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:

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

<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" />

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<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<t>，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 + "\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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