<commit_before>HISTOGRAMA
mlp_1_layer(100), mlp_1_layer(300), mlp_1_layer(500), mlp_1_layer(700), mlp_1_layer(900)
default encoding
0.203, 0.18, 0.185, 0.203, 0.211

mlp_2_layer(100, 900), mlp_2_layer(900, 100), mlp_2_layer(300, 700), mlp_2_layer(300, 300), mlp_2_layer(500, 500)
default encoding
0.098, 0.119, 0.109, 0.115, 0.098

CANAIS SOMADOS
encoding com todos os pixels tendo seus canais somados e standartized
10% (3000 iterations)

B&W STANDARTIZED

[mlp_1_layer(300)]
23% (3000 iterations)
26% (10000 iterations)

models = [mlp_1_layer(100), mlp_1_layer(300), mlp_1_layer(500), mlp_1_layer(700), mlp_1_layer(900)]
24%, 22%, 22%, 26%, 23%

models = [mlp_3_layer(300, 700, 300)]
25% (2000 iterações)

models = [mlp_3_layer(700, 500, 300)]
27% (2000 iterações)

models = [mlp_3_layer(900, 700, 500)]
27% (max: 29) (2000 iterações)

models = [mlp_3_layer(300, 300, 300)]
27% (2000 iterações)

models = [mlp_3_layer(300, 500, 700)]
10% (2000 iterações)

models = [mlp_1_layer(30)]
encoding com todos os pixels standartized
25% (10000)

RAW STANDARTIZED

[mlp_1_layer(300)]
25% (3000 iterations)

models = [mlp_1_layer(30)]
Usando sigmoid nos layers internos
28% (10000)

models = [mlp_2_layer(30, 30)]
Usando sigmoid nos layers internos
31% (2000)

models = [mlp_2_layer(300, 100)]
Usando sigmoid nos layers internos
31% (2000)

models = [mlp_2_layer(300, 300)]
Usando sigmoid nos layers internos
33% (2000)

models = [mlp_2_layer(300, 500)]
Usando sigmoid nos layers internos
30% (2000)

models = [mlp_2_layer(300, 300)]
Usando elu/sigmoid
31% (2000)

models = [mlp_2_layer(1500, 1500)]
Usando sigmoid nos layers internos
10% (não terminei de rodar)

models = [mlp_1_layer(1500)]
Usando sigmoid nos layers internos
10% (não terminei de rodar)

models = [mlp_3_layer(300, 300, 300)]
Usando sigmoid nos layers internos
32% (2000)

models = [mlp_3_layer(300, 300, 300)]
Usando sigmoid/relu/sigmoid
30% (2000)

models = [mlp_3_layer(300, 300, 300)]
Usando relu/relu/sigmoid
35% (2000)

models = [mlp_3_layer(300, 300, 300)]
Usando elu/elu/sigmoid
38% (2000) 35% numa segunda tentativa

models = [mlp_3_layer(300, 300, 300)]
 Usando softplus/softplus/sigmoid
36% (2000)

models = [mlp_3_layer(300, 300, 300)]
Usando elu/elu/tanh
10% (2000)

models = [mlp_3_layer(300, 300, 300)]
Usando tanh/tanh/tanh
26% (2000)

models = [mlp_3_layer(300, 300, 300)]
Usando tanh/tanh/sigmoid
27% (2000)

models = [mlp_3_layer(300, 300, 300)]
Usando relu6/relu6/sigmoid
26% (2000)

models = [mlp_3_layer(300, 300, 300)]
Usando relu/relu/relu
10% (2000)

models = [mlp_3_layer(300, 300, 300)]
Usando relu6/relu6/relu6
10% (2000)

models = [mlp_3_layer(300, 500, 300)]
Usando sigmoid nos layers internos
34% (2000)

models = [mlp_3_layer(300, 700, 300)]
Usando sigmoid nos layers internos
31% (2000) mais chegou a 35% T.T

models = [mlp_3_layer(900, 700, 500)]
Usando elu/elu/sigmoid
10% (2000)

