diff --git a/scripts/tmp/csnn/log_train.txt b/scripts/tmp/csnn/log_train.txt
deleted file mode 100644
index bddf5521dc63dcd31b66d0342acb7d7e7f797967..0000000000000000000000000000000000000000
--- a/scripts/tmp/csnn/log_train.txt
+++ /dev/null
@@ -1,111 +0,0 @@
-10:46:1 20/7/2023
-Random seed: 0
-
-Run start at 10:46:1 20/7/2023
-Input data [28, 28, 1]
-Train:
-#1: MNIST(/home/ggoupy/phd/SNN-PCN/datasets/mnist//X_train.bin, /home/ggoupy/phd/SNN-PCN/datasets/mnist//y_train.bin)
-Test:
-
-DefaultOnOffFilter 1: [28, 28, 2]
-Process.DefaultOnOffFilter {
- center_dev: 1.000000
- filter_size: 7
- surround_dev: 2.000000
-}
-
-FeatureScaling 2: [28, 28, 2]
-Process.FeatureScaling {
-}
-
-LatencyCoding 3: [28, 28, 2]
-InputConverter.LatencyCoding {
-}
-
-Convolution 4: conv1 [24, 24, 16]
-Layer.Convolution (conv1) {
- annealing: 0.950000
- epoch: 25
- filter_height: 5
- filter_number: 16
- filter_width: 5
- lr_th: 1.000000
- min_th: 2.000000
- padding_x: 0
- padding_y: 0
- stdp: STDP.Multiplicative {
- am: 0.100000
- ap: 0.100000
- beta: 1.000000
- }
- stride_x: 1
- stride_y: 1
- t_obj: 0.750000
- th: 5.000000 [16]
- w: Gaussian(mean: 0.500000, dev: 0.010000) [5, 5, 2, 16]
- wta_infer: 0
-}
-
-Pooling 5: pool1 [6, 6, 16]
-Layer.Pooling (pool1) {
- filter_height: 4
- filter_number: 16
- filter_width: 4
- padding_x: 0
- padding_y: 0
- stride_x: 4
- stride_y: 4
-}
-
-
-Output 1 of conv1 [24, 24, 16]: train-conv1
-OutputConverter.DefaultOutput {
- max: 1.000000
- min: 0.000000
-}
-
-Output 1, Analysis: 1
-Analysis.Coherence {
-}
-
-Output 2 of pool1 [6, 6, 16]: train-pool1
-OutputConverter.DefaultOutput {
- max: 1.000000
- min: 0.000000
-}
-
-Output 2, Analysis: 1
-Analysis.Activity {
-}
-
-Output 3 of pool1 [6, 6, 16]: train-pool1
-OutputConverter.SpikeTiming {
-}
-
-Output 3, Analysis: 1
-Analysis.SaveFeatureNumpy {
-}
-
-Load 60000 train samples from MNIST(/home/ggoupy/phd/SNN-PCN/datasets/mnist//X_train.bin, /home/ggoupy/phd/SNN-PCN/datasets/mnist//y_train.bin)
-Save trained parameters at /home/ggoupy/phd/SNN-PCN/scripts/tmp//csnn//model//Process.FeatureScaling/
-train-conv1, analysis Coherence:
-===Coherence===
-Mean weights: 0.245774
-------
-N: 120
-Min: 0.00311696
-Q1: 0.0992967
-Q2: 0.276067
-Q3: 0.465661
-Max: 0.870946
-
-Save trained parameters at /home/ggoupy/phd/SNN-PCN/scripts/tmp//csnn//model//Layer.Convolution.conv1/
-train-pool1, analysis Activity:
-===Activity===
-* train set:
-Sparsity: 0.439407
-Active unit: 53.5977%
-Quiet: 0%
-train-pool1, analysis SaveFeatureNumpy:
-Run end at 10:46:35 20/7/2023
-Duration: 0h 0m 34s
diff --git a/scripts/tmp/csnn/model/Layer.Convolution.conv1/thresholds.npy b/scripts/tmp/csnn/model/Layer.Convolution.conv1/thresholds.npy
deleted file mode 100644
index ad2ae7d546593a9d605f149e5227272f61462a19..0000000000000000000000000000000000000000
Binary files a/scripts/tmp/csnn/model/Layer.Convolution.conv1/thresholds.npy and /dev/null differ
