编码器/解码器从encog中的autoencoder中翻录出来

我在Encog中创建并学习了自动编码器，我尝试将其分成几部分：编码器和解码器部分 . 不幸的是我无法得到它并且我不断得到奇怪的不正确的数据（比较从一次网络应用到数据和两次数据 - > enc - > dec的结果） .

我试图用简单的GetWeight和SetWeight来制作它，但结果不正确 . 在encog文档中找到的解决方案 - 初始化平面网络对我来说不清楚（我无法让它工作） .

public static BasicNetwork getEncoder(BasicNetwork net)
        {
            var enc = new BasicNetwork();
            enc.AddLayer(new BasicLayer(null, true, net.GetLayerNeuronCount(0)));
            enc.AddLayer(new BasicLayer(new ActivationSigmoid(), true, net.GetLayerNeuronCount(1)));
            enc.AddLayer(new BasicLayer(new ActivationSigmoid(), false, net.GetLayerNeuronCount(2)));
            enc.Structure.FinalizeStructure ();

            var weights1 = net.Structure.Flat.Weights;
            var weights2 = enc.Structure.Flat.Weights;
            var idx1 = net.Structure.Flat.WeightIndex;
            var idx2 = enc.Structure.Flat.WeightIndex;

            for(var i = 0; i < 1; i++)
            {
                int n = net.GetLayerNeuronCount(i);
                int m = net.GetLayerNeuronCount(i + 1);

                Console.WriteLine("Decoder: {0} - {1}", n, m);

                for(var j = 0; j < n; j++)
                {
                    for(var k = 0; k < m; k++)
                    {
                        weights1 [idx1[i] + j * m + k] = weights2 [idx2[i] + j * m * k];
                    }
                }
            }


            return enc;
        }

AutoEncoder的完全旧的（设置/获取权重）代码：

using System;
using Encog.Engine.Network.Activation;
using Encog.ML.Data;
using Encog.ML.Data.Basic;
using Encog.ML.Train;
using Encog.Neural.Networks;
using Encog.Neural.Networks.Layers;
using Encog.Neural.Networks.Training.Propagation.Resilient;

namespace engine
{
    public class AutoEncoder
    {
        private int k = 0;
        public IMLDataSet trainingSet
        {
            get;
            set;
        }

        public AutoEncoder(int k)
        {
            this.k = k;
        }

        public static BasicNetwork getDecoder(BasicNetwork net)
        {
            var dec = new BasicNetwork();
            dec.AddLayer(new BasicLayer(null, true, net.GetLayerNeuronCount(1)));
            dec.AddLayer(new BasicLayer(new ActivationSigmoid(), true, net.GetLayerNeuronCount(2)));

            dec.Structure.FinalizeStructure();

            for(var i = 1; i < 2; i++)
            {
                int n = net.GetLayerNeuronCount(i);
                int m = net.GetLayerNeuronCount(i + 1);

                Console.WriteLine("Decoder: {0} - {1}", n, m);

                for(var j = 0; j < n; j++)
                {
                    for(var k = 0; k < m; k++)
                    {
                        dec.SetWeight(i - 1, j, k, net.GetWeight(i, j, k));
                    }
                }
            }

            return dec;
        }

        public static BasicNetwork getEncoder(BasicNetwork net)
        {
            var enc = new BasicNetwork();
            enc.AddLayer(new BasicLayer(null, true, net.GetLayerNeuronCount(0)));
            enc.AddLayer(new BasicLayer(new ActivationSigmoid(), true, net.GetLayerNeuronCount(1)));

            enc.Structure.FinalizeStructure();

            for(var i = 0; i < 1; i++)
            {
                int n = net.GetLayerNeuronCount(i);
                int m = net.GetLayerNeuronCount(i + 1);

                Console.WriteLine("Encoder: {0} - {1}", n, m);

                for(var j = 0; j < n; j++)
                {
                    for(var k = 0; k < m; k++)
                    {
                        enc.SetWeight(i, j, k, net.GetWeight(i, j, k));
                    }
                }
            }

            return enc;
        }

        public BasicNetwork learn(double[][] data,
            double eps = 1e-6,
            long trainMaxIter = 10000)
        {
            int n = data.Length;
            int m = data[0].Length;
            double[][] output = new double[n][];
            for(var i = 0; i < n; i++)
            {
                output[i] = new double[m];
                data[i].CopyTo(output[i], 0);
            }

            var network = new BasicNetwork();
            network.AddLayer(new BasicLayer(null, true, m));
            network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, k));
            network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, m));
            network.Structure.FinalizeStructure();
            network.Reset();

            trainingSet = new BasicMLDataSet(data, output);
            IMLTrain train = new ResilientPropagation(network, trainingSet);

            int epoch = 1;
            do
            {
                train.Iteration();
                Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
                epoch++;
            } while(train.Error > eps && epoch < trainMaxIter);

            train.FinishTraining();

            return network;
        }
    }
}

如何才能正确地从编码器的自动编码器和最后一层中的两个第一层切换到解码器？