models = [mlp_3_layer(30, 300, 30)]
Usando elu/elu/sigmoid
35% (2000)

models = [mlp_3_layer(700, 700, 700)]
Usando elu/elu/sigmoid
10% (2000)

models = [mlp_3_layer(500, 500, 500)]
Usando elu/elu/sigmoid
26% (2000)

models = [mlp_3_layer(200, 200, 200)]
Usando elu/elu/sigmoid
37% (2000) 40% max

models = [mlp_3_layer(150, 150, 150)]
Usando elu/elu/sigmoid
36% (2000) 38% max

models = [mlp_3_layer(100, 100, 100)]
Usando elu/elu/sigmoid
36% (2000)

models = [mlp_3_layer(50, 50, 50)]
Usando elu/elu/sigmoid
30% (2000)

models = [mlp_3_layer(30, 30, 30)]
Usando elu/elu/sigmoid
32% (2000)

models = [mlp_3_layer(10, 10, 10)]
Usando elu/elu/sigmoid
28% (2000)

models = [mlp_3_layer(500, 700, 500)]
Usando elu/elu/sigmoid
10% (2000)

mlp_3_layer_act(tf.nn.elu, 200, tf.nn.elu, 200, tf.nn.sigmoid, 200)
38% (10000)

mlp_3_layer_act(tf.nn.elu, 30, tf.nn.elu, 300, tf.nn.sigmoid, 30)
35% (10000)

mlp_3_layer_act(tf.nn.sigmoid, 300, tf.nn.sigmoid, 500, tf.nn.sigmoid, 300)
33% (10000)

mlp_3_layer_act(tf.nn.softplus, 300, tf.nn.softplus, 300, tf.nn.sigmoid, 300)
39% (10000)

mlp_3_layer_act(tf.nn.relu, 300, tf.nn.relu, 300, tf.nn.sigmoid, 300)
40% (10000)

RAW NORMALIZADO

mlp_1_layer(300)
sigmoid
37%/38% (2000)
39% (10000)

mlp_1_layer(30)
sigmoid
35% (2000)

mlp_1_layer(900)
sigmoid
10% (2000)

mlp_1_layer(2000)
sigmoid
10% (2000)

mlp_2_layer(300, 300)
elu, sigmoid
31% (2000)

mlp_3_layer_act(tf.nn.relu, 300, tf.nn.relu, 300, tf.nn.sigmoid, 300),
10% (2000)

mlp_3_layer_act(tf.nn.sigmoid, 300, tf.nn.sigmoid, 500, tf.nn.sigmoid, 300)
36% (2000)

mlp_3_layer_act(tf.nn.elu, 30, tf.nn.elu, 300, tf.nn.sigmoid, 30)
36%/39% (2000)

mlp_2_layer(300, 300)
sigmoid sigmoid
38% (2000)

mlp_3_layer_act(tf.nn.elu, 30, tf.nn.elu, 30, tf.nn.sigmoid, 30)
36% (2000)

mlp_1_layer(elu, 300)
10% (2000)

mlp_1_layer(tanh, 300),
10% (2000)

POSITION NORMALIZED

mlp_1_layer(sigmoid, 300),
28% (2000)

mlp_1_layer(sigmoid, 300),
23% (2000)

mlp_3_layer(elu, 300, elu, 300, sigmoid, 300)
10% (2000)

mlp_3_layer(sigmoid, 300, sigmoid, 300, sigmoid, 300)
28% (2000)