diff --git a/scripts/tmp/csnn/model/Layer.Convolution.conv1/weights.npy b/scripts/tmp/csnn/model/Layer.Convolution.conv1/weights.npy
deleted file mode 100644
index fd5afb741abd495a95d5bc80372af7a38f9f2d34..0000000000000000000000000000000000000000
Binary files a/scripts/tmp/csnn/model/Layer.Convolution.conv1/weights.npy and /dev/null differ
diff --git a/scripts/tmp/csnn/model/Process.FeatureScaling/maxs.npy b/scripts/tmp/csnn/model/Process.FeatureScaling/maxs.npy
deleted file mode 100644
index d90563f45b6041768e6019dda25f7c9a70390ad8..0000000000000000000000000000000000000000
Binary files a/scripts/tmp/csnn/model/Process.FeatureScaling/maxs.npy and /dev/null differ
diff --git a/scripts/tmp/csnn/model/Process.FeatureScaling/mins.npy b/scripts/tmp/csnn/model/Process.FeatureScaling/mins.npy
deleted file mode 100644
index 46d74134f634ae44c6fcad920718f1036741b046..0000000000000000000000000000000000000000
Binary files a/scripts/tmp/csnn/model/Process.FeatureScaling/mins.npy and /dev/null differ
diff --git a/scripts/tmp/csnn/model/config.json b/scripts/tmp/csnn/model/config.json
deleted file mode 100644
index 91e0003c22c82292c174470ee77f16a1ad8b2e90..0000000000000000000000000000000000000000
--- a/scripts/tmp/csnn/model/config.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
- "dataset": "MNIST",
- "to_grayscale": false,
- "feature_scaling": true,
- "dog": [
- 7,
- 1,
- 2
- ],
- "conv1_epochs": 25,
- "conv1_c": 16,
- "conv1_k": 5,
- "conv1_w_init_mean": 0.5,
- "conv1_w_init_std": 0.01,
- "conv1_t_obj": 0.75,
- "conv1_th": 5,
- "conv1_min_th": 2,
- "conv1_lr_th": 1,
- "conv1_stdp": "multiplicative",
- "conv1_stdp_t": 0.1,
- "conv1_stdp_b": 1,
- "conv1_stdp_lr": 0.1,
- "conv1_annealing": 0.95,
- "conv1_wta_infer": false,
- "pool1_size": 4,
- "use_sparse": true,
- "seed": 0
-}
\ No newline at end of file
diff --git a/scripts/tmp/csnn/trainset.npy b/scripts/tmp/csnn/trainset.npy
deleted file mode 100644
index 940cb0f0aeac987b75ce6337c305e5d8e1d8b852..0000000000000000000000000000000000000000
Binary files a/scripts/tmp/csnn/trainset.npy and /dev/null differ
diff --git a/scripts/tmp2/csnn/log_train.txt b/scripts/tmp2/csnn/log_train.txt
deleted file mode 100644
index 5aaea3f528eee671a2e678a60feb4cfad2b9cdde..0000000000000000000000000000000000000000
--- a/scripts/tmp2/csnn/log_train.txt
+++ /dev/null
@@ -1,111 +0,0 @@
-10:48:52 20/7/2023
-Random seed: 0
-
-Run start at 10:48:52 20/7/2023
-Input data [28, 28, 1]
-Train:
-#1: MNIST(/home/ggoupy/phd/SNN-PCN/datasets/mnist/with-val//X_train.bin, /home/ggoupy/phd/SNN-PCN/datasets/mnist/with-val//y_train.bin)
-Test:
-
-DefaultOnOffFilter 1: [28, 28, 2]
-Process.DefaultOnOffFilter {
- center_dev: 1.000000
- filter_size: 7
- surround_dev: 2.000000
-}
-
-FeatureScaling 2: [28, 28, 2]
-Process.FeatureScaling {
-}
-
-LatencyCoding 3: [28, 28, 2]
-InputConverter.LatencyCoding {
-}
-
-Convolution 4: conv1 [24, 24, 16]
-Layer.Convolution (conv1) {
- annealing: 0.950000
- epoch: 25
- filter_height: 5
- filter_number: 16
- filter_width: 5
- lr_th: 1.000000
- min_th: 2.000000
- padding_x: 0
- padding_y: 0
- stdp: STDP.Multiplicative {
- am: 0.100000
- ap: 0.100000
- beta: 1.000000
- }
- stride_x: 1
- stride_y: 1
- t_obj: 0.750000
- th: 5.000000 [16]
- w: Gaussian(mean: 0.500000, dev: 0.010000) [5, 5, 2, 16]
- wta_infer: 0
-}
-
-Pooling 5: pool1 [6, 6, 16]
-Layer.Pooling (pool1) {
- filter_height: 4
- filter_number: 16
- filter_width: 4
- padding_x: 0
- padding_y: 0
- stride_x: 4
- stride_y: 4
-}
-
-
-Output 1 of conv1 [24, 24, 16]: train-conv1
-OutputConverter.DefaultOutput {
- max: 1.000000
- min: 0.000000
-}
-
-Output 1, Analysis: 1
-Analysis.Coherence {
-}
-
-Output 2 of pool1 [6, 6, 16]: train-pool1
-OutputConverter.DefaultOutput {
- max: 1.000000
- min: 0.000000
-}
-
-Output 2, Analysis: 1
-Analysis.Activity {
-}
-
-Output 3 of pool1 [6, 6, 16]: train-pool1
-OutputConverter.SpikeTiming {
-}
-
-Output 3, Analysis: 1
-Analysis.SaveFeatureNumpy {
-}
-
-Load 54000 train samples from MNIST(/home/ggoupy/phd/SNN-PCN/datasets/mnist/with-val//X_train.bin, /home/ggoupy/phd/SNN-PCN/datasets/mnist/with-val//y_train.bin)
-Save trained parameters at /home/ggoupy/phd/SNN-PCN/scripts/tmp2//csnn//model//Process.FeatureScaling/
-train-conv1, analysis Coherence:
-===Coherence===
-Mean weights: 0.248656
-------
-N: 120
-Min: 0.000932025
-Q1: 0.0853569
-Q2: 0.307391
-Q3: 0.466387
-Max: 0.822632
-
-Save trained parameters at /home/ggoupy/phd/SNN-PCN/scripts/tmp2//csnn//model//Layer.Convolution.conv1/
-train-pool1, analysis Activity:
-===Activity===
-* train set:
-Sparsity: 0.47984
-Active unit: 48.2265%
-Quiet: 0%
-train-pool1, analysis SaveFeatureNumpy:
-Run end at 10:49:21 20/7/2023
-Duration: 0h 0m 29s
diff --git a/scripts/tmp2/csnn/model/Layer.Convolution.conv1/thresholds.npy b/scripts/tmp2/csnn/model/Layer.Convolution.conv1/thresholds.npy
deleted file mode 100644
index b1cbb21326700144e3984b4324b5e2a08ab7bb4b..0000000000000000000000000000000000000000
Binary files a/scripts/tmp2/csnn/model/Layer.Convolution.conv1/thresholds.npy and /dev/null differ
diff --git a/scripts/tmp2/csnn/model/Layer.Convolution.conv1/weights.npy b/scripts/tmp2/csnn/model/Layer.Convolution.conv1/weights.npy
deleted file mode 100644
index dab30b7d86f376db0c9c552b1eb2738df172b247..0000000000000000000000000000000000000000
Binary files a/scripts/tmp2/csnn/model/Layer.Convolution.conv1/weights.npy and /dev/null differ
diff --git a/scripts/tmp2/csnn/model/Process.FeatureScaling/maxs.npy b/scripts/tmp2/csnn/model/Process.FeatureScaling/maxs.npy
deleted file mode 100644
index 2b3557e1d6eeea95683b29f209e30f4fb196d3d8..0000000000000000000000000000000000000000
Binary files a/scripts/tmp2/csnn/model/Process.FeatureScaling/maxs.npy and /dev/null differ
diff --git a/scripts/tmp2/csnn/model/Process.FeatureScaling/mins.npy b/scripts/tmp2/csnn/model/Process.FeatureScaling/mins.npy
deleted file mode 100644
index 46d74134f634ae44c6fcad920718f1036741b046..0000000000000000000000000000000000000000
Binary files a/scripts/tmp2/csnn/model/Process.FeatureScaling/mins.npy and /dev/null differ
