From c4d5f23cc81981da26ed6bfefb881c0397dee79e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Radek=20Pu=C5=A1?= <pusradek@fit.cvut.cz>
Date: Sat, 2 Nov 2019 00:41:47 +0100
Subject: [PATCH] save to DB functionality for neural network
---
Core/Core/Data/Model.cs | 25 ++++--
Core/Core/Models/Network.cs | 37 ++++++++
.../NeuralNetNamespace/Layers/ILayer.cs | 2 +
.../NeuralNetNamespace/Layers/InputLayer.cs | 10 +++
.../NeuralNetNamespace/Layers/Layer.cs | 14 +++
.../NeuralNetNamespace/NeuralNet.cs | 33 +++++++
.../NeuralNetNamespace/Neurons/INeuron.cs | 3 +
.../NeuralNetNamespace/Neurons/Neuron.cs | 15 ++--
Core/NeuralNetwork/Program.cs | 89 +++++++++++++++++--
9 files changed, 209 insertions(+), 19 deletions(-)
create mode 100644 Core/Core/Models/Network.cs
diff --git a/Core/Core/Data/Model.cs b/Core/Core/Data/Model.cs
index 75623b0..cbc2cd2 100644
--- a/Core/Core/Data/Model.cs
+++ b/Core/Core/Data/Model.cs
@@ -32,16 +32,18 @@ namespace Core.Data
public DbSet<ForeignPayment> ForeignPayments { get; set; }
public DbSet<Repetition> Repetitions { get; set; }
+ public DbSet<Network> Networks { get; set; }
protected override void OnModelCreating(ModelBuilder modelBuilder)
{
- modelBuilder.Entity<Expense>().ToTable("Expense");
+ SetRelations(modelBuilder);
+ SetTableNames(modelBuilder);
+ base.OnModelCreating(modelBuilder);
+ }
- modelBuilder.Entity<Account>().ToTable("Account");
- modelBuilder.Entity<ConstantSymbol>().ToTable("ConstantSymbol");
- modelBuilder.Entity<Person>().ToTable("Person");
- //modelBuilder.Entity<Transaction>().ToTable("Transaction");
+ private void SetRelations(ModelBuilder modelBuilder)
+ {
modelBuilder.Entity<Transaction>()
.HasOne(t => t.RecipientAccount)
.WithMany(a => a.RecipientTransactions)
@@ -56,8 +58,15 @@ namespace Core.Data
.OnDelete(DeleteBehavior.Restrict);
modelBuilder.Entity<User>().HasIndex(u => u.Username).IsUnique();
- modelBuilder.Entity<User>().ToTable("Users");
+ }
+ private void SetTableNames(ModelBuilder modelBuilder)
+ {
+ modelBuilder.Entity<Expense>().ToTable("Expense");
+ modelBuilder.Entity<Account>().ToTable("Account");
+ modelBuilder.Entity<ConstantSymbol>().ToTable("ConstantSymbol");
+ modelBuilder.Entity<Person>().ToTable("Person");
+ modelBuilder.Entity<User>().ToTable("Users");
modelBuilder.Entity<AccountType>().ToTable("AccountType");
modelBuilder.Entity<TransactionType>().ToTable("TransactionType");
modelBuilder.Entity<Kind>().ToTable("Kind");
@@ -65,10 +74,8 @@ namespace Core.Data
modelBuilder.Entity<MinistryPredefined>().ToTable("MinistryPredefined");
modelBuilder.Entity<ReservedSymbol>().ToTable("ReservedSymbol");
modelBuilder.Entity<ForeignPayment>().ToTable("ForeignPayment");
-
modelBuilder.Entity<Repetition>().ToTable("Repetition");
-
- base.OnModelCreating(modelBuilder);
+ modelBuilder.Entity<Network>().ToTable("Network");
}
}
}
diff --git a/Core/Core/Models/Network.cs b/Core/Core/Models/Network.cs
