Merge pull request #6 from kamalsaleh/master

Update methods for creating and fitting neural networks

Author: kamalsaleh

PackageInfo.g

@@ -10,7 +10,7 @@ SetPackageInfo( rec(
 
 PackageName := "MachineLearningForCAP",
 Subtitle := "Exploring categorical machine learning in CAP",
-Version := "2024.07-15",
+Version := "2024.07-17",
 Date := (function ( ) if IsBound( GAPInfo.SystemEnvironment.GAP_PKG_RELEASE_DATE ) then return GAPInfo.SystemEnvironment.GAP_PKG_RELEASE_DATE; else return Concatenation( ~.Version{[ 1 .. 4 ]}, "-", ~.Version{[ 6, 7 ]}, "-01" ); fi; end)( ),
 License := "GPL-2.0-or-later",
 

gap/CategoryOfParametrisedMorphisms.gi

@@ -304,6 +304,16 @@ InstallMethod( ReparametriseMorphism,
 
 end );
 
+##
+InstallOtherMethod( Eval,
+        [ IsMorphismInCategoryOfParametrisedMorphisms, IsDenseList ],
+  
+  function( f, pair )
+    
+    return Eval( UnderlyingMorphism( f ), Concatenation( pair ) );
+    
+end );
+
 ##
 InstallMethod( NaturalEmbeddingIntoCategoryOfParametrisedMorphisms,
         [ IsCapCategory, IsCategoryOfParametrisedMorphisms ],

gap/FitParameters.gi

@@ -139,20 +139,22 @@ InstallMethod( Fit,
 
     l_n := Length( String( n ) );
 
-    for i in [ 0 .. n ] do
+    Print( "Epoch ", JoinStringsWithSeparator( ListWithIdenticalEntries( l_n - 1, " " ), "" ), "0/", String( n ), " - loss = ", String( get( w )[1] ), "\n" );
+    
+    for i in [ 1 .. n ] do
 
         str_i := String( i );
 
         l_i := Length( str_i );
 
         spaces := JoinStringsWithSeparator( ListWithIdenticalEntries( l_n - l_i, " " ), "" );
 
+        w := put( w );
+        
         loss := get( w );
 
         Print( "Epoch ", spaces, String( i ), "/", String( n ), " - loss = ", String( loss[1] ), "\n" );
 
-        w := put( w );
-        
         #Display( w );
 
     od;

gap/NeuralNetworks.gi

@@ -15,8 +15,6 @@ InstallMethod( LogitsMorphismOfNeuralNetwork,
 
     N := Length( dims );
 
-    Print( "The total number of layers is ", String( N ), "\n\n" );
-    
     L := [ ];
 
     for i in [ 1 .. N - 1 ] do
@@ -25,8 +23,6 @@ InstallMethod( LogitsMorphismOfNeuralNetwork,
 
         P_i := UnderlyingObject( l_i );
 
-        Print( "Creating a morphism from layer ", String( i ), " to ", String( i + 1 ), " with ", String( RankOfObject( P_i ) ), " parameters\n" );
-        
         Add( L, l_i );
 
         if i <> N - 1 then
@@ -37,8 +33,6 @@ InstallMethod( LogitsMorphismOfNeuralNetwork,
 
     od;
 
-    Print( "\n" );
-    
     return PreComposeList( Para, L );
 
 end );

tst/functor.tst

@@ -4,13 +4,10 @@ gap> Smooth := SkeletalSmoothMaps;;
 gap> Lenses := CategoryOfLenses( Smooth );;
 gap> Para := CategoryOfParametrisedMorphisms( Smooth );;
 gap> Para_Lenses := CategoryOfParametrisedMorphisms( Lenses );;
-gap> f := LossMorphismOfNeuralNetwork( Para, 2, [], 1, "IdFunc" );;
-The total number of layers is 2
-
-Creating a morphism from layer 1 to 2 with 3 parameters
+gap> ell := LossMorphismOfNeuralNetwork( Para, 2, [], 1, "IdFunc" );;
 gap> dummy_input := ConvertToExpressions( [ "w1", "w2", "b1", "x1", "x2", "y" ] );
 [ w1, w2, b1, x1, x2, y ]
-gap> Display( f : dummy_input := dummy_input );
+gap> Display( ell : dummy_input := dummy_input );
 ℝ^3 -> ℝ^1 defined by:
 
 Parameter Object:
@@ -24,7 +21,7 @@ Parametrised Morphism:
 ‣ (w1 * x1 + w2 * x2 + b1 - y) ^ 2 / 1
 gap> R := EmbeddingIntoCategoryOfParametrisedMorphisms( Para, Para_Lenses );
 Embedding into category of parametrised morphisms
-gap> Rf := ApplyFunctor( R, f );
+gap> Rf := ApplyFunctor( R, ell );
 (ℝ^3, ℝ^3) -> (ℝ^1, ℝ^1) defined by:
 
 Parameter Object:

tst/neural-network-1.tst

@@ -2,9 +2,6 @@ gap> Smooth := SkeletalSmoothMaps;;
 gap> Lenses := CategoryOfLenses( Smooth );;
 gap> Para := CategoryOfParametrisedMorphisms( Smooth );;
 gap> f := LossMorphismOfNeuralNetwork( Para, 2, [], 1, "IdFunc" );;
-The total number of layers is 2
-
-Creating a morphism from layer 1 to 2 with 3 parameters
 gap> optimizer := Lenses.AdamOptimizer( );;
 gap> training_examples_path := SelectBasedOnCondition( IsExistingFile( "data-1.txt" ), "data-1.txt", "tst/data-1.txt" );;
 gap> batch_size := 5;;
@@ -63,4 +60,4 @@ Epoch 47/50 - loss = 0.00092586526390837627
 Epoch 48/50 - loss = 0.00091733056032563621
 Epoch 49/50 - loss = 0.00091003319324738866
 Epoch 50/50 - loss = 0.00090379645850528724
-[ 1021, -0.00236067, -0.00633157, 0.000258869, 0.0135747, 0.0500079, 0.0310695, 2.00197, -2.99162, 0.997524 ]
+[ 1001, -0.0024867, -0.00684601, 0.000293851, 0.0138455, 0.051012, 0.0316896, 2.00229, -2.99088, 0.997374 ]

tst/neural-network-2.tst

@@ -2,11 +2,6 @@ gap> Smooth := SkeletalSmoothMaps;;
 gap> Lenses := CategoryOfLenses( Smooth );;
 gap> Para := CategoryOfParametrisedMorphisms( Smooth );;
 gap> f := LossMorphismOfNeuralNetwork( Para, 2, [ 5, 5 ], 4, "Softmax" );;
-The total number of layers is 4
-
-Creating a morphism from layer 1 to 2 with 15 parameters
-Creating a morphism from layer 2 to 3 with 30 parameters
-Creating a morphism from layer 3 to 4 with 24 parameters
 gap> optimizer := Lenses.GradientDescentOptimizer( : learning_rate := 0.01 );;
 gap> training_examples_path := SelectBasedOnCondition( IsExistingFile( "data-2.txt" ), "data-2.txt", "tst/data-2.txt" );;
 gap> batch_size := 1;;