    }
    Console.WriteLine();
    Console.WriteLine("Load Images To NDArray: " + c);
}

private NDArray ReadTensorFromImageFile(string file_name)
{
    using (var graph = tf.Graph().as_default())
    {
        var file_reader = tf.read_file(file_name, "file_reader");
        var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: n_channels, name: "DecodeJpeg");
        var cast = tf.cast(decodeJpeg, tf.float32);
        var dims_expander = tf.expand_dims(cast, 0);
        var resize = tf.constant(new int[] { img_h, img_w });
        var bilinear = tf.image.resize_bilinear(dims_expander, resize);
        var sub = tf.subtract(bilinear, new float[] { img_mean });
        var normalized = tf.divide(sub, new float[] { img_std });
 
        using (var sess = tf.Session(graph))
        {
            return sess.run(normalized);
        }
    }
}

计算图构建

构建CNN静态计算图，其中学习率每n轮Epoch进行1次递减。

#region BuildGraph
public Graph BuildGraph()
{
    var graph = new Graph().as_default();
 
    tf_with(tf.name_scope("Input"), delegate
            {
                x = tf.placeholder(tf.float32, shape: (-1, img_h, img_w, n_channels), name: "X");
                y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y");
            });
 
    var conv1 = conv_layer(x, filter_size1, num_filters1, stride1, name: "conv1");
    var pool1 = max_pool(conv1, ksize: 2, stride: 2, name: "pool1");
    var conv2 = conv_layer(pool1, filter_size2, num_filters2, stride2, name: "conv2");
    var pool2 = max_pool(conv2, ksize: 2, stride: 2, name: "pool2");
    var layer_flat = flatten_layer(pool2);
    var fc1 = fc_layer(layer_flat, h1, "FC1", use_relu: true);
    var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false);
 
    //Some important parameter saved with graph , easy to load later
    var img_h_t = tf.constant(img_h, name: "img_h");
    var img_w_t = tf.constant(img_w, name: "img_w");
    var img_mean_t = tf.constant(img_mean, name: "img_mean");
    var img_std_t = tf.constant(img_std, name: "img_std");
    var channels_t = tf.constant(n_channels, name: "img_channels");
 
    //learning rate decay
    gloabl_steps = tf.Variable(0, trainable: false);
    learning_rate = tf.Variable(learning_rate_base);
 
    //create train images graph
    tf_with(tf.variable_scope("LoadImage"), delegate
            {
                decodeJpeg = tf.placeholder(tf.@byte, name: "DecodeJpeg");
                var cast = tf.cast(decodeJpeg, tf.float32);
                var dims_expander = tf.expand_dims(cast, 0);
                var resize = tf.constant(new int[] { img_h, img_w });
                var bilinear = tf.image.resize_bilinear(dims_expander, resize);
                var sub = tf.subtract(bilinear, new float[] { img_mean });
                normalized = tf.divide(sub, new float[] { img_std }, name: "normalized");
            });
 
    tf_with(tf.variable_scope("Train"), delegate
            {
                tf_with(tf.variable_scope("Loss"), delegate
                        {
                            loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits), name: "loss");
                        });
 
                tf_with(tf.variable_scope("Optimizer"), delegate
                        {
                            optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss, global_step: gloabl_steps);
                        });
 
                tf_with(tf.variable_scope("Accuracy"), delegate
                        {
                            var correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name: "correct_pred");
                            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy");
                        });
 
                tf_with(tf.variable_scope("Prediction"), delegate
                        {
                            cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions");
                            prob = tf.nn.softmax(output_logits, axis: 1, name: "prob");
                        });
            });
    return graph;
}
 
/// <summary>
/// Create a 2D convolution layer
/// </summary>
/// <param name="x">input from previous layer</param>
/// <param name="filter_size">size of each filter</param>
/// <param name="num_filters">number of filters(or output feature maps)</param>
/// <param name="stride">filter stride</param>
/// <param name="name">layer name</param>
/// <returns>The output array</returns>
private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride, string name)
{
    return tf_with(tf.variable_scope(name), delegate
                   {
 
                       var num_in_channel = x.shape[x.NDims - 1];
                       var shape = new[] { filter_size, filter_size, num_in_channel, num_filters };
                       var W = weight_variable("W", shape);
                       // var tf.summary.histogram("weight", W);
                       var b = bias_variable("b", new[] { num_filters });
                       // tf.summary.histogram("bias", b);
                       var layer = tf.nn.conv2d(x, W,
                                                strides: new[] { 1, stride, stride, 1 },
                                                padding: "SAME");
                       layer += b;
                       return tf.nn.relu(layer);
                   });
}
 
/// <summary>
/// Create a max pooling layer
/// </summary>
/// <param name="x">input to max-pooling layer</param>
/// <param name="ksize">size of the max-pooling filter</param>
/// <param name="stride">stride of the max-pooling filter</param>
/// <param name="name">layer name</param>
/// <returns>The output array</returns>
private Tensor max_pool(Tensor x, int ksize, int stride, string name)
{
    return tf.nn.max_pool(x,
                          ksize: new[] { 1, ksize, ksize, 1 },
                          strides: new[] { 1, stride, stride, 1 },
                          padding: "SAME",
                          name: name);
}
 
/// <summary>
/// Flattens the output of the convolutional layer to be fed into fully-connected layer
/// </summary>
/// <param name="layer">input array</param>
/// <returns>flattened array</returns>
private Tensor flatten_layer(Tensor layer)
{
    return tf_with(tf.variable_scope("Flatten_layer"), delegate
                   {
                       var layer_shape = layer.TensorShape;
                       var num_features = layer_shape[new Slice(1, 4)].size;
                       var layer_flat = tf.reshape(layer, new[] { -1, num_features });
 
                       return layer_flat;
                   });
}
 
/// <summary>
/// Create a weight variable with appropriate initialization
/// </summary>
/// <param name="name"></param>
/// <param name="shape"></param>
/// <returns></returns>
private RefVariable weight_variable(string name, int[] shape)
{
    var initer = tf.truncated_normal_initializer(stddev: 0.01f);
    return tf.get_variable(name,
                           dtype: tf.float32,
                           shape: shape,
                           initializer: initer);
}
 
/// <summary>
/// Create a bias variable with appropriate initialization
/// </summary>
/// <param name="name"></param>
/// <param name="shape"></param>
/// <returns></returns>
private RefVariable bias_variable(string name, int[] shape)
{
    var initial = tf.constant(0f, shape: shape, dtype: tf.float32);
    return tf.get_variable(name,
                           dtype: tf.float32,
                           initializer: initial);
}
 
/// <summary>
/// Create a fully-connected layer
/// </summary>
/// <param name="x">input from previous layer</param>
/// <param name="num_units">number of hidden units in the fully-connected layer</param>
/// <param name="name">layer name</param>
/// <param name="use_relu">boolean to add ReLU non-linearity (or not)</param>
/// <returns>The output array</returns>
private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true)
{
    return tf_with(tf.variable_scope(name), delegate
                   {
                       var in_dim = x.shape[1];
 
                       var W = weight_variable("W_" + name, shape: new[] { in_dim, num_units });
                       var b = bias_variable("b_" + name, new[] { num_units });
 
                       var layer = tf.matmul(x, W) + b;
                       if (use_relu)
                           layer = tf.nn.relu(layer);
 
                       return layer;
                   });
}
#endregion

模型训练和模型保存

· Batch数据集的读取，采用了 SharpCV 的cv2.imread，可以直接读取本地图像文件至NDArray，实现CV和Numpy的无缝对接；

· 使用.NET的异步线程安全队列BlockingCollection，实现TensorFlow原生的队列管理器FIFOQueue；

    • 在训练模型的时候，我们需要将样本从硬盘读取到内存之后，才能进行训练。我们在会话中运行多个线程，并加入队列管理器进行线程间的文件入队出队操作，并限制队列容量，主线程可以利用队列中的数据进行训练，另一个线程进行本地文件的IO读取，这样可以实现数据的读取和模型的训练是异步的，降低训练时间。

