From cc0c9bf0c7b3e73cdb9baf7921046559a7dcf32c Mon Sep 17 00:00:00 2001
From: Kamal Saleh <kamal.saleh@web.de>
Date: Mon, 8 Jul 2024 16:03:03 +0200
Subject: [PATCH] test neural networks

---
 tst/data-1.txt           |  102 ++
 tst/data-2.txt           | 3002 ++++++++++++++++++++++++++++++++++++++
 tst/neural-network-1.tst |   66 +
 tst/neural-network-2.tst |   26 +
 4 files changed, 3196 insertions(+)
 create mode 100644 tst/data-1.txt
 create mode 100644 tst/data-2.txt
 create mode 100644 tst/neural-network-1.tst
 create mode 100644 tst/neural-network-2.tst

diff --git a/tst/data-1.txt b/tst/data-1.txt
new file mode 100644
index 0000000..c35b54f
--- /dev/null
+++ b/tst/data-1.txt
@@ -0,0 +1,102 @@
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diff --git a/tst/data-2.txt b/tst/data-2.txt
new file mode 100644
index 0000000..7c857a3
--- /dev/null
+++ b/tst/data-2.txt
@@ -0,0 +1,3002 @@
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+[ -0.4, 0.39, 0, 0, 1, 0 ],
+[ -0.47, 0.16, 0, 0, 1, 0 ],
+[ 0.6900000000000001, -0.09, 1, 0, 0, 0 ],
+[ 0.46, -0.22, 1, 0, 0, 0 ],
+[ -0.52, 0.76, 0, 0, 1, 0 ],
+[ -0.72, 1., 1, 0, 0, 0 ],
+[ 0.42, 0.6, 0, 1, 0, 0 ],
+[ -0.15, -0.34, 0, 0, 0, 1 ],
+[ -0.75, -0.8100000000000001, 1, 0, 0, 0 ],
+[ -0.78, -0.38, 1, 0, 0, 0 ],
+[ -0.09, 0.17, 1, 0, 0, 0 ],
+[ 0.84, 0.03, 1, 0, 0, 0 ],
+[ -0.8300000000000001, -0.16, 1, 0, 0, 0 ],
+[ 0.97, -0.52, 1, 0, 0, 0 ],
+[ 0.59, 0.53, 0, 1, 0, 0 ],
+[ -0.27, -0.55, 1, 0, 0, 0 ],
+[ 0.75, 0.14, 0, 1, 0, 0 ],
+[ 0.38, -0.84, 1, 0, 0, 0 ],
+[ 0.88, 0.92, 1, 0, 0, 0 ],
+[ -0.01, -0.71, 0, 0, 0, 1 ],
+[ -0.5700000000000001, 0.12, 0, 0, 1, 0 ],
+[ 0.02, -0.88, 0, 0, 0, 1 ],
+[ 0.16, -0.08, 0, 0, 0, 1 ],
+[ -0.08, 0.9400000000000001, 1, 0, 0, 0 ],
+[ -0.61, -0.17, 1, 0, 0, 0 ],
+[ 0.39, 0.6900000000000001, 0, 1, 0, 0 ],
+[ 0.12, 0.01, 1, 0, 0, 0 ],
+[ -0.16, -0.68, 1, 0, 0, 0 ],
+[ 0.85, 0.55, 0, 1, 0, 0 ],
+[ -0.03, -0.66, 0, 0, 0, 1 ],
+[ 0.41, 0.11, 0, 1, 0, 0 ],
+[ -0.38, 0.8200000000000001, 0, 0, 1, 0 ],
+[ -0.73, 0.93, 1, 0, 0, 0 ],
+[ 0.17, -0.33, 0, 0, 0, 1 ],
+[ 0.88, 0.63, 0, 1, 0, 0 ],
+[ -0.27, -0.38, 0, 0, 0, 1 ],
+[ -0.5700000000000001, 0.47, 0, 0, 1, 0 ],
+[ 0.46, 1., 1, 0, 0, 0 ],
+[ -0.48, -0.6900000000000001, 1, 0, 0, 0 ],
+[ -0.85, 0.8200000000000001, 1, 0, 0, 0 ],
+[ -0.28, 0.05, 1, 0, 0, 0 ],
+[ -0.96, 0.21, 1, 0, 0, 0 ],
+[ -0.79, 0.63, 0, 0, 1, 0 ],
+[ -0.5600000000000001, 0.02, 1, 0, 0, 0 ],
