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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 spread retract

https://github.com/Timthony/self_drive

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zth_train.py 6.52 KB
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tianhangz authored 2018-11-05 18:27 . 修改版
# 搭建深度学习模型
# 导入库
# 自动驾驶模型真实道路模拟行驶
import keras
import tensorflow
import sys
import os
import h5py
import numpy as np
import glob
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Lambda, Conv2D, MaxPooling2D, Dropout, Dense, Flatten
from keras.models import load_model, Model, Input
from keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard
from keras.optimizers import Adam, SGD
np.random.seed(0)
# 全局变量
IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNELS = 120, 160, 3
INPUT_SHAPE = (IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNELS)
# step1,载入数据,并且分割为训练和验证集
# 问题,数据集太大了,已经超过计算机内存
def load_data():
# load
image_array = np.zeros((1, 120, 160, 3)) # 初始化
label_array = np.zeros((1, 5), 'float')
training_data = glob.glob('training_data_npz/*.npz')
# 匹配所有的符合条件的文件,并将其以list的形式返回。
print("匹配完成。开始读入")
print("一共%d轮", len(training_data))
# if no data, exit,容错判断
if not training_data:
print("No training data in directory, exit")
sys.exit()
i = 0
for single_npz in training_data:
with np.load(single_npz) as data:
print(data.keys())
i = i + 1
print("在打印关键值", i)
train_temp = data['train_imgs']
train_labels_temp = data['train_labels']
image_array = np.vstack((image_array, train_temp)) # 把文件读取都放入,内存
label_array = np.vstack((label_array, train_labels_temp))
print("第%d轮完成", i)
print("循环完了")
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
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
# step2 建立模型
def build_model(keep_prob):
print("开始编译模型")
model = Sequential()
model.add(Lambda(lambda x: (x/102.83 - 1), input_shape = INPUT_SHAPE))
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(Conv2D(64, (3, 3),activation='elu'))
model.add(Conv2D(64, (3, 3),activation='elu'))
model.add(Dropout(keep_prob)) # Dropout将在训练过程中每次更新参数时随机断开一定百分比(p)的输入神经元连接
model.add(Flatten())
#model.add(Dense(500, activation='elu'))
model.add(Dense(250, activation='elu'))
#model.add(Dense(50, activation='elu'))
model.add(Dense(5))
model.summary()
return model
# step3 训练模型
def train_model(model, learning_rate, nb_epoch, samples_per_epoch,
batch_size, X_train, X_valid, y_train, y_valid):
# 值保存最好的模型存下来
checkpoint = ModelCheckpoint('model-{epoch:03d}.h5',
monitor='val_loss',
verbose=0,
save_best_only=True,
mode='min')
# EarlyStopping patience:当earlystop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='loss', min_delta=.0005, patience=10,
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)
# 编译神经网络模型,loss损失函数,optimizer优化器, metrics列表,包含评估模型在训练和测试时网络性能的指标
model.compile(loss='mean_squared_error', optimizer=keras.optimizers.Adam(lr=learning_rate), metrics=['accuracy'])
# 训练神经网络模型,batch_size梯度下降时每个batch包含的样本数,epochs训练多少轮结束,
# verbose是否显示日志信息,validation_data用来验证的数据集
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)
# step4
# 可以一个batch一个batch进行训练,CPU和GPU同时开工
def batch_generator(X, y, batch_size):
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)
# step5 评估模型
#def evaluate(x_test, y_test):
#score = model.evaluate(x_test, y_test, verbose=0)
#print('Test loss:', score[0])
#print('Test accuracy:', score[1])
def main():
# 打印出超参数
print('-'*30)
print('parameters')
print('-'*30)
keep_prob = 0.5
learning_rate = 0.0001
nb_epoch = 100
samples_per_epoch = 3000
batch_size = 30
print('keep_prob = ', keep_prob)
print('learning_rate = ', learning_rate)
print('nb_epoch = ', nb_epoch)
print('samples_per_epoch = ', samples_per_epoch)
print('batch_size = ', batch_size)
print('-' * 30)
# 开始载入数据
data = load_data()
print("数据加载完毕")
# 编译模型
model = build_model(keep_prob)
# 在数据集上训练模型,保存成model.h5
train_model(model, learning_rate, nb_epoch, samples_per_epoch, batch_size, *data)
print("模型训练完毕")
if __name__ == '__main__':
main()

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