new file mode 100644
index 0000000..7ae69fa
--- /dev/null
+++ b/Core/Core/Models/Network.cs
@@ -0,0 +1,37 @@
+using Core.Data;
+using System;
+using System.Collections.Generic;
+using System.ComponentModel.DataAnnotations;
+using System.Linq;
+using System.Threading.Tasks;
+
+namespace Core.Models
+{
+ public class Network
+ {
+ [Key]
+ public long ID { get; set; }
+ public ICollection<Layer> Layer { get; set; }
+ }
+
+ public class Layer
+ {
+ [Key]
+ public long ID { get; set; }
+ public ICollection<Neuron> Neuron { get; set; }
+ }
+
+ public class Neuron
+ {
+ [Key]
+ public long ID { get; set; }
+ public ICollection<Weight> Weight { get; set; }
+ }
+
+ public class Weight
+ {
+ [Key]
+ public long ID { get; set; }
+ public double Value { get; set; }
+ }
+}
diff --git a/Core/NeuralNetwork/NeuralNetNamespace/Layers/ILayer.cs b/Core/NeuralNetwork/NeuralNetNamespace/Layers/ILayer.cs
index b0b5a74..4e9d824 100644
--- a/Core/NeuralNetwork/NeuralNetNamespace/Layers/ILayer.cs
+++ b/Core/NeuralNetwork/NeuralNetNamespace/Layers/ILayer.cs
@@ -14,5 +14,7 @@ namespace NeuralNetwork.NeuralNetNamespace.Layers
void Evaluate(DenseVector inputVector);
void Propagate(DenseVector error);
void AdjustWeights();
+ List<List<double>> Export();
+ void Import(List<List<double>> list);
}
}
diff --git a/Core/NeuralNetwork/NeuralNetNamespace/Layers/InputLayer.cs b/Core/NeuralNetwork/NeuralNetNamespace/Layers/InputLayer.cs
index 98c506d..6b96ea9 100644
--- a/Core/NeuralNetwork/NeuralNetNamespace/Layers/InputLayer.cs
+++ b/Core/NeuralNetwork/NeuralNetNamespace/Layers/InputLayer.cs
@@ -37,6 +37,16 @@ namespace NeuralNetwork.NeuralNetNamespace.Layers
NextLayer.Evaluate(inputVector);
}
+ public List<List<double>> Export()
+ {
+ return new List<List<double>> { new List<double> { Size } };
+ }
+
+ public void Import(List<List<double>> list)
+ {
+ throw new NotImplementedException();
+ }
+
public void Propagate(DenseVector error)
{
return;
diff --git a/Core/NeuralNetwork/NeuralNetNamespace/Layers/Layer.cs b/Core/NeuralNetwork/NeuralNetNamespace/Layers/Layer.cs
index 085f199..274d74a 100644
--- a/Core/NeuralNetwork/NeuralNetNamespace/Layers/Layer.cs
+++ b/Core/NeuralNetwork/NeuralNetNamespace/Layers/Layer.cs
@@ -74,5 +74,19 @@ namespace NeuralNetwork.NeuralNetNamespace.Layers
if (PrevLayer != null)
PrevLayer.Propagate(layerError);
}
+
+ public List<List<double>> Export()
+ {
+ List<List<double>> layer = new List<List<double>> (Neurons.Count);
+ for (int i = 0; i < Neurons.Count; i++)
+ layer.Add(Neurons[i].Export());
+ return layer;
+ }
+
+ public void Import(List<List<double>> layers)
+ {
+ for (int i = 0; i < layers.Count; i++)
+ Neurons[i].Import(layers[i]);
+ }
}
}
diff --git a/Core/NeuralNetwork/NeuralNetNamespace/NeuralNet.cs b/Core/NeuralNetwork/NeuralNetNamespace/NeuralNet.cs
index 2086545..724cb03 100644
--- a/Core/NeuralNetwork/NeuralNetNamespace/NeuralNet.cs
