C#使用TensorFlow.NET训练自己的数据集的方法

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：

• train：X - 384pcs ； Y - 384pcs ； Z - 384pcs
• validation：X - 96pcs ； Y - 96pcs ； Z - 96pcs
• 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：

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

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

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：

准备数据

创建计算图

训练

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();
   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;
}

/// 

/// Create a 2D convolution layer
/// 
/// input from previous layer
/// size of each filter
/// number of filters(or output feature maps)
/// filter stride
/// layer name
/// The output array
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);
     });
}

/// 

/// Create a max pooling layer
/// 
/// input to max-pooling layer
/// size of the max-pooling filter
/// stride of the max-pooling filter
/// layer name
/// The output array
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);
}

/// 

/// Flattens the output of the convolutional layer to be fed into fully-connected layer
/// 
/// input array
/// flattened array
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;
     });
}

/// 

/// Create a weight variable with appropriate initialization
/// 
/// 
/// 
/// 
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);
}

/// 

/// Create a bias variable with appropriate initialization
/// 
/// 
/// 
/// 
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);
}

/// 

/// Create a fully-connected layer
/// 
/// input from previous layer
/// number of hidden units in the fully-connected layer
/// layer name
/// boolean to add ReLU non-linearity (or not)
/// The output array
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 mydic)
{
 FileStream fs = new FileStream(path, FileMode.Create);
 StreamWriter sw = new StreamWriter(fs);
 foreach (var d in mydic) { sw.Write(d.Key + "," + d.Value + "\r\n"); }
 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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