Udacity 自駕車 project 4

Kevin Chiu

Published in

CodingJourney

4 min readJul 1, 2020

Project 4: Behavior Cloning

Github: Project 4_Behavioral-Cloning

大致介紹了：

1. Keras應用

2. Transfer Learning

3. CNNs History and introduction

4. Behavior Cloning project

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1. Keras應用

在上一個project 3用了tensorflow來刻出CNN，這次介紹了使用keras來更有效率的刻出CNN。

簡單比較之前用tensorflow 和這次用keras，可以發現keras方便很多:

2. Transfer Learning

Case 1: Small Data Set, Similar Data

If the new data set is small and similar to the original training data:

簡單來說，保留前面CNN的weight，修改後面FCN的輸出數量並重新計算weight

slice off the end of the neural network
add a new fully connected layer that matches the number of classes in the new data set
randomize the weights of the new fully connected layer; freeze all the weights from the pre-trained network
train the network to update the weights of the new fully connected layer

Case 2: Small Data Set, Different Data

If the new data set is small and different from the original training data:

slice off most of the pre-trained layers near the beginning of the network
add to the remaining pre-trained layers a new fully connected layer that matches the number of classes in the new data set
randomize the weights of the new fully connected layer; freeze all the weights from the pre-trained network
train the network to update the weights of the new fully connected layer

Case 3: Large Data Set, Similar Data

If the new data set is large and similar to the original training data:

remove the last fully connected layer and replace with a layer matching the number of classes in the new data set
randomly initialize the weights in the new fully connected layer
initialize the rest of the weights using the pre-trained weights
re-train the entire neural network

Case 4: Large Data Set, Different Data

If the new data set is large and different from the original training data:

remove the last fully connected layer and replace with a layer matching the number of classes in the new data set
retrain the network from scratch with randomly initialized weights
alternatively, you could just use the same strategy as the “large and similar” data case

3. CNN introduction

先介紹了imagenet的功用（一個巨大的dataset）

甚至可以透過Keras Applications直接導入已經pre-trained的知名object detection models, 例如 ResNet50, VGG16, InceptionV3…

AlexNet：類似LeNet的架構

VGG16/19 (數字代表層數)：使用多個3*3 convolution & pooling 作為架構

可以簡單透過keras import VGG16

from keras.applications.vgg16 import VGG16

model = VGG16(weights='imagenet', include_top=False)

GoogLeNet/Inception：導入inception module的概念

GoogLeNet 特點是速度很快，因此可應用在自駕車上

from keras.applications.inception_v3 import InceptionV3

model = InceptionV3(weights='imagenet', include_top=False)

ResNet: 透過增加層數(152 layers)來達到更好的低錯誤率的表現

但一般來說增加層數反而會使網路退化(Degradation problem)，深度增加時，網絡準確度出現飽和，甚至出現下降。

那ResNet怎麼解決這個問題的呢？ Ans: 殘差學習 Residual learning

ResNet網絡是參考了VGG19網絡，在其基礎上進行了修改，並通過短路機制加入了殘差單元

詳情請見：ResNet intro

from keras.applications.resnet50 import ResNet50

model = ResNet50(weights='imagenet', include_top=False)

4. Behavior Cloning project

最後就進入我們的專案了！

這次目標是要讓車子自己學習在路上跑，不要超過車道邊緣

這主要就是要累積一堆讓車子盡量維持在中間的影像data，udacity的project引導都有提到，但實作起來還是花了一些時間

在做這個專案，建構CNN反而不難，麻煩的是需要自己累積data，並不斷在模擬器上測試。在訓練&測試的過程中，車子常常在影片第46秒那個轉彎開出跑道，因此又特別針對那個地方做了許多的圖片和方向盤的資料擷取，最後終於訓練出完整跑完整條道路的模型。

— — Additional Resources on Deep Learning — —

Behavioral Cloning

The below paper shows one of the techniques Waymo has researched using imitation learning (aka behavioral cloning) to drive a car.

ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst

Object Detection and Tracking

The below papers include various deep learning-based approaches to 2D and 3D object detection and tracking.

SSD: Single Shot MultiBox Detector

1. SSD結合了YOLO中的回歸思想和Faster-RCNN中的Anchor機制，使用全圖各個位置的多尺度區域特徵進行回歸，既保持了YOLO速度快的特性，也保證了窗口預測的跟Faster-RCNN一樣比較精準。

2. SSD的核心是在特徵圖上採用卷積核來預測一系列Default Bounding Boxes的類別、坐標偏移。為了提高檢測準確率，SSD在不同尺度的特徵圖上進行預測。

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection