private static void SetupPyEnv(){string envPythonHome = @"C:\Users\arnal\AppData\Local\Programs\Python\Python37\";string envPythonLib = envPythonHome + "Lib\\;" + envPythonHome + @"Lib\site-packages\";Environment.SetEnvironmentVariable("PYTHONHOME", envPythonHome, EnvironmentVariableTarget.Process);Environment.SetEnvironmentVariable("PATH", envPythonHome + ";" + envPythonLib + ";" + Environment.GetEnvironmentVariable("PATH", EnvironmentVariableTarget.Machine), EnvironmentVariableTarget.Process);Environment.SetEnvironmentVariable("PYTHONPATH", envPythonLib, EnvironmentVariableTarget.User);PythonEngine.PythonHome = envPythonHome;PythonEngine.PythonPath = Environment.GetEnvironmentVariable("PYTHONPATH");}

现在，我们将看到使用Keras.NET创建用于硬币识别的CNN多么容易和透明。以下类显示了包含模型所有逻辑的Cnn类。

public class Cnn{private DataSet _dataset;private Sequential _model;public Cnn(DataSet dataset){_dataset = dataset;_model = new Sequential();}public void Train(){// Build CNN model_model.Add(new Conv2D(32, kernel_size: (3, 3).ToTuple(),padding: Settings.PaddingMode,input_shape: new Shape(Settings.ImgWidth, Settings.ImgHeight, Settings.Channels)));_model.Add(new Activation(Settings.ActivationFunction));_model.Add(new Conv2D(32, (3, 3).ToTuple()));_model.Add(new Activation(Settings.ActivationFunction));_model.Add(new MaxPooling2D(pool_size: (2, 2).ToTuple()));_model.Add(new Dropout(0.25));_model.Add(new Conv2D(64, kernel_size: (3, 3).ToTuple(),padding: Settings.PaddingMode));_model.Add(new Activation(Settings.ActivationFunction));_model.Add(new Conv2D(64, (3, 3).ToTuple()));_model.Add(new Activation(Settings.ActivationFunction));_model.Add(new MaxPooling2D(pool_size: (2, 2).ToTuple()));_model.Add(new Dropout(0.25));_model.Add(new Flatten());_model.Add(new Dense(Settings.FullyConnectedNodes));_model.Add(new Activation(Settings.ActivationFunction));_model.Add(new Dropout(0.5));_model.Add(new Dense(_dataset.NumberClasses));_model.Add(new Softmax());_model.Compile(loss: Settings.LossFunction,optimizer: Settings.Optimizer, metrics: new string[] { Settings.Accuracy });_model.Fit(_dataset.TrainX, _dataset.TrainY,batch_size: Settings.BatchSize,epochs: Settings.Epochs,validation_data: new NDarray[] { _dataset.ValidationX, _dataset.ValidationY });var score = _model.Evaluate(_dataset.ValidationX, _dataset.ValidationY, verbose: 0);Console.WriteLine("Test loss:" + score[0]);Console.WriteLine("Test accuracy:" + score[1]);}public NDarray Predict(string imgPath){NDarray x = Utils.Normalize(imgPath);x = x.reshape(1, x.shape[0], x.shape[1], x.shape[2]);return _model.Predict(x);}}

如我们所见，我们首先有一个构造函数，用于接收数据集（在本系列的第二篇文章中导入和处理），并创建存储在private变量中的Sequential类的新实例_model。什么Sequential？ 它是一个空模型，使我们有可能堆叠图层，而这正是我们所需要的。

var cnn = new Cnn(dataSet);
cnn.Train();

让我们看看在本系列中我们要解决的硬币识别问题的训练过程中获得的结果：