mlp_3_layer(tf.nn.elu, 30, tf.nn.elu, 300, tf.nn.sigmoid, 30)
37%

SIFT (10 clusters, 10000 iterations)
mlp_1_layer(sigmoid, 5), # 22%
mlp_1_layer(elu, 5), # 24%
mlp_1_layer(relu, 5), # 22%
mlp_1_layer(tanh, 5), # 20%
mlp_1_layer(tf.nn.softplus, 5), # 24%
mlp_1_layer(sigmoid, 5), # 23%
mlp_1_layer(sigmoid, 10), # 24%
mlp_1_layer(sigmoid, 15), # 23%
mlp_1_layer(sigmoid, 20), # 23%
mlp_1_layer(sigmoid, 30), # 24%
mlp_1_layer(sigmoid, 100), # 20
mlp_2_layer(sigmoid, 5, sigmoid, 5), # 21%
mlp_2_layer(elu, 5, sigmoid, 5), # 25%
mlp_2_layer(sigmoid, 5, elu, 5), # 24%
mlp_2_layer(elu, 5, elu, 5), # 22%
mlp_3_layer(sigmoid, 5, sigmoid, 5, sigmoid, 5), # 21%
mlp_3_layer(sigmoid, 5, sigmoid, 5, elu, 5), # 24%
mlp_3_layer(sigmoid, 5, elu, 5, sigmoid, 5), # 22%
mlp_3_layer(sigmoid, 5, elu, 5, elu, 5), # 22%
mlp_3_layer(elu, 5, sigmoid, 5, sigmoid, 5), # 24%
mlp_3_layer(elu, 5, sigmoid, 5, elu, 5), # 22%
mlp_3_layer(elu, 5, elu, 5, sigmoid, 5), # 21%
mlp_3_layer(elu, 5, elu, 5, elu, 5), # 25%

SIFT (100 clusters, 10000 iterations)
mlp_1_layer(sigmoid, 5), # 24%
mlp_1_layer(elu, 5), # 25%
mlp_1_layer(relu, 5), # 24%
mlp_1_layer(tanh, 5), # 25%
mlp_1_layer(tf.nn.softplus, 5), # 26%

mlp_1_layer(sigmoid, 5), # 23%
mlp_1_layer(sigmoid, 10), # 24%
mlp_1_layer(sigmoid, 15), # 23%
mlp_1_layer(sigmoid, 20), # 23%
mlp_1_layer(sigmoid, 30), # 21%
mlp_1_layer(sigmoid, 100), # 21%

mlp_2_layer(sigmoid, 5, sigmoid, 5), # 24%
mlp_2_layer(elu, 5, sigmoid, 5), # 22%
mlp_2_layer(sigmoid, 5, elu, 5), # 24%
mlp_2_layer(elu, 5, elu, 5), # 24%
mlp_3_layer(sigmoid, 5, sigmoid, 5, sigmoid, 5), # 21%
mlp_3_layer(sigmoid, 5, sigmoid, 5, elu, 5), # 25%
mlp_3_layer(sigmoid, 5, elu, 5, sigmoid, 5), # 22%
mlp_3_layer(sigmoid, 5, elu, 5, elu, 5), # 25%
mlp_3_layer(elu, 5, sigmoid, 5, sigmoid, 5), # 20%
mlp_3_layer(elu, 5, sigmoid, 5, elu, 5), # 23%
mlp_3_layer(elu, 5, elu, 5, sigmoid, 5), # 24%
mlp_3_layer(elu, 5, elu, 5, elu, 5), # 24%,

HoG
mlp_1_layer(tf.nn.sigmoid, 5)
ecoding com pixels não normalizados
treinamento: 10000
42,4%

HoG
mlp_1_layer(tf.nn.sigmoid, 5)
ecoding com pixels normalizados
treinamento: 10000
8,9%

HoG
mlp_1_layer(elu, 5),
ecoding com pixels não normalizados
treinamento: 10000
40,2%

HoG
mlp_1_layer(relu, 5),
ecoding com pixels não normalizados
treinamento: 10000
37,6%

treinamento: 9000
41,6%

HoG
mlp_1_layer(tanh, 5),
ecoding com pixels não normalizados
treinamento: 10000
41,2%

treinamento: 8900
42,8%

treinamento: 5400
43,7%

HoG
mlp_1_layer(tf.nn.softplus, 5)
encoding com pixels não normalizados
treinamento: 7200
44,2%

HoG
{
    'func': mlp_1_layer,
    'args': [elu, 300],
    'title': 'mlp 1 layer com elu'
 },
 20000
 48%
 
 HoG
{
    'func': mlp_1_layer,
    'args': [elu, 300],
    'title': 'mlp 1 layer com elu'
 },
 1600
 48,89%
 
