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：
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
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<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 ;
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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
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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 )：
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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
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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
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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
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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
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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。
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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。
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#region BuildGraphpublic 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
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#region Trainpublic 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。
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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。
到此这篇关于C#使用TensorFlow.NET训练自己的数据集的方法的文章就介绍到这了,更多相关C# TensorFlow.NET训练数据集内容请搜索服务器之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持服务器之家！
原文链接：https://www.cnblogs.com/pepure/p/pepure.html
