在C＃中从Keras.NET开始——训练您的第一个模型

PM> Install-Package Keras.NET -Version 3.8.4.4

除此之外，您将需要使用Windows CLI或Powershell中的pip安装程序来安装Keras和Tensorflow for Python：

pip install keras

pip install tensorflow

它必须在Windows-CLI中看起来像：

C:\Users\YOURNAME>pip install keras

如果您对pip安装程序有疑问，请检查是否已将Python设置为系统环境。安装屏幕底部有一个复选框，称为“将Python添加到PATH”，这非常重要。

using System;

namespace Keras.net_and_fashion_mnist
{
    class Program
    {
        static void Main(string[] args)
        {
            KerasClass keras = new KerasClass();
            keras.TrainModel();
        }        
    }
}

KerasClass是更加有趣的：

using Keras.Datasets;
using Keras.Layers;
using Keras.Models;
using Keras.Utils;
using Numpy;
using System;
using System.IO;
using System.Linq;

namespace Keras.net_and_fashion_mnist
{
    class KerasClass
    {
        public void TrainModel()
        {

            int batch_size = 1000;   //Size of the batches per epoch
            int num_classes = 10;    //We got 10 outputs since 
                                     //we can predict 10 different labels seen on the 
                    //dataset: https://github.com/zalandoresearch/fashion-mnist#labels
            int epochs = 30;         //Amount on trainingperiods, 
                                     //I figure it out that the maximum is something about 
                                     //700 epochs, after this it won't increase the 
                                     //accuracy siginificantly

            // input image dimensions
            int img_rows = 28, img_cols = 28;

            // the data, split between train and test sets
            var ((x_train, y_train), (x_test, y_test)) = 
                                      FashionMNIST.LoadData(); // Load the datasets from 
                                      // fashion MNIST, Keras.Net implement this directly

            x_train.reshape(-1, img_rows, img_cols).astype(np.float32); //ByteArray needs 
                                      //to be reshaped to fit the dimmensions of the y arrays

            y_train = Util.ToCategorical(y_train, num_classes); //here, you modify the 
                                                                //forecast data to 10 outputs
                                                                //as we have 10 different 
                                                                //labels to predict (see the 
                                                                //Labels on the Dataset)
            y_test = Util.ToCategorical(y_test, num_classes);   //same for the test data 
                                                                //[hint: you can change this 
                                                                //in example you want to 
                                                                //make just a binary 
                                                                //crossentropy as you just 
                                                                //want to figure, i.e., if 
                                                                //this is a angleboot or not

            var model = new Sequential();

            model.Add(new Dense(100, 784, "sigmoid")); //hidden dense layer, with 100 neurons, 
                                                       //you have 28*28 pixel which make 
                                                       //784 'inputs', and sigmoid function 
                                                       //as activationfunction
            model.Add(new Dense(10, null, "sigmoid")); //Ouputlayer with 10 outputs,...
            model.Compile(optimizer: "sgd", loss: "categorical_crossentropy", 
                metrics: new string[] { "accuracy" }); //we have a crossentropy as prediction 
                                                       //and want to see as well the 
                                                       //accuracy metric.

            var X_train = x_train.reshape(60000, 784); //this is actually very important. 
                                                       //C# works with pointers, 
                                                       //so if you have to reshape (again) 
                                                       //the function for the correct 
                                                       //processing, you need to write this 
                                                       //to a different var
            var X_test = x_test.reshape(10000, 784);

            model.Fit(X_train, y_train, batch_size, epochs, 1); //now, we set the data to 
                                                                //the model with all the 
                                                                //arguments (x and y data, 
                                                                //batch size...the '1' is 
                                                                //just verbose=1

            Console.WriteLine("---------------------");
            Console.WriteLine(X_train.shape);
            Console.WriteLine(X_test.shape);
            Console.WriteLine(y_train[0]);
            Console.WriteLine(y_train[1]);       //some outputs...you can play with them
            
            var y_train_pred = model.Predict(X_train);       //prediction on the train data
            Console.WriteLine(y_train_pred);

            model.Evaluate(X_test.reshape(-1, 784), y_test); //-1 tells the code that 
                                                             //it can figure out the size of 
                                                             //the array by itself
        }

        private byte[] openDatas(string path, int skip)      //just the open Data function. 
                                                             //As I mentioned, I did not work 
                                                             //with unzip stuff, you have 
                                                             //to unzip the data before 
                                                             //by yourself
        {
            var file = File.ReadAllBytes(path).Skip(skip).ToArray();
            return file;
        }

        //Hint: First, I was working by opening the data locally and 
        //I wanted to figure it out how to present data to the arrays. 
        //So you can use something like this and call this within the TrainModel() function:
        //x_train = openDatas(@"PATH\OF\YOUR\DATAS\train-images-idx3-ubyte", 16);
        //y_train = openDatas(@"PATH\OF\YOUR\DATAS\train-labels-idx1-ubyte", 8);
        //x_test = openDatas(@"PATH\OF\YOUR\DATAS\t10k-images-idx3-ubyte", 16);
        //y_test = openDatas(@"PATH\OF\YOUR\DATAS\t10k-labels-idx1-ubyte", 8);
    }
}