From 75299eae669d77288c50a73d5a371fa275605f74 Mon Sep 17 00:00:00 2001 From: adri-mp Date: Thu, 15 Feb 2024 19:33:29 +0100 Subject: [PATCH] post 13 --- docs/_posts/2024-02-15-Semana-13.md | 194 ++++++++++++++++++ .../assets/images/post13/balancedDataset1.png | Bin 0 -> 62032 bytes docs/assets/images/post13/epochTest.png | Bin 0 -> 85015 bytes docs/assets/images/post13/expertGraphic.png | Bin 0 -> 43165 bytes docs/assets/images/post13/graphicRqtPlot.png | Bin 0 -> 87833 bytes .../images/post13/inferenceFrecuency.png | Bin 0 -> 35133 bytes .../images/post13/lossCurveBalanced1.png | Bin 0 -> 20975 bytes .../images/post13/lossCurveUnbalanced1.png | Bin 0 -> 45843 bytes docs/assets/images/post13/resultBalanced1.png | Bin 0 -> 47149 bytes .../images/post13/resultSemiBalanced1.png | Bin 0 -> 49805 bytes .../images/post13/resultUnbalanced1.png | Bin 0 -> 43537 bytes .../images/post13/semiBalancedDataset1.png | Bin 0 -> 59784 bytes .../images/post13/semiBalancedDataset2.png | Bin 0 -> 67484 bytes .../images/post13/unbalancedDataset1.pn | Bin 0 -> 70093 bytes .../images/post13/unbalancedDataset2.png | Bin 0 -> 73452 bytes 15 files changed, 194 insertions(+) create mode 100644 docs/_posts/2024-02-15-Semana-13.md create mode 100644 docs/assets/images/post13/balancedDataset1.png create mode 100644 docs/assets/images/post13/epochTest.png create mode 100644 docs/assets/images/post13/expertGraphic.png create mode 100644 docs/assets/images/post13/graphicRqtPlot.png create mode 100644 docs/assets/images/post13/inferenceFrecuency.png create mode 100644 docs/assets/images/post13/lossCurveBalanced1.png create mode 100644 docs/assets/images/post13/lossCurveUnbalanced1.png create mode 100644 docs/assets/images/post13/resultBalanced1.png create mode 100644 docs/assets/images/post13/resultSemiBalanced1.png create mode 100644 docs/assets/images/post13/resultUnbalanced1.png create mode 100644 docs/assets/images/post13/semiBalancedDataset1.png create mode 100644 docs/assets/images/post13/semiBalancedDataset2.png create mode 100644 docs/assets/images/post13/unbalancedDataset1.pn create mode 100644 docs/assets/images/post13/unbalancedDataset2.png diff --git a/docs/_posts/2024-02-15-Semana-13.md b/docs/_posts/2024-02-15-Semana-13.md new file mode 100644 index 0000000..dfd86d2 --- /dev/null +++ b/docs/_posts/2024-02-15-Semana-13.md @@ -0,0 +1,194 @@ +--- +title: "Semana 13 - Comparando soluciones" +categories: + - Registro Semanal +tags: + - ROS2 + - Aprendizaje Automático +--- + +En esta semana se desarrolló una manera de cuantificar cuán buena es cada solución para el problema y qué resultados aporta. + +## Índice + +--- + +## Balanceo del dataset +Se terminó de balancear el dataset para que, a nivel de código, se tuviera una manera mucho mejor de poner etiquetas y, a su vez, modificar estas mismas si era necesario. Quedando la gráfica de la siguiente manera: + +Lo primero que se hace es balancear todas las etiquetas al mismo número de muestras para posteriormente submuestrear en un porcentaje concreto cada label, así dejando los ejemplos menos frecuentes con un número de muestras menor, dejando un dataset más óptimo para entrenar y más manejable. + +--- + +## Gráfica de posición +Para comparar soluciones, lo siguiente fue crear una gráfica donde se mostraran el eje x y el eje y. Primero necesitaríamos un dron que siguiera la línea a la perfección para luego comparar el resto de soluciones. Hay varios topics de donde podemos obtener la posición, pero se decidió sacar directamente de la tf del dron, ya que es el más fiable en este caso y su utilidad es comparar resultados. + +
+ +
Rqt plot graphics
+