  HoG
{
    'func': mlp_1_layer,
    'args': [elu, 300],
    'title': 'mlp 1 layer com elu'
 },
 5200
 49,2%
 
 {
    'func': mlp_1_layer,
    'args': [elu, 300],
    'title': 'mlp 1 layer com elu'
 },
 7500
 50,4%

HoG
{
 'func': mlp_3_layer,
 'args': [relu, 300, relu, 300, sigmoid, 300],
 'title': 'mlp 1 layer com elu'
},
10000
46%

HoG
{
    'func': mlp_1_layer,
    'args': [relu, 500],
    'title': 'mlp 1 layer com sigmoid'
},
10000
47%

mlp_1_layer(tf.nn.softplus, 5)
ecoding com pixels não normalizados
treinamento: 19100
44,4%

HoG
mlp_2_layer(softplus, 5, softplus, 10)
ecoding com pixels não normalizados
treinamento: 7200
44,6%

HoG
mlp_2_layer(softplus, 5, softplus, 10)
ecoding com pixels não normalizados
treinamento: 7600
44,9%

HoG
mlp_3_layer(softplus, 5, softplus, 10, softplus, 10)
ecoding com pixels não normalizados
treinamento: 10000
40,4%

HoG
mlp_3_layer(softplus, 5, softplus, 20, softplus, 10)
ecoding com pixels não normalizados 
treinamento: 10000
42,4%<commit_msg>Mlehorando formatação da arquivo de resultados<commit_after>Esse arquivo contém os resultados que obtivemos em todos os testes que foram feitos.

Os melhores resultados estarão em destaque.

A não ser quando notado, os resultado foram obtidos com 10.000 iterações e todos os layers são softmax

# HISTOGRAMA
1 layer: (100 nós)
0.203%

1 layer: (300 nós)
0.18%

1 layer: (500 nós)
0.185%

1 layer: (700 nós)
0.203%

1 layer: (900 nós)
0.211%

2 layers: (100, 900)
0.098%

2 layers: (900, 100)
0.119%

2 layers: (300, 700)
0.109%

2 layers: (300, 300)
0.115%

2 layers: (500, 500)
0.098%

# CANAIS SOMADOS
## encoding com todos os pixels tendo seus canais somados e standartized
1 layer: (300)
10% (3000 iterations)

# Preto e Branco (standardize)

1 layer: (300)
23% (3000 iterações)
26%

1 layer: (100)
24%

1 layer: (300)
22%

1 layer: (500)
22%

1 layer: (700)
26%

1 layer: (900)
23%

1 layer: (30)
25%

3 layers:(300, 700, 300)
25% (2000 iterações)

3 layers:(700, 500, 300)
27% (2000 iterações)

3 layers:(900, 700, 500)
27% (2000 iterações)

3 layers:(300, 300, 300)
27% (2000 iterações)

3 layers:(300, 500, 700)
10% (2000 iterações)

# RAW (standardize)

1 layer: (300)
25% (3000 iterations)

1 layer: (30)
Usando sigmoid nos layers internos
28%

2 layers: (30, 30)
Usando sigmoid nos layers internos
31% (2000)

2 layers: (300, 100)
Usando sigmoid nos layers internos
31% (2000)

2 layers: (300, 300)
Usando sigmoid nos layers internos
33% (2000)

2 layers: (300, 500)
Usando sigmoid nos layers internos
30% (2000)

2 layers: (300, 300)
Usando elu/sigmoid
31% (2000)

2 layers: (1500, 1500)
Usando sigmoid nos layers internos
10%

1 layer: (1500)
Usando sigmoid nos layers internos
10%

3 layers:(300, 300, 300)
Usando sigmoid nos layers internos
32% (2000)

3 layers:(300, 300, 300)
Usando sigmoid/relu/sigmoid
30% (2000)

3 layers:(300, 300, 300)
Usando relu/relu/sigmoid
35% (2000)

3 layers:(300, 300, 300)
Usando elu/elu/sigmoid
38% (2000)

3 layers:(300, 300, 300)
Usando softplus/softplus/sigmoid
36% (2000)

3 layers:(300, 300, 300)
Usando elu/elu/tanh
10% (2000)

3 layers:(300, 300, 300)
Usando tanh/tanh/tanh
26% (2000)

3 layers:(300, 300, 300)
Usando tanh/tanh/sigmoid
27% (2000)

3 layers:(300, 300, 300)
Usando relu6/relu6/sigmoid
26% (2000)

3 layers:(300, 300, 300)
Usando relu/relu/relu
10% (2000)

3 layers:(300, 300, 300)
Usando relu6/relu6/relu6
10% (2000)

3 layers:(300, 500, 300)
Usando sigmoid nos layers internos
34% (2000)

3 layers:(300, 700, 300)
Usando sigmoid nos layers internos
31% (2000)

3 layers:(900, 700, 500)
Usando elu/elu/sigmoid
10% (2000)

3 layers:(30, 300, 30)
Usando elu/elu/sigmoid
35% (2000)

3 layers:(700, 700, 700)
Usando elu/elu/sigmoid
10% (2000)

3 layers:(500, 500, 500)
Usando elu/elu/sigmoid
26% (2000)

3 layers:(200, 200, 200)
Usando elu/elu/sigmoid
37% (2000) 40% max

3 layers:(150, 150, 150)
Usando elu/elu/sigmoid
36% (2000) 38% max

3 layers:(100, 100, 100)
Usando elu/elu/sigmoid
36% (2000)

3 layers:(50, 50, 50)
Usando elu/elu/sigmoid
30% (2000)

3 layers:(30, 30, 30)
Usando elu/elu/sigmoid
32% (2000)

3 layers:(10, 10, 10)
Usando elu/elu/sigmoid
28% (2000)

3 layers:(500, 700, 500)
Usando elu/elu/sigmoid
10% (2000)

3 layers:(elu, 200, elu, 200, sigmoid, 200)
Usando elu/elu/sigmoid
38%

3 layers:(elu, 30, elu, 300, sigmoid, 30)
Usando elu/elu/sigmoid
35%

3 layers:(sigmoid, 300, sigmoid, 500, sigmoid, 300)
Usando sigmoid nos layers internos
33%

3 layers:(softplus, 300, softplus, 300, sigmoid, 300)
Usando softplus/softplus/sigmoid
39%

3 layers:(relu, 300, relu, 300, sigmoid, 300)
Usando relu/relu/sigmoid
40%

# RAW (normalized)

1 layer: (300)
Usando sigmoid nos layers internos
38% (2000)
39% (10000)

1 layer: (30)
Usando sigmoid nos layers internos
35% (2000)

1 layer: (900)
Usando sigmoid nos layers internos
10% (2000)

1 layer: (2000)
Usando sigmoid nos layers internos
10% (2000)

2 layers: (300, 300)
Usando elu, sigmoid nos layers internos
31% (2000)

3 layers:(relu, 300, relu, 300, sigmoid, 300)
Usando relu/relu/sigmoid
10% (2000)

3 layers:(sigmoid, 300, sigmoid, 500, sigmoid, 300)
Usando sigmoid nos layers internos
36% (2000)

3 layers:(elu, 30, elu, 300, sigmoid, 30)
Usando elu/elu/sigmoid
39% (2000)

2 layers: (300, 300)
Usando sigmoid/sigmoid
38% (2000)

3 layers:(elu, 30, elu, 30, sigmoid, 30)
36% (2000)

1 layer: (elu, 300)
10% (2000)

1 layer: (tanh, 300)
10% (2000)

# SIFT

## 10 clusters
1 layer: (sigmoid, 5),                         # 22%
1 layer: (elu, 5),                             # 24%
1 layer: (relu, 5),                            # 22%
1 layer: (tanh, 5),                            # 20%
1 layer: (softplus, 5),                        # 24%

1 layer: (sigmoid, 5),                         # 23%
1 layer: (sigmoid, 10),                        # 24%
1 layer: (sigmoid, 15),                        # 23%
1 layer: (sigmoid, 20),                        # 23%
1 layer: (sigmoid, 30),                        # 24%
1 layer: (sigmoid, 100),                       # 20

