---
# System prepended metadata

title: Architecture Multiclass Classifier

---

# Architecture Multiclass Classifier

## Tests with bird_engine_wind_600 database

- **Parameters**

 dataset :  ../../databaseAI/images/bird_engine_wind_600
 class attribution :  ['bird', 'vehicule', 'wind']
 learning rate :  0.0001
 gradient momentum :  0.9
 optimizer :  sgd
 weight decay :  0.0001
 max epochs :  300
 accuracy threshold :  70
 overfitting tolerance on last epochs :  5
 stagnate tolerance on last epochs  :  15

### Model multiclass configuration A (Base)

- **Architecture**

receptive_fieds = [3, 8, 8, 16]

- **Results**

Epoch : 200, Training loss : 0.846, Validation loss : 0.816, Accuracy : 63.8 %
[ Stagnation detected ]

=====================================

### Model multiclass configuration B (COLAB)

- **Architecture**

receptive_fields = [3, 8, 8, 8, 8, 8]

- **Results** (Trained on COLAB)

 Epoch : 9, Training loss : 1.111, Validation loss : 1.127, Accuracy : 29.95 %
 [ Stagnation detected ]


### Model multiclass configuration C (COLAB)

- Architecture

receptive_fields = [3, 16, 16, 16]

- **Results** (Trained on COLAB)

Epoch : 53, Training loss : 0.894, Validation loss : 0.92, Accuracy : 58.07 %
[ Stagnation detected ]

### Model multiclass configuration D

- **Architecture**

receptive_fields = [3, 8, 16, 24]

- **Results**

 Epoch : 300, Training loss : 1.036, Validation loss : 1.05, Accuracy : 46.09 %
 Training time : 7846.811 seconds

 
 ### Model multiclass configuration E

- **Architecture**

receptive_fields = [3, 16, 8, 8]
 
 - **Results**

 Epoch : 33, Training loss : 1.091, Validation loss : 1.091, Accuracy : 42.71 %
 MANUAL INTERRUPT (STAGNATE)

 ## Results Table 3 class


| Config | Arch               | Result | TERMINATION | Parms    |
| ------ | ------------------ | ------ | ----------- |    ---   |
| B      | [3, 8, 8, 8, 8, 8] | 30 %   | STAGNATE    |     |
| D      | [3, 8, 16, 24]     | 46 %   | MAX EPOCHS  |     |
| C      | [3, 16, 16, 16]    | 58 %   | STAGNATE    |     |
| A      | [3, 8, 8, 16]      | 64 %   | STAGNATE    |     |
| E      | [3, 16, 8, 8]      | 45 %   | MANUAL      |     |
| F      | [3, 16, 24, 16, 8] | ? %    |             |     |

 ## Conclude
 
 Model seem to stagnate, meaning model complexity is enough to learn pattern, but database doesn't allow model to learn more.
 ***NEED MORE DATA***

# ============================

Many trials with 3 class larger database didn't provide any results, switching to 2 class for validate dataset

# ============================

## Tests with bird_human_970 database
### 2023-05-14 02:53:05.078205 - bird_human_970_params_v1_arch_v2

- **Parameters**

dataset : ../../databaseAI/images/bird_human_970
class attribution : ['bird', 'human']
learning rate : 0.001
gradient momentum : 0.94
optimizer : sgd
weight decay : 1e-05
max epochs : 500
accuracy threshold : 70
overfitting tolerance on last epochs : 15
stagnate tolerance on last epochs : 25

- **Architecture**

receptive_fields = [3, 8, 8, 8, 8, 8]

- **Results**

Epoch : 23
Accuracy : 46.63461538461539 %
Training time : 0:12:00.599619 seconds

### 2023-05-14 02:53:09.476311 - bird_human_970_params_v1_arch_v3

- **Parameters**

No change

- **Architecture**

receptive_fields = [3, 16, 16, 16]

- **Results**

Epoch : 23
Accuracy : 48.55769230769231 %
Training time : 0:14:13.218823 seconds



### 2023-05-14 01:01:44.272442 - bird_human_970_params_v1_arch_v4

- **Parameters**

No change

- **Architecture**

receptive_fields = [3, 8, 16, 24]

- **Results**

Epoch : 63
Accuracy : 49.51923076923077 %
Training time : 0:30:09.139139 seconds

### 2023-05-14 01:31:55.237347 - bird_human_970_params_v1_arch_v4

- **Parameters**

learning rate : 0.0001

- **Architecture**

No change

- **Results**

Epoch : 78
Accuracy : 61.29807692307692 %
Training time : 0:36:28.495559 seconds

 ## Conclude
 
 Model seem to stagnate, tests with other 2 class database not including human sounds (bird_engine_970) seem to be working well, removing human sounds from db.
 ***CHANGE DB***

# ============================

## Tests with bird_engine_chainsaw_970 database

================================================================================
### 2023-05-17 02:48:42.915229 - bird_engine_chainsaw_970_default_thin_24

- **Parameters**

dataset : ../../databaseAI/images/bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 85
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 20

- **Architecture**

receptive_fields = [3, 24, 24, 12, 12]

- **Results**

Epoch : 26
Accuracy : 85.36184210526316 %
Training time : 0:38:14.202481 seconds

================================================================================
### 2023-05-17 00:56:10.685347 - bird_engine_chainsaw_970_default_less_pool

- **Parameters**

dataset : ../../databaseAI/images/bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 85
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 20

- **Architecture**

receptive_fields = [3, 32, 32, 16, 16]

- **Results**

Epoch : 45
Accuracy : 85.47149122807018 %
Training time : 0:48:20.269194 seconds

================================================================================
### 2023-05-17 01:44:30.976560 - bird_engine_chainsaw_970_default_fat_32

- **Parameters**

dataset : ../../databaseAI/images/bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 85
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 20

- **Architecture**

receptive_fields = [3, 32, 32, 32, 32]

- **Results**

Epoch : 37
Accuracy : 86.1842105263158 %
Training time : 1:04:11.925233 seconds

## Tests with bird_engine_chainsaw_970 database



================================================================================
### 2023-05-19 17:22:09.993466 - bird_engine_chainsaw_970_leo_less_pool

- **Parameters**

dataset : ../../databaseAI/images/bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : adam
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 32, 32, 16, 16]

- **Results**

Epoch : 49
Accuracy : 87.17105263157895 %
Training time : 0:51:01.788529 seconds

================================================================================
### 2023-05-16 20:49:22.702684 - bird_engine_chainsaw_970_default_2_2

- **Parameters**

dataset : ../../bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 20, 30, 38, 2]

- **Results**

Epoch : 49
Accuracy : 81.74342105263158 %
Training time : 0:51:39.972712 seconds

### 2023-05-16 21:41:02.688330 - bird_engine_chainsaw_970_default_2_3

- **Parameters**

dataset : ../../bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 20, 30, 38, 4]

- **Results**

Epoch : 49
Accuracy : 87.00657894736842 %
Training time : 0:51:30.718878 seconds

### 2023-05-16 22:32:33.423578 - bird_engine_chainsaw_970_default_2_4

- **Parameters**

dataset : ../../bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 20, 30, 38, 6]

- **Results**

Epoch : 49
Accuracy : 88.43201754385964 %
Training time : 0:51:36.063948 seconds


### 2023-05-16 23:24:09.500357 - bird_engine_chainsaw_970_default_2_5

- **Parameters**

dataset : ../../bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 20, 30, 38, 8]

- **Results**

Epoch : 49
Accuracy : 80.92105263157895 %
Training time : 0:51:32.612040 seconds

### 2023-05-17 00:15:42.126205 - bird_engine_chainsaw_970_default_2_6

- **Parameters**

dataset : ../../bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 20, 30, 38, 10]

- **Results**

Epoch : 49
Accuracy : 86.78728070175438 %
Training time : 0:51:31.266450 seconds

### 2023-05-17 01:07:13.406466 - bird_engine_chainsaw_970_default_2_7

- **Parameters**

dataset : ../../bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 20, 30, 38, 12]

- **Results**

Epoch : 49
Accuracy : 87.82894736842105 %
Training time : 0:51:40.962897 seconds

### 2023-05-17 01:58:54.380244 - bird_engine_chainsaw_970_default_2_8

- **Parameters**

dataset : ../../bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 20, 30, 38, 20]

