NVIDIA End-to-End 自动驾驶深度学习算法
之前一篇文章提到的要用tf来实现，不过那时候写的代码版本已经旧了，所以干脆用keras重写一个，后端依然使用的是tensorflow。
本文使用Udacity开源的无人驾驶模拟器生成数据并测试效果：
udacity/self-driving-car-sim
如何使用模拟器可以参考：
udacity/CarND-Behavioral-Cloning-P3
先上模型结构图
其实看代码更清晰，不的不说Keras真是一个非常好用的工具，写出来的模型非常干净整洁。
所用到的API
Guide to the Sequential model
Convolutional Layers conv2d - Keras Documentation
Convolutional Layers cropping2d - Keras Documentation
Core Layers flatten - Keras Documentation
Core Layers dense - Keras Documentation
Core Layers lambda - Keras Documentation
Core Layers dropout - Keras Documentation
# 数据载入依赖 import csv import cv2 import numpy as np # 模型构建依赖 from keras.models import Sequential from keras.layers import Conv2D, Cropping2D from keras.layers import Flatten, Dense, Lambda, Dropout # 模型数据处理所需要的依赖 from sklearn.utils import shuffle from sklearn.model_selection import train_test_split # 加载训练数据 LINES = [] # 数据矫正值，用于纠正方向盘角度 CORRECTION = 0.2 # 从数据集中读取必要数据 with open('./data/driving_log.csv') as csvfile: READER = csv.reader(csvfile) for line in READER: LINES.append(line) # 分组出训练集与校验集 TRAIN_SAMPLES, VALIDATION_SAMPLES = train_test_split(LINES, test_size=0.2) # 用一个生成器来传入训练数据，避免出现内存不足的情况 def generator(samples, batch_size=32): """ generat training samples """ num_samples = len(samples) while 1: # Loop forever so the generator never terminates shuffle(samples) for offset in range(0, num_samples, batch_size): batch_samples = samples[offset:offset+batch_size] images = [] measurements = [] for batch_sample in batch_samples: # Use 3 cameras measurement = float(batch_sample[3]) measurement_left = measurement + CORRECTION measurement_right = measurement - CORRECTION for i in range(3): source_path = batch_sample[i] filename = source_path.split('/')[-1] current_path = './data/IMG/' + filename image_bgr = cv2.imread(current_path) # OpenCV的大坑，大坑啊！！！ image = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB) # image = cv2.resize(image, (80, 160), cv2.INTER_NEAREST) images.append(image) # Augment image images.append(cv2.flip(image, 1)) measurements.extend([measurement, measurement*-1, measurement_left, measurement_left*-1, measurement_right, measurement_right*-1]) x_train = np.array(images) y_train = np.array(measurements) yield shuffle(x_train, y_train) TRAIN_GENERATOR = generator(TRAIN_SAMPLES, batch_size=32) VALIDATION_GENERATOR = generator(VALIDATION_SAMPLES, batch_size=32) # 输入帧的参数 ROW, COL, CH = 160, 320, 3 # Model MODEL = Sequential() # 执行图像归一化 MODEL.add(Lambda(lambda x: x / 127.5 - 1.0, input_shape=(ROW, COL, CH))) # 剪裁图片 只保留和道路相关的部分 #MODEL.add(Cropping2D(cropping=((60, 20), (0, 0)))) # 利用卷积层来进行特征提取 MODEL.add(Conv2D(24, 5, strides=(2, 2), activation='relu')) MODEL.add(Dropout(0.7)) MODEL.add(Conv2D(36, 5, strides=(2, 2), activation='relu')) MODEL.add(Conv2D(48, 5, strides=(2, 2), activation='relu')) MODEL.add(Conv2D(64, 3, activation='relu')) MODEL.add(Conv2D(64, 3, activation='relu')) # 如果出现过拟合可以添加Dropout # MODEL.add(Dropout(0.8)) # 全链接层 MODEL.add(Flatten()) MODEL.add(Dense(100)) MODEL.add(Dense(50)) MODEL.add(Dense(10)) MODEL.add(Dense(1)) MODEL.compile(loss='mse', optimizer='adam') MODEL.fit_generator(TRAIN_GENERATOR, steps_per_epoch=len(TRAIN_SAMPLES), validation_data=VALIDATION_GENERATOR, validation_steps=len(VALIDATION_SAMPLES), epochs=3) # 保存模型 MODEL.save('model.h5')然后车子就可以开起来咯：
提高训练效果的注意事项：
使用模拟器生成数据的时候尽量使用摇杆或者鼠标，这样数据比较平滑尽量保持车在车道中间，如果车技不佳就开慢点。注意opencv读取图片颜色顺序为BGR，这是一个大坑，一开始训练的时候总往水里开，后来才发现原来是颜色搞反了。