· 模型的保存，可以选择每轮训练都保存，或最佳训练模型保存

·       #region Train
·       public void Train(Session sess)
·       {
·           // Number of training iterations in each epoch
·           var num_tr_iter = (ArrayLabel_Train.Length) / batch_size;
·        
·           var init = tf.global_variables_initializer();
·           sess.run(init);
·        
·           var saver = tf.train.Saver(tf.global_variables(), max_to_keep: 10);
·        
·           path_model = Name + "\MODEL";
·           Directory.CreateDirectory(path_model);
·        
·           float loss_val = 100.0f;
·           float accuracy_val = 0f;
·        
·           var sw = new Stopwatch();
·           sw.Start();
·           foreach (var epoch in range(epochs))
·           {
·               print($"Training epoch: {epoch + 1}");
·               // Randomly shuffle the training data at the beginning of each epoch 
·               (ArrayFileName_Train, ArrayLabel_Train) = ShuffleArray(ArrayLabel_Train.Length, ArrayFileName_Train, ArrayLabel_Train);
·               y_train = np.eye(Dict_Label.Count)[new NDArray(ArrayLabel_Train)];
·        
·               //decay learning rate
·               if (learning_rate_step != 0)
·               {
·                   if ((epoch != 0) && (epoch % learning_rate_step == 0))
·                   {
·                       learning_rate_base = learning_rate_base * learning_rate_decay;
·                       if (learning_rate_base <= learning_rate_min) { learning_rate_base = learning_rate_min; }
·                       sess.run(tf.assign(learning_rate, learning_rate_base));
·                   }
·               }
·        
·                //Load local images asynchronously,use queue,improve train efficiency
·               BlockingCollection<(NDArray c_x, NDArray c_y, int iter)> BlockC = new BlockingCollection<(NDArray C1, NDArray C2, int iter)>(TrainQueueCapa);
·               Task.Run(() =>
·                        {
·                            foreach (var iteration in range(num_tr_iter))
·                            {
·                                var start = iteration * batch_size;
·                                var end = (iteration + 1) * batch_size;
·                                (NDArray x_batch, NDArray y_batch) = GetNextBatch(sess, ArrayFileName_Train, y_train, start, end);
·                                BlockC.Add((x_batch, y_batch, iteration));
·                            }
·                            BlockC.CompleteAdding();
·                        });
·        
·               foreach (var item in BlockC.GetConsumingEnumerable())
·               {
·                   sess.run(optimizer, (x, item.c_x), (y, item.c_y));
·        
·                   if (item.iter % display_freq == 0)
·                   {
·                       // Calculate and display the batch loss and accuracy
·                       var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, item.c_x), new FeedItem(y, item.c_y));
·                       loss_val = result[0];
·                       accuracy_val = result[1];
·                       print("CNN：" + ($"iter {item.iter.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")} {sw.ElapsedMilliseconds}ms"));
·                       sw.Restart();
·                   }
·               }             
·        
·               // Run validation after every epoch
·               (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, x_valid), (y, y_valid));
·               print("CNN：" + "---------------------------------------------------------");
·               print("CNN：" + $"gloabl steps: {sess.run(gloabl_steps) },learning rate: {sess.run(learning_rate)}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
·               print("CNN：" + "---------------------------------------------------------");
·        
·               if (SaverBest)
·               {
·                   if (accuracy_val > max_accuracy)
·                   {
·                       max_accuracy = accuracy_val;
·                       saver.save(sess, path_model + "\CNN_Best");
·                       print("CKPT Model is save.");
·                   }
·               }
·               else
·               {
·                   saver.save(sess, path_model + string.Format("\CNN_Epoch_{0}_Loss_{1}_Acc_{2}", epoch, loss_val, accuracy_val));
·                   print("CKPT Model is save.");
·               }
·           }
·           Write_Dictionary(path_model + "\dic.txt", Dict_Label);
·       }
·       private void Write_Dictionary(string path, Dictionary<Int64, string> mydic)
·       {
·           FileStream fs = new FileStream(path, FileMode.Create);
·           StreamWriter sw = new StreamWriter(fs);
·           foreach (var d in mydic) { sw.Write(d.Key + "," + d.Value + "
"); }
·           sw.Flush();
·           sw.Close();
·           fs.Close();
·           print("Write_Dictionary");
·       }
·       private (NDArray, NDArray) Randomize(NDArray x, NDArray y)
·       {
·           var perm = np.random.permutation(y.shape[0]);
·           np.random.shuffle(perm);
·           return (x[perm], y[perm]);
·       }
·       private (NDArray, NDArray) GetNextBatch(NDArray x, NDArray y, int start, int end)
·       {
·           var slice = new Slice(start, end);
·           var x_batch = x[slice];
·           var y_batch = y[slice];
·           return (x_batch, y_batch);
·       }
·       private unsafe (NDArray, NDArray) GetNextBatch(Session sess, string[] x, NDArray y, int start, int end)
·       {
·           NDArray x_batch = np.zeros(end - start, img_h, img_w, n_channels);
·           int n = 0;
·           for (int i = start; i < end; i++)
·           {
·             NDArray img4 = cv2.imread(x[i], IMREAD_COLOR.IMREAD_GRAYSCALE);
·               x_batch[n] = sess.run(normalized, (decodeJpeg, img4));
·               n++;
·           }
·           var slice = new Slice(start, end);
·           var y_batch = y[slice];
·           return (x_batch, y_batch);
·       }
·       #endregion

测试集预测

· 训练完成的模型对test数据集进行预测，并统计准确率

· 计算图中增加了一个提取预测结果Top-1的概率的节点，最后测试集预测的时候可以把详细的预测数据进行输出，方便实际工程中进行调试和优化。

·       public void Test(Session sess)
·       {
·           (loss_test, accuracy_test) = sess.run((loss, accuracy), (x, x_test), (y, y_test));
·           print("CNN：" + "---------------------------------------------------------");
·           print("CNN：" + $"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
·           print("CNN：" + "---------------------------------------------------------");
·        
·           (Test_Cls, Test_Data) = sess.run((cls_prediction, prob), (x, x_test));
·        
·       }
·       private void TestDataOutput()
·       {
·           for (int i = 0; i < ArrayLabel_Test.Length; i++)
·           {
·               Int64 real = ArrayLabel_Test[i];
·               int predict = (int)(Test_Cls[i]);
·               var probability = Test_Data[i, predict];
·               string result = (real == predict) ? "OK" : "NG";
·               string fileName = ArrayFileName_Test[i];
·               string real_str = Dict_Label[real];
·               string predict_str = Dict_Label[predict];
·               print((i + 1).ToString() + "|" + "result:" + result + "|" + "real_str:" + real_str + "|"
·                     + "predict_str:" + predict_str + "|" + "probability:" + probability.GetSingle().ToString() + "|"
·                     + "fileName:" + fileName);
·           }
·       }

总结

本文主要是 .NET下的TensorFlow在实际工业现场视觉检测项目中的应用，使用SciSharp的TensorFlow.NET构建了简单的CNN图像分类模型，该模型包含输入层、卷积与池化层、扁平化层、全连接层和输出层，这些层都是CNN分类模型的必要的层，针对工业现场的实际图像进行了分类，分类准确性较高。

完整代码可以直接用于大家自己的数据集进行训练，已经在工业现场经过大量测试，可以在GPU或CPU环境下运行，只需要更换tensorflow.dll文件即可实现训练环境的切换。

同时，训练完成的模型文件，可以使用 “CKPT+Meta” 或 冻结成“PB” 2种方式，进行现场的部署，模型部署和现场应用推理可以全部在.NET平台下进行，实现工业现场程序的无缝对接。摆脱了以往Python下 需要通过Flask搭建服务器进行数据通讯交互 的方式，现场部署应用时无需配置Python和TensorFlow的环境【无需对工业现场的原有PC升级安装一大堆环境】，整个过程全部使用传统的.NET的DLL引用的方式。

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