+[ -0.72, 0.9500000000000001, 1, 0, 0, 0 ],
+[ -0.17, 0.9400000000000001, 1, 0, 0, 0 ],
+[ 0.39, -0.48, 1, 0, 0, 0 ],
+[ -0.54, 0.68, 0, 0, 1, 0 ],
+[ 0.9, -0.37, 1, 0, 0, 0 ],
+[ 0.28, 0.79, 0, 1, 0, 0 ],
+[ 0.09, -0.9400000000000001, 1, 0, 0, 0 ],
+[ -0.1, 0.18, 1, 0, 0, 0 ],
+[ 0.54, -0.79, 1, 0, 0, 0 ],
+[ 0.34, 0.51, 0, 1, 0, 0 ],
+[ -0.33, 0.13, 0, 0, 1, 0 ],
+[ -0.7000000000000001, 0.71, 0, 0, 1, 0 ],
+[ 0.01, 0.9400000000000001, 1, 0, 0, 0 ],
+[ -0.18, 0.79, 0, 0, 1, 0 ],
+[ 0.73, 0.92, 1, 0, 0, 0 ],
+[ 0.86, 0.63, 0, 1, 0, 0 ],
+[ -0.91, -0.26, 1, 0, 0, 0 ],
+]
\ No newline at end of file
diff --git a/tst/neural-network-1.tst b/tst/neural-network-1.tst
new file mode 100644
index 0000000..584702b
--- /dev/null
+++ b/tst/neural-network-1.tst
@@ -0,0 +1,66 @@
+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;;
+gap> one_epoch_update := OneEpochUpdateLens( f, optimizer, training_examples_path, batch_size );;
+gap> w := [ 1, 0, 0, 0, 0, 0, 0, 0.21, -0.31, 0.7 ];;
+gap> nr_epochs := 50;;
+gap> w := Fit( one_epoch_update, nr_epochs, w );
+Epoch  0/50 - loss = 4.4578023668000011
+Epoch  1/50 - loss = 1.0912498874689163
+Epoch  2/50 - loss = 0.44837221398566263
+Epoch  3/50 - loss = 0.24595770628142952
+Epoch  4/50 - loss = 0.15657057365631694
+Epoch  5/50 - loss = 0.10826332558680457
+Epoch  6/50 - loss = 0.078775390947121549
+Epoch  7/50 - loss = 0.05933834979231474
+Epoch  8/50 - loss = 0.045840095625750206
+Epoch  9/50 - loss = 0.036104869750419565
+Epoch 10/50 - loss = 0.028879339234755188
+Epoch 11/50 - loss = 0.023394659237038019
+Epoch 12/50 - loss = 0.019155660355490118
+Epoch 13/50 - loss = 0.015830769241799054
+Epoch 14/50 - loss = 0.013190764093109656
+Epoch 15/50 - loss = 0.011072910169532824
+Epoch 16/50 - loss = 0.0093590480803782839
+Epoch 17/50 - loss = 0.0079617187736609248
+Epoch 18/50 - loss = 0.0068151039311510888
+Epoch 19/50 - loss = 0.0058689511596490664
+Epoch 20/50 - loss = 0.0050844046715407716
+Epoch 21/50 - loss = 0.0044310843614971257
+Epoch 22/50 - loss = 0.0038850017730287646
+Epoch 23/50 - loss = 0.0034270487022568063
+Epoch 24/50 - loss = 0.0030418848966412781
+Epoch 25/50 - loss = 0.0027171085539510805
+Epoch 26/50 - loss = 0.0024426302526743592
+Epoch 27/50 - loss = 0.0022101952388165077
+Epoch 28/50 - loss = 0.0020130152675700576
+Epoch 29/50 - loss = 0.0018454822805761821
+Epoch 30/50 - loss = 0.0017029438615871223
+Epoch 31/50 - loss = 0.0015815257852121037