+++ b/Core/NeuralNetwork/NeuralNetNamespace/NeuralNet.cs
@@ -16,6 +16,8 @@ namespace NeuralNetwork.NeuralNetNamespace
public NeuralNet CreateEmpty(List<int> layerSizes)
{
+ layers = new List<ILayer>(layerSizes.Count);
+
ILayer prevLayer = new InputLayer(layerSizes[0]);
layers.Add(prevLayer);
@@ -35,6 +37,37 @@ namespace NeuralNetwork.NeuralNetNamespace
return this;
}
+ /// <summary>
+ /// weights for neurons in corresponding layers
+ /// (net 1:N > layer 1:N > neuron 1:N > weight)
+ /// </summary>
+ /// <returns>weigts for individual neurons (+layers)</returns>
+ public List<List<List<double>>> ExportNet()
+ {
+ List<List<List<double>>> net = new List<List<List<double>>>(layers.Count);
+ for (int i = 0; i < layers.Count; i++)
+ net.Add(layers[i].Export());
+
+ return net;
+ }
+
+ public void Import(List<List<List<double>>> net)
+ {
+ //instance initialization first
+ List<int> shape = new List<int>(net.Count);
+
+ shape.Add((int)net[0][0][0]);
+ for (int i = 1; i < net.Count; i++)
+ shape.Add(net[i].Count);
+
+ CreateEmpty(shape);
+
+ //fill with data
+
+ for (int i = 1; i < net.Count; i++)
+ layers[i].Import(net[i]);
+ }
+
public double LastResult { get; private set; }
public bool EvaluateInput(List<double> input)
diff --git a/Core/NeuralNetwork/NeuralNetNamespace/Neurons/INeuron.cs b/Core/NeuralNetwork/NeuralNetNamespace/Neurons/INeuron.cs
index 8703e11..82ebadf 100644
--- a/Core/NeuralNetwork/NeuralNetNamespace/Neurons/INeuron.cs
+++ b/Core/NeuralNetwork/NeuralNetNamespace/Neurons/INeuron.cs
@@ -12,5 +12,8 @@ namespace NeuralNetwork.NeuralNetNamespace.Neurons
DenseVector Propagate(double error);
void AdjustWeights();
double Output { get; }
+
+ List<double> Export();
+ void Import(List<double> list);
}
}
diff --git a/Core/NeuralNetwork/NeuralNetNamespace/Neurons/Neuron.cs b/Core/NeuralNetwork/NeuralNetNamespace/Neurons/Neuron.cs
index d8c36f2..7bad697 100644
--- a/Core/NeuralNetwork/NeuralNetNamespace/Neurons/Neuron.cs
+++ b/Core/NeuralNetwork/NeuralNetNamespace/Neurons/Neuron.cs
@@ -7,7 +7,6 @@ using System.Text;
namespace NeuralNetwork.NeuralNetNamespace.Neurons
{
- //TODO: Abs() pro WeightTotal - buÄŹ ano nebo ne
//TODO: komentáře
//TODO: dekompozice
class Neuron : INeuron
@@ -21,10 +20,6 @@ namespace NeuralNetwork.NeuralNetNamespace.Neurons
private int PropagationIterator;
public DenseVector Weights { get; private set; }
- /// <summary>
- /// sum of all weights in Weights vector
- /// </summary>
- private double WeightTotal;
private DenseVector Corrections;
@@ -108,5 +103,15 @@ namespace NeuralNetwork.NeuralNetNamespace.Neurons
return $"({Output.ToString()} # {str})";
}
+
+ public List<double> Export()
+ {
+ return new List<double>(Weights.ToArray());
+ }
+
+ public void Import(List<double> list)
+ {
+ Weights.SetValues(list.ToArray());
+ }
}
}
diff --git a/Core/NeuralNetwork/Program.cs b/Core/NeuralNetwork/Program.cs
index 04c9d66..bd7ff27 100644