diff --git a/scripts/tmp2/csnn/model/config.json b/scripts/tmp2/csnn/model/config.json
deleted file mode 100644
index 91e0003c22c82292c174470ee77f16a1ad8b2e90..0000000000000000000000000000000000000000
--- a/scripts/tmp2/csnn/model/config.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
- "dataset": "MNIST",
- "to_grayscale": false,
- "feature_scaling": true,
- "dog": [
- 7,
- 1,
- 2
- ],
- "conv1_epochs": 25,
- "conv1_c": 16,
- "conv1_k": 5,
- "conv1_w_init_mean": 0.5,
- "conv1_w_init_std": 0.01,
- "conv1_t_obj": 0.75,
- "conv1_th": 5,
- "conv1_min_th": 2,
- "conv1_lr_th": 1,
- "conv1_stdp": "multiplicative",
- "conv1_stdp_t": 0.1,
- "conv1_stdp_b": 1,
- "conv1_stdp_lr": 0.1,
- "conv1_annealing": 0.95,
- "conv1_wta_infer": false,
- "pool1_size": 4,
- "use_sparse": true,
- "seed": 0
-}
\ No newline at end of file
diff --git a/scripts/tmp2/csnn/testset.npy b/scripts/tmp2/csnn/testset.npy
deleted file mode 100644
index 6f666b8c8bac6c7bed2304bc2ab2d5b8c3aa1981..0000000000000000000000000000000000000000
Binary files a/scripts/tmp2/csnn/testset.npy and /dev/null differ
diff --git a/scripts/tmp2/csnn/trainset.npy b/scripts/tmp2/csnn/trainset.npy
deleted file mode 100644
index 651728323c7bcde9e0dd1631ff9b25e831326d4f..0000000000000000000000000000000000000000
Binary files a/scripts/tmp2/csnn/trainset.npy and /dev/null differ
diff --git a/scripts/tmp2/csnn/valset.npy b/scripts/tmp2/csnn/valset.npy
deleted file mode 100644
index 20b88c78317cfe7bf96f64ac1b93c6cd8b9c562e..0000000000000000000000000000000000000000
Binary files a/scripts/tmp2/csnn/valset.npy and /dev/null differ
diff --git a/scripts/tmp2/readout/readout_log.txt b/scripts/tmp2/readout/readout_log.txt
deleted file mode 100644
index 2591121306022d23acd08afb7f9a49e399ac5a5c..0000000000000000000000000000000000000000
--- a/scripts/tmp2/readout/readout_log.txt
+++ /dev/null
@@ -1,101 +0,0 @@
-Training start : 2023-07-20 10:49:32.446457
-
-Parameters :
-{'n_neurons': 20, 'firing_threshold': 60, 'leak_tau': None, 'decision_making': 'single', 'w_min': 0, 'w_max': 1, 'w_normal_init': True, 'w_init_mean': 0.5, 'w_init_std': 0.01, 'normalize_weights': True, 'w_mean_norm': 0.3, 'seed': 0, 'max_time': 1, 'n_epochs': 100, 'training_method': 'sstdp', 'loss_type': 'mean', 't_gap': 0.02, 'adjust_t_gap': True, 'loss_bounds': False, 'class_wta': True, 'early_stopping': True, 'early_stop_patience': 10, 'early_stop_tuning_acc': None, 'stdp_type': 'multiplicative', 'stdp_soft_bound': False, 'stdp_ignore_silent': False, 'stdp_ap': 0.001, 'stdp_am': -0.1, 'stdp_anti_ap': -0.001, 'stdp_anti_am': 0.1, 'stdp_ltp_win': 1, 'stdp_ltd_win': 1, 'stdp_ltp_offset': 0, 'stdp_b': 1, 'modulate_lr_perf': False, 'lr_annealing': 0.98, 'dropout_in': 0, 'dropout_out': 0, 'full_logs': True}
-
-Mean weights : 0.29980000853538513+-0.0723000019788742 (min:0.0 ; max:0.9980000257492065)
-First spk:0.8901518909732501 ; Mean spks:0.9090196490287781+-0.05492551624774933 ; Unique spks:7.133055555555556