2 layers: (sigmoid, 5, sigmoid, 5),             # 21%
2 layers: (elu, 5, sigmoid, 5),                 # 25%
2 layers: (sigmoid, 5, elu, 5),                 # 24%
2 layers: (elu, 5, elu, 5),                     # 22%

3 layers:(sigmoid, 5, sigmoid, 5, sigmoid, 5), # 21%
3 layers:(sigmoid, 5, sigmoid, 5, elu, 5),     # 24%
3 layers:(sigmoid, 5, elu, 5, sigmoid, 5),     # 22%
3 layers:(sigmoid, 5, elu, 5, elu, 5),         # 22%
3 layers:(elu, 5, sigmoid, 5, sigmoid, 5),     # 24%
3 layers:(elu, 5, sigmoid, 5, elu, 5),         # 22%
3 layers:(elu, 5, elu, 5, sigmoid, 5),         # 21%
3 layers:(elu, 5, elu, 5, elu, 5),             # 25%

## 100 clusters
1 layer: (sigmoid, 5),                         # 24%
1 layer: (elu, 5),                             # 25%
1 layer: (relu, 5),                            # 24%
1 layer: (tanh, 5),                            # 25%
1 layer: (softplus, 5),                  # 26%

1 layer: (sigmoid, 5),                         # 23%
1 layer: (sigmoid, 10),                        # 24%
1 layer: (sigmoid, 15),                        # 23%
1 layer: (sigmoid, 20),                        # 23%
1 layer: (sigmoid, 30),                        # 21%
1 layer: (sigmoid, 100),                       # 21%

2 layers: (sigmoid, 5, sigmoid, 5),             # 24%
2 layers: (elu, 5, sigmoid, 5),                 # 22%
2 layers: (sigmoid, 5, elu, 5),                 # 24%
2 layers: (elu, 5, elu, 5),                     # 24%

3 layers:(sigmoid, 5, sigmoid, 5, sigmoid, 5)  # 21%
3 layers:(sigmoid, 5, sigmoid, 5, elu, 5)      # 25%
3 layers:(sigmoid, 5, elu, 5, sigmoid, 5)      # 22%
3 layers:(sigmoid, 5, elu, 5, elu, 5)          # 25%
3 layers:(elu, 5, sigmoid, 5, sigmoid, 5)      # 20%
3 layers:(elu, 5, sigmoid, 5, elu, 5)          # 23%
3 layers:(elu, 5, elu, 5, sigmoid, 5)          # 24%
3 layers:(elu, 5, elu, 5, elu, 5)              # 24%

# HoG

## encoding com pixels normalizados
1 layer: (sigmoid, 5)
8,9%

## encoding com pixels não normalizados

1 layer: (sigmoid, 5)
42,4%

1 layer: (elu, 5)
40,2%

1 layer: (relu, 5)
37,6%
41,6% (9000 iterações)

1 layer: (tanh, 5)
41,2%
42,8% (8900 iterações)
43,7% (5400 iterações)

1 layer: (softplus, 5)
44,2% (7200 iterações)

1 layer: (elu, 300)
48,89% (1600 iterações)
49,2%
50,4% (7500 iterações)
48% (20000 iterações)

3 layers:(relu, 300, relu, 300, sigmoid, 300)
46%

1 layer: (relu, 500)
47%

1 layer: (softplus, 5)
44,4% (19100 iterações)

2 layers: (softplus, 5, softplus, 10)
44,6% (7200 iterações)

2 layers: (softplus, 5, softplus, 10)
44,9% (7600 iterações)

3 layers:(softplus, 5, softplus, 10, softplus, 10)
40,4%

3 layers:(softplus, 5, softplus, 20, softplus, 10)
42,4%
{'fraction_non_alphanumeric': 0.15213539439115292, 'fraction_numerical': 0.17180912812366347, 'mean_word_length': 3.7129283040598904, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5575969', 'n_tokens_mistral': 9549, 'n_tokens_neox': 7423, 'n_words': 2081}