基于树莓派的自动驾驶小车，利用树莓派和Tensorflow实现小车在赛道的自动驾驶

#import pandas as pd # data analysis toolkit - create, read, update, delete datasets import numpy as np #matrix math from sklearn.model_selection import train_test_split #to split out training and testing data #keras is a high level wrapper on top of tensorflow (machine learning library) #The Sequential container is a linear stack of layers import tensorflow from keras.models import Sequential #popular optimization strategy that uses gradient descent from keras.optimizers import Adam,SGD #to save our model periodically as checkpoints for loading later from keras.callbacks import ModelCheckpoint from keras.callbacks import EarlyStopping from keras.callbacks import TensorBoard #what types of layers do we want our model to have? from keras.layers import Lambda, Conv2D, MaxPooling2D, Dropout, Dense, Flatten from keras.models import Model, Input from keras.models import load_model import glob import os import glob import h5py import sys import keras #for debugging, allows for reproducible (deterministic) results np.random.seed(0) IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNELS = 120, 160, 3 INPUT_SHAPE = (IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNELS) def load_data(): """ Load training data and split it into training and validation set """ # load training data image_array = np.zeros((1,120,160,3)) label_array = np.zeros((1, 5), 'float') training_data = glob.glob('training_data_npz/*.npz') # if no data, exit if not training_data: print ("No training data in directory, exit") sys.exit() for single_npz in training_data: with np.load(single_npz) as data: print(data.keys()) train_temp = data['train_imgs'] train_labels_temp = data['train_labels'] image_array = np.vstack((image_array, train_temp)) label_array = np.vstack((label_array, train_labels_temp)) X = image_array[1:, :] y = label_array[1:, :] print ('Image array shape: '+ str(X.shape)) print ('Label array shape: '+ str(y.shape)) print(np.mean(X)) print(np.var(X)) #now we can split the data into a training (80), testing(20), and validation set #thanks scikit learn X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=0) return X_train, X_valid, y_train, y_valid def build_model(keep_prob): """ NVIDIA model used Image normalization to avoid saturation and make gradients work better. Convolution: 5x5, filter: 24, strides: 2x2, activation: ELU Convolution: 5x5, filter: 36, strides: 2x2, activation: ELU Convolution: 5x5, filter: 48, strides: 2x2, activation: ELU Convolution: 3x3, filter: 64, strides: 1x1, activation: ELU Convolution: 3x3, filter: 64, strides: 1x1, activation: ELU Drop out (0.5) Fully connected: neurons: 100, activation: ELU Fully connected: neurons: 50, activation: ELU Fully connected: neurons: 10, activation: ELU Fully connected: neurons: 1 (output) # the convolution layers are meant to handle feature engineering the fully connected layer for predicting the steering angle. dropout avoids overfitting ELU(Exponential linear unit) function takes care of the Vanishing gradient problem. """ # IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNELS = 240, 240, 3 # INPUT_SHAPE = (IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNELS) model = Sequential() model.add(Lambda(lambda x: (x/102.83-1), input_shape=INPUT_SHAPE))#/85-5139 model.add(Conv2D(24, (5, 5), activation='elu', strides=(2, 2))) model.add(Conv2D(36, (5, 5), activation='elu', strides=(2, 2))) model.add(Conv2D(48, (5, 5), activation='elu', strides=(2, 2))) #model.add(Dropout(0.5)) model.add(Conv2D(64, (3, 3), activation='elu')) #model.add(Dropout(0.3)) model.add(Conv2D(64, (3, 3), activation='elu')) model.add(Dropout(keep_prob)) model.add(Flatten()) model.add(Dense(500, activation='elu')) #model.add(Dropout(0.1)) model.add(Dense(250, activation='elu')) #model.add(Dropout(0.1)) model.add(Dense(50, activation='elu')) #model.add(Dropout(0.1)) model.add(Dense(5)) model.summary() return model def train_model(model,learning_rate,nb_epoch,samples_per_epoch, batch_size,X_train, X_valid, y_train, y_valid): """ Train the model """ #Saves the model after every epoch. #quantity to monitor, verbosity i.e logging mode (0 or 1), #if save_best_only is true the latest best model according to the quantity monitored will not be overwritten. #mode: one of {auto, min, max}. If save_best_only=True, the decision to overwrite the current save file is # made based on either the maximization or the minimization of the monitored quantity. For val_acc, #this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically # inferred from the name of the monitored quantity. checkpoint = ModelCheckpoint('model-{epoch:03d}.h5', monitor='val_loss', verbose=0, save_best_only=True, mode='min') # EarlyStopping patience：当early # stop被激活（如发现loss相比上一个epoch训练没有下降），则经过patience个epoch后停止训练。 # mode：‘auto’，‘min’，‘max’之一，在min模式下，如果检测值停止下降则中止训练。在max模式下，当检测值不再上升则停止训练。 early_stop = EarlyStopping(monitor='val_loss', min_delta=.0005, patience=4, verbose=1, mode='min') tensorboard = TensorBoard(log_dir='./logs', histogram_freq=0, batch_size=20, write_graph=True, write_grads=True, write_images=True, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None) #calculate the difference between expected steering angle and actual steering angle #square the difference #add up all those differences for as many data points as we have #divide by the number of them #that value is our mean squared error! this is what we want to minimize via #gradient descent #opt = SGD(lr=0.0001) model.compile(loss='mean_squared_error', optimizer=Adam(lr=learning_rate),metrics=['accuracy'])# #Fits the model on data generated batch-by-batch by a Python generator. #The generator is run in parallel to the model, for efficiency. #For instance, this allows you to do real-time data augmentation on images on CPU in #parallel to training your model on GPU. #so we reshape our data into their appropriate batches and train our model simulatenously model.fit_generator(batch_generator(X_train, y_train,batch_size), steps_per_epoch=samples_per_epoch/batch_size, epochs=nb_epoch, max_queue_size=1, validation_data=batch_generator(X_valid, y_valid, batch_size), validation_steps=len(X_valid)/batch_size, callbacks=[tensorboard, checkpoint, early_stop], verbose=2) ## model.fit(X_train,y_train,samples_per_epoch,nb_epoch,max_q_size=1,X_valid,y_valid,\ ## nb_val_samples=len(X_valid),callbacks=[checkpoint],verbose=1) def batch_generator(X, y, batch_size): """ Generate training image give image paths and associated steering angles """ images = np.empty([batch_size, IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNELS]) steers = np.empty([batch_size,5]) while True: i = 0 for index in np.random.permutation(X.shape[0]): images[i] = X[index] steers[i] = y[index] i += 1 if i == batch_size: break yield (images, steers) def main(): #print parameters print('-' * 30) print('Parameters') print('-' * 30) keep_prob = 0.5 lea