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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_train2.py 6.06 KB
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tianhangz authored 2018-11-05 18:27 . 修改版
# coding=utf-8
# 搭建模型,训练,预测
# 使用Inception-v3模型进行迁移学习
import tensorflow as tf
import glob
import os.path
import numpy as np
from tensorflow.python.platform import gfile
import tensorflow.contrib.slim as slim
##########################1. 定义训练过程中将要使用到的常量################################
# 加载通过tensorflow-slim定义好的inception——v3模型
import tensorflow.contrib.slim.python.slim.nets.inception_v3 as inception_v3
# 处理好的程序文件
INPUT_DATA = 'datasets/processed_data.npy'
# 保存训练好模型的路径
TRAIN_FILE = 'save_model'
# 谷歌提供训练好的模型的路径
CKPT_FILE = 'inception_v3/inception_v3.ckpt'
# 定义训练使用到的参数
LEARNING_RATE = 0.0001
STEPS = 300
BATCH = 20
N_CLASSES = 5
# 不需要从谷歌训练好的模型中加载参数,指的最后的全连接层,给出的是参数的前缀
CHECKPOINT_EXCLUDE_SCOPES = 'InceptionV3/Logits,InceptionV3/AuxLogits'
# 需要训练的网络层参数名称,在fine-tuning的过程中就是最后的全连接层,这里给出的是参数的前缀
TRAINABLE_SCOPES='InceptionV3/Logits, InceptionV3/AuxLogit'
##########################2.获取所有需要从谷歌训练好的模型中加载的参数######################
def get_tuned_variables():
exclusions = [scope.strip() for scope in CHECKPOINT_EXCLUDE_SCOPES.split(',')]
variables_to_restore = []
# 枚举inception-v3模型中所有的参数,然后判断是否需要从加载列表中移除。
for var in slim.get_model_variables():
excluded = False
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
excluded = True
break
if not excluded:
variables_to_restore.append(var)
return variables_to_restore
##############################3.获取所有需要训练的变量列表##################################
def get_trainable_variables():
scopes = [scope.strip() for scope in TRAINABLE_SCOPES.split(',')]
variables_to_train = []
# 枚举所有需要训练的参数前缀,并通过这些前缀找到所有需要训练的参数。
for scope in scopes:
variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
variables_to_train.extend(variables)
return variables_to_train
###################################4.定义训练过程#############################
def main():
# 加载预处理好的数据。
processed_data = np.load(INPUT_DATA)
training_images = processed_data[0]
n_training_example = len(training_images)
training_labels = processed_data[1]
validation_images = processed_data[2]
validation_labels = processed_data[3]
testing_images = processed_data[4]
testing_labels = processed_data[5]
print("%d training examples, %d validation examples and %d testing examples." % (
n_training_example, len(validation_labels), len(testing_labels)))
# 定义inception-v3的输入,images为输入图片,labels为每一张图片对应的标签。
images = tf.placeholder(tf.float32, [None, 299, 299, 3], name='input_images')
labels = tf.placeholder(tf.int64, [None], name='labels')
# 定义inception-v3模型。因为谷歌给出的只有模型参数取值,所以这里
# 需要在这个代码中定义inception-v3的模型结构。虽然理论上需要区分训练和
# 测试中使用到的模型,也就是说在测试时应该使用is_training=False,但是
# 因为预先训练好的inception-v3模型中使用的batch normalization参数与
# 新的数据会有出入,所以这里直接使用同一个模型来做测试。
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
logits, _ = inception_v3.inception_v3(
images, num_classes=N_CLASSES, is_training=True)
trainable_variables = get_trainable_variables()
# 定义损失函数和训练过程。
tf.losses.softmax_cross_entropy(
tf.one_hot(labels, N_CLASSES), logits, weights=1.0)
total_loss = tf.losses.get_total_loss()
train_step = tf.train.RMSPropOptimizer(LEARNING_RATE).minimize(total_loss)
# 计算正确率。
with tf.name_scope('evaluation'):
correct_prediction = tf.equal(tf.argmax(logits, 1), labels)
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 定义加载Google训练好的Inception-v3模型的Saver。
load_fn = slim.assign_from_checkpoint_fn(
CKPT_FILE,
get_tuned_variables(),
ignore_missing_vars=True)
# 定义保存新模型的Saver。
saver = tf.train.Saver()
with tf.Session() as sess:
# 初始化没有加载进来的变量。
init = tf.global_variables_initializer()
sess.run(init)
# 加载谷歌已经训练好的模型。
print('Loading tuned variables from %s' % CKPT_FILE)
load_fn(sess)
start = 0
end = BATCH
for i in range(STEPS):
_, loss = sess.run([train_step, total_loss], feed_dict={
images: training_images[start:end],
labels: training_labels[start:end]})
if i % 30 == 0 or i + 1 == STEPS:
saver.save(sess, TRAIN_FILE, global_step=i)
validation_accuracy = sess.run(evaluation_step, feed_dict={
images: validation_images, labels: validation_labels})
print('Step %d: Training loss is %.1f Validation accuracy = %.1f%%' % (
i, loss, validation_accuracy * 100.0))
start = end
if start == n_training_example:
start = 0
end = start + BATCH
if end > n_training_example:
end = n_training_example
# 在最后的测试数据上测试正确率。
test_accuracy = sess.run(evaluation_step, feed_dict={
images: testing_images, labels: testing_labels})
print('Final test accuracy = %.1f%%' % (test_accuracy * 100))
if __name__ == '__main__':
main()

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