- **Results**

Epoch : 49
Accuracy : 84.86842105263158 %
Training time : 0:51:42.338960 seconds


### 2023-05-17 02:50:36.730471 - bird_engine_chainsaw_970_default_2_9

- **Parameters**

dataset : ../../bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 20, 30, 38, 24]

- **Results**

Epoch : 49
Accuracy : 85.47149122807018 %
Training time : 0:51:33.998725 seconds


### 2023-05-17 03:42:10.742873 - bird_engine_chainsaw_970_default_2_10

- **Parameters**

dataset : ../../bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 20, 30, 38, 32]

- **Results**

Epoch : 49
Accuracy : 87.17105263157895 %
Training time : 0:51:35.672881 seconds


### 2023-05-17 04:33:46.429914 - bird_engine_chainsaw_970_default_2_11

- **Parameters**

dataset : ../../bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 20, 30, 38, 38]

- **Results**

Epoch : 49
Accuracy : 87.4451754385965 %
Training time : 0:51:39.509117 seconds


### 2023-05-17 05:25:25.952135 - bird_engine_chainsaw_970_default_2_12

- **Parameters**

dataset : ../../bird_engine_chainsaw_970
class attribution : ['bird', 'engine', 'chainsaw']
learning rate : 0.001
gradient momentum : 0.95
optimizer : sgd
weight decay : 0.0001
max epochs : 50
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 20, 30, 38, 42]

- **Results**

Epoch : 49
Accuracy : 84.4298245614035 %
Training time : 0:51:41.777742 seconds

### 2023-05-16 08:21:24.398931 - bird_engine_chainsaw_970_default_2

- **Parameters**


- **Architecture**

receptive_fields = [3, 20, 30, 38, 16]

- **Results**

Epoch : 27
Accuracy : 85.85526315789474 %
Training time : 0:30:22.465546 seconds

================================================================================
### 2023-05-16 15:22:51.583474 - bird_engine_chainsaw_970_default_2

- **Parameters**

accuracy threshold : 99

- **Architecture**


- **Results**

Epoch : 49
Accuracy : 87.06140350877193 %
Training time : 0:51:41.585158 seconds

### 2023-05-16 08:51:46.887957 - bird_engine_chainsaw_970_default_3



- **Parameters**


- **Architecture**

receptive_fields = [3, 38, 30, 10, 16]

- **Results**

Epoch : 34
Accuracy : 85.47149122807018 %
Training time : 0:52:13.358785 seconds

================================================================================
### 2023-05-16 16:14:33.184635 - bird_engine_chainsaw_970_default_3

- **Parameters**

accuracy threshold : 99

- **Architecture**


- **Results**

Epoch : 49
Accuracy : 86.07456140350878 %
Training time : 1:12:48.567429 seconds

## Tests with new database 6 class : (bird, chainsaw, engine, rain, speech, static)

========================================================
### 2023-09-03 23:10:35.268023 - default_128peak_dropout

- **Parameters**

dataset : ../database/images/
class attribution : ['bird', 'chainsaw', 'engine', 'rain', 'speech', 'static']
learning rate : 0.001
gradient momentum : 0.96
optimizer : sgd
weight decay : 0.0001
max epochs : 75
accuracy threshold : 90
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [3, 32, 64, 64, 64, 32]

- **Results**

Epoch : 26
Accuracy : 90.02659574468085 %
Training time : 1:40:56.620812 seconds

### 2023-10-24 13:24:11.547664 - default_tars

- **Parameters**

dataset : ../database/images/
class attribution : ['bird', 'chainsaw', 'engine', 'rain', 'speech', 'static', 'gunshot']
learning rate : 0.001
gradient momentum : 0.96
optimizer : sgd
weight decay : 0.0001
max epochs : 75
accuracy threshold : 96
overfitting tolerance on last epochs : 10
stagnate tolerance on last epochs : 10

- **Architecture**

receptive_fields = [8, 16, 32, 64, 64]

- **Results**

Epoch : 28
Accuracy : 86.3970588235294 %
Training time : 2:28:14.668570 seconds