+ +También se investigó cómo calcular el error entre ambas gráficas. Los principales métodos que se encontraron fueron: + +1. Error Cuadrático Medio (MSE - Mean Squared Error) +2. Error Absoluto Medio (MAE - Mean Absolute Error) +3. Raíz del Error Cuadrático Medio (RMSE - Root Mean Squared Error) +4. Error Cuadrático Medio Normalizado (NMSE - Normalized Mean Squared Error) +5. Coeficiente de Correlación (por ejemplo, Pearson) +6. Índice de Similitud de Fréchet + +A la hora de grabar rosbags se modificó el comando de la siguiente manera: + +```bash +# En la carpeta drone_driver/training_dataset +timeout 240s ros2 bag record -o folderName /drone0/self_localization/twist /drone0/sensor_measurements/frontal_camera/image_raw /tf +``` + +Esta modificación se realizó porque el anterior topic que monitorizaba las velocidades lo hacía con velocidades relativas al tf global del mapa y por eso no se ajustaban bien. Además, se graba también el topic de las tfs para poder comparar la precisión de la ruta en las distintas ejecuciones. + +El siguiente paso fue graficar el circuito donde se realizarán los test. Primero se hizo con un piloto experto lento para tener más detalle del circuito y luego con el piloto externo que se está usando para generar el dataset de la red. Mostrando además el tiempo de vuelta y juntando los ejes X e Y para que las gráficas fueran más claras. Dando el siguiente resultado: + +
+ +
First expert pilot graphic
+
+ +--- +--- + +Una vez solucionados estos problemas, se volvió a empezar a generar otro dataset. Ahora estos se irán etiquetando y guardando para comparar soluciones. + +--- +--- + +## Dataset 1 +Se conservaron los parámetros de la semana pasada, pero con los problemas que se tuvieron en la misma solucionados + +### Dataset desbalanceado +
+ +
Unbalanced dataset 1
+
+ +
+ +
Loss curve with unbalanced dataset 1
+
+ +Se paró el entrenamiento ya que a partir del epoch 23 la red neuronal se podría estar sobreentrenando. + +Dando el siguiente resultado: +
+ +
Unbalanced dataset result 1
+
+ +Podemos ver cómo en las curvas menos pronunciadas no tiene un suficiente número de muestras, por lo que el dron no llega a realizar la curva ni a completar el circuito. + +--- + +### Dataset totalmente balanceado +
+ +
Balanced dataset 1
+
+ +
+ +
Loss curve with balanced dataset 1
+
+ +Dando el siguiente resultado: +
+ +
Unbalanced dataset result 1
+
+ +--- + +### Dataset parcialmente balanceado + +La matriz de pesos es la siguiente: + +```python3 + # < 4.5 5 inf + weights = [ (1.0, 0.5, 0.4), # < -0.5 + (0.7, 1.0, 0.8), # < -0.25 + (0.3, 0.7, 1.0), # < 0 + (0.3, 0.7, 1.0), # < 0.25 + (0.7, 1.0, 0.8), # < 0.5 + (1.0, 0.5, 0.4)] # < inf +``` + +
+ +
Unbalanced dataset 1
+
+ +
+ +
Loss curve with unbalanced dataset 1
+
+ +Dando el siguiente resultado: +
+ +
Unbalanced dataset result 1
+
+ + + + + +Se vio que se tenían avances significativos, pero no eran suficientes ya que no se conseguía completar el circuito. + +--- + +## Etiquetado 2 +Primero se pensó en que la solución podría estar en que no había suficientes etiquetas, por lo que se añadieron 2 etiquetas angulares más para tener en cuenta también los casos más extremos. + +### Dataset desbalanceado +
+ +
Unbalanced dataset 2
+
+ +Dando el siguiente dataset semibalanced: + +```python3 + # < 5 5.5 inf + weights = [(0.40, 0.0, 0.0), # < -1.5 + (0.50, 0.3, 0.0), # < -0.6 + (0.60, 0.5, 0.7), # < -0.3 + (0.70, 0.8, 0.99), # < 0 + (0.70, 0.8, 0.99), # < 0.3 + (0.60, 0.5, 0.7), # < 0.6 + (0.50, 0.3, 0.0), # < 1.5 + (0.40, 0.0, 0.0)] # < inf +``` + +
+ +
Semibalanced dataset 2
+
+ +El siguiente presentimiento fue que tal vez con 30 epochs la red neuronal no era capaz de aprender adecuadamente. A partir de esta misma distribución de dataset, se hicieron pruebas con 3 tamaños de batch distintos: 4, 20 y 100. El tiempo de convergencia fue parecido, aunque el resultado fue sin duda por parte de epoch = 4: + +* **Naranja:** Epoch = 4 +* **Morado:** Epoch = 20 +* **Gris:** Epoch = 100 + +
+ +
Epoch test
+
+ +Además, hay que destacar que la media de frecuencia de publicación era en torno a 30 Hz. + +
+ +
Inference frequency
+
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