--- a/Core/NeuralNetwork/Program.cs
+++ b/Core/NeuralNetwork/Program.cs
@@ -9,7 +9,85 @@ namespace NeuralNetwork
private static int startSize;
static void Main(string[] args)
{
- XORGenerator();
+ NeuralNet ann = TestTrain(infinite: false);
+
+ Console.WriteLine("evaluate trained:");
+ EvaluationTest(ann);
+
+ Console.WriteLine("evaluate trained 2:");
+ EvaluationTest(ann);
+
+ Console.WriteLine("exporting:");
+ List<List<List<double>>> export = ann.ExportNet();
+ Console.WriteLine("done");
+
+ Console.WriteLine("evaluate trained 3:");
+ EvaluationTest(ann);
+
+ Console.WriteLine("importing to new net:");
+ NeuralNet import = new NeuralNet();
+ import.Import(export);
+ Console.WriteLine("done");
+
+ Console.WriteLine("test:");
+ EvaluationTest(import);
+ Console.WriteLine("done");
+
+ Console.WriteLine("importing to old net:");
+ ann.Import(export);
+ Console.WriteLine("done");
+
+ Console.WriteLine("test:");
+ EvaluationTest(ann);
+ Console.WriteLine("done");
+ }
+
+ private static KeyValuePair<bool, List<double>> GetGenerator()
+ {
+ return OddEvenGenerator();
+ }
+
+ private static void EvaluationTest(NeuralNet ann)
+ {
+ int passed = 0;
+ int failed = 0;
+ double yes = 0;
+ double predictionYes = 0;
+ double no = 0;
+ double predictionNo = 0;
+ const int iterations=1000;
+ for (int i = 0; i < iterations; i++)
+ {
+ var data = GetGenerator();
+ bool guess = ann.EvaluateInput(data.Value);
+ bool result = data.Key;
+
+ if (guess == result)
+ passed++;
+ else
+ failed++;
+
+ if (result)
+ yes++;
+ else
+ no++;
+
+ if (guess)
+ predictionYes++;
+ else
+ predictionNo++;
+ }
+
+ double localResult = ((double)passed / iterations) * 100;
+ Console.WriteLine($"succes rate: {localResult}% (p:{passed} vs. f:{failed})");
+ Console.WriteLine($"Actualyes: {yes}, Actualno:{no}");
+ Console.WriteLine($"predictionYes: {predictionYes}, predictionNo:{predictionNo}");
+ Console.WriteLine("_-_-_-_-_-_-_-_-_-_-_-_-_");
+ }
+
+ private static NeuralNet TestTrain(bool infinite)
+ {
+ GetGenerator();
List<int> widths = new List<int>() { startSize, 3, 3, 1 };
NeuralNet ANN = new NeuralNet().CreateEmpty(widths);
@@ -29,9 +107,9 @@ namespace NeuralNetwork
const int adjust = 100;
//const int step = 20;
//const int adjust = 3;
- for (int i = 0; true || i < 1000; i++)
+ for (int i = 0; infinite || i < 10000; i++)
{
- var data = XORGenerator();
+ var data = GetGenerator();
bool guess = ANN.EvaluateInput(data.Value);
bool result = data.Key;
@@ -54,7 +132,7 @@ namespace NeuralNetwork
if (i % adjust == 0 && i > adjust)
ANN.AdjustWeights();
- if (i % step == 0)
+ if (i>1 && i % step == 0)
{
Console.WriteLine(ANN.ToString());
Console.WriteLine("_-_-_-_-_-_-_-_-_-_-_-_-_");
@@ -73,7 +151,8 @@ namespace NeuralNetwork
}
}
- Console.WriteLine("Hello World!");
+
+ return ANN;
}
private static KeyValuePair<bool, List<double>> XORGenerator()
--
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