+Epoch 32/50 - loss = 0.0014779907885363139
+Epoch 33/50 - loss = 0.0013896254374855411
+Epoch 34/50 - loss = 0.0013141489527016307
+Epoch 35/50 - loss = 0.0012496393231695038
+Epoch 36/50 - loss = 0.0011944731209836026
+Epoch 37/50 - loss = 0.0011472762425819396
+Epoch 38/50 - loss = 0.0011068834144712385
+Epoch 39/50 - loss = 0.0010723047675106358
+Epoch 40/50 - loss = 0.001042698140982218
+Epoch 41/50 - loss = 0.0010173460533340986
+Epoch 42/50 - loss = 0.00099563649065474344
+Epoch 43/50 - loss = 0.00097704683139054871
+Epoch 44/50 - loss = 0.00096113035752483963
+Epoch 45/50 - loss = 0.00094750490661015734
+Epoch 46/50 - loss = 0.00093584330188459025
+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 ]
diff --git a/tst/neural-network-2.tst b/tst/neural-network-2.tst
new file mode 100644
index 0000000..1bdf16d
--- /dev/null
+++ b/tst/neural-network-2.tst
@@ -0,0 +1,26 @@
+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;;
+gap> one_epoch_update := OneEpochUpdateLens( f, optimizer, training_examples_path, batch_size );;
+gap> w1 := [[-0.61789644,  0.56407845, -0.5965204,  -0.85063416,  0.770488  ], [-0.13079625,  0.47618425,  0.8807312,   0.24377191,  0.18529081], [0.,           0.,          0.,          0.,          0.        ] ];;
+gap> w1 := Concatenation( TransposedMat( w1 ) );;
+gap> w2 := [[-0.52913845,  0.524745,    0.67446196, -0.13036567, -0.5108599 ], [-0.12336099,  0.7475884,  -0.18031466,  0.30409217, -0.5017855 ], [-0.5523451,   0.74021363, -0.38746935, -0.2771675,   0.6162708 ], [-0.24399745,  0.523523,    0.31327105, -0.5376833,  -0.4945482 ], [ 0.33063114, -0.10083395,  0.13537377,  0.671383,   -0.2012583 ], [ 0.,          0.,          0.,          0.,          0.        ] ];;
+gap> w2 := Concatenation( TransposedMat( w2 ) );;
+gap> w3 := [[-0.05885905, -0.81396204,  0.00370395, -0.42547446], [-0.39928403,  0.56314194,  0.6614479 ,  0.5060446 ], [ 0.6662301,  -0.2800727 ,  0.1187852 , -0.27065504], [ 0.15874296, -0.6039741 , -0.7533438 , -0.33242884], [ 0.26578736, -0.45036432, -0.61879224,  0.8060001 ], [ 0.,          0.,          0.,          0.,       ] ];;
+gap> w3 := Concatenation( TransposedMat( w3 ) );;
+gap> w := Concatenation( [ w3, w2, w1 ] );;
+gap> nr_epochs := 3;;
+gap> w := Fit( one_epoch_update, nr_epochs, w );;
+Epoch  0/3 - loss = 0.36090265698232782
+Epoch  1/3 - loss = 0.21968044680244986
+Epoch  2/3 - loss = 0.17883151112398918
+Epoch  3/3 - loss = 0.15935373922266871