-Abs error: 0.0032511032047349547
-Accuracy on training set after epoch 0: 0.9326
-Accuracy on validation set after epoch 0: 0.9518
-Accuracy on test set after epoch 0: 0.9553
-Mean weights : 0.29980000853538513+-0.09099999815225601 (min:0.0 ; max:0.9929999709129333)
-First spk:0.876227005738903 ; Mean spks:0.899161159992218+-0.0530686192214489 ; Unique spks:8.319333333333333
-Abs error: 0.0025223157532861234
-Accuracy on training set after epoch 1: 0.9578
-Accuracy on validation set after epoch 1: 0.9618
-Accuracy on test set after epoch 1: 0.9652
-Mean weights : 0.29980000853538513+-0.10140000283718109 (min:0.0 ; max:0.9879999756813049)
-First spk:0.8714953058306818 ; Mean spks:0.8944270610809326+-0.052035242319107056 ; Unique spks:8.431666666666667
-Abs error: 0.002029718874710484
-Accuracy on training set after epoch 2: 0.9645
-Accuracy on validation set after epoch 2: 0.9658
-Accuracy on test set after epoch 2: 0.9672
-Mean weights : 0.29980000853538513+-0.10840000212192535 (min:0.0010000000474974513 ; max:0.9940000176429749)
-First spk:0.8688277496695519 ; Mean spks:0.8917882442474365+-0.05144016817212105 ; Unique spks:8.470740740740741
-Abs error: 0.0018557841141468624
-Accuracy on training set after epoch 3: 0.9675
-Accuracy on validation set after epoch 3: 0.9667
-Accuracy on test set after epoch 3: 0.9687
-Mean weights : 0.29980000853538513+-0.1136000007390976 (min:0.0 ; max:0.9959999918937683)
-First spk:0.8670560026102596 ; Mean spks:0.8900707960128784+-0.051097769290208817 ; Unique spks:8.486037037037036
-Abs error: 0.0017691601411003398
-Accuracy on training set after epoch 4: 0.9689
-Accuracy on validation set after epoch 4: 0.9667
-Accuracy on test set after epoch 4: 0.9688
-Mean weights : 0.29980000853538513+-0.11749999970197678 (min:0.0010000000474974513 ; max:0.9909999966621399)
-First spk:0.86569960979731 ; Mean spks:0.888793408870697+-0.050875529646873474 ; Unique spks:8.46587037037037
-Abs error: 0.0017145750640284927
-Accuracy on training set after epoch 5: 0.9696
-Accuracy on validation set after epoch 5: 0.9673
-Accuracy on test set after epoch 5: 0.9697
-Mean weights : 0.29980000853538513+-0.12049999833106995 (min:0.0 ; max:0.9909999966621399)
-First spk:0.8647167684104707 ; Mean spks:0.8878604173660278+-0.05077669024467468 ; Unique spks:8.465703703703703
-Abs error: 0.0016816720181074332
-Accuracy on training set after epoch 6: 0.9704
-Accuracy on validation set after epoch 6: 0.9678
-Accuracy on test set after epoch 6: 0.9704
-Mean weights : 0.29980000853538513+-0.12300000339746475 (min:0.0 ; max:0.9909999966621399)
-First spk:0.8639137146605386 ; Mean spks:0.8871123194694519+-0.050733532756567 ; Unique spks:8.458462962962964
-Abs error: 0.0016646266230790968
-Accuracy on training set after epoch 7: 0.9706
-Accuracy on validation set after epoch 7: 0.9673
-Accuracy on test set after epoch 7: 0.9705
-Mean weights : 0.29980000853538513+-0.12520000338554382 (min:0.0 ; max:0.9950000047683716)
-First spk:0.8632216123298363 ; Mean spks:0.8864855170249939+-0.050713956356048584 ; Unique spks:8.464314814814815
-Abs error: 0.001651047839176684
-Accuracy on training set after epoch 8: 0.9714
-Accuracy on validation set after epoch 8: 0.9683
-Accuracy on test set after epoch 8: 0.9705
-Mean weights : 0.29980000853538513+-0.12720000743865967 (min:0.0 ; max:0.9940000176429749)
-First spk:0.8626228035555945 ; Mean spks:0.8859443664550781+-0.05071594938635826 ; Unique spks:8.466203703703703
-Abs error: 0.0016459656954708498
-Accuracy on training set after epoch 9: 0.9716
-Accuracy on validation set after epoch 9: 0.9672
-Accuracy on test set after epoch 9: 0.9706
-Mean weights : 0.29980000853538513+-0.1290999948978424 (min:0.0010000000474974513 ; max:0.9909999966621399)
-First spk:0.8620755980202446 ; Mean spks:0.8854557871818542+-0.050730105489492416 ; Unique spks:8.473944444444445
-Abs error: 0.0016363852087103307
-Accuracy on training set after epoch 10: 0.9718
-Accuracy on validation set after epoch 10: 0.9667
-Accuracy on test set after epoch 10: 0.9708
-Mean weights : 0.29980000853538513+-0.1307000070810318 (min:0.0 ; max:0.9929999709129333)
-First spk:0.8615775177633321 ; Mean spks:0.8850138783454895+-0.05075480788946152 ; Unique spks:8.46787037037037
-Abs error: 0.0016258562030193903
-Accuracy on training set after epoch 11: 0.972
-Accuracy on validation set after epoch 11: 0.9673
-Accuracy on test set after epoch 11: 0.9714
-Mean weights : 0.29980000853538513+-0.13220000267028809 (min:0.0 ; max:0.9909999966621399)
-First spk:0.8611204042832057 ; Mean spks:0.884613037109375+-0.050783831626176834 ; Unique spks:8.480037037037038
-Abs error: 0.0016202781597563394
-Accuracy on training set after epoch 12: 0.9723
-Accuracy on validation set after epoch 12: 0.9672
-Accuracy on test set after epoch 12: 0.9719
-Mean weights : 0.29980000853538513+-0.13369999825954437 (min:0.0 ; max:0.9909999966621399)
-First spk:0.860683818048901 ; Mean spks:0.8842380046844482+-0.05081281065940857 ; Unique spks:8.479962962962963
-Abs error: 0.0016112652775561425
-Accuracy on training set after epoch 13: 0.9724
-Accuracy on validation set after epoch 13: 0.9672
-Accuracy on test set after epoch 13: 0.9718
-Mean weights : 0.29980000853538513+-0.13500000536441803 (min:0.0 ; max:0.9919999837875366)
-First spk:0.860268470119547 ; Mean spks:0.8838828802108765+-0.05084911361336708 ; Unique spks:8.487351851851852
-Abs error: 0.0016054101923336397
-Accuracy on training set after epoch 14: 0.9728
-Accuracy on validation set after epoch 14: 0.9677
-Accuracy on test set after epoch 14: 0.9713
-Mean weights : 0.29980000853538513+-0.13619999587535858 (min:0.0 ; max:0.9919999837875366)
-First spk:0.8598991990497818 ; Mean spks:0.8835610747337341+-0.05088621750473976 ; Unique spks:8.483185185185185
-Abs error: 0.001595237087203618
-Accuracy on training set after epoch 15: 0.973
-Accuracy on validation set after epoch 15: 0.9675
-Accuracy on test set after epoch 15: 0.9709