『TensorFlow』迁移学习_他山之石，可以攻玉_V2
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絮絮叨叨
开学以来课业繁重（虽然也不怎么认真听讲...）一直没怎么闲着，一直没写什么长的代码，正巧好几件事赶在一块了：导师催着我把超新星检测的神经网络实现，我不想用现有的demo（当然一时间也没有合适的，之前写的AlexNet不太喜欢，毕竟是个老掉牙的网络架构，很多结构今天看来并不漂亮），而且分类之后需要考虑扩充为检测网络；和武师兄一起参加了个遥感影像识别的比赛，由于我对遥感是一窍不通而且也没什么兴趣学（逃），所以基本上要做的也是利用tensorflow搭建DL网络了，正好也需要一个图片处理的DL架构，不过这里需要我去额外学习一下语义分割了；这次的十一假期真的挺长，天文系的筒子们都回家了，新班级除了宿舍的熟人也不多，正是宅学校的好时机。于是就利用这个假期自己写了个迁移学习的类，蛮大的架构（原谅我见识少）完全自己来设计机会还真是不多，搭建的速度很慢，而且遇到了不少问题，当然过程是曲折的，结果算是圆满的，至此，分类部分是搭建完了，还是有蛮多体会的，对tensorflow以及编程思想什么的（玄学大师），记录一下，算是不愧对这个假期233
简介
1.这暂时是个基于vgg16（可更换模型）的迁移学习的分类网络，之后会考虑升级语义分割部分以适应之后的任务，不过应该是很久以后了（因为太难...）。
2.除了分类外，它可以做一部分图像预处理的活，暂时是批量图片尺寸调整（特意升级成了三线程加速，飞一般感觉），以后可能会升级为图像增强模块（因为现在的数据已经被师兄做了扩充，所以暂时用不到增强，也就没加）。
3.这是个人的一点执念了，前几天开始写的时候我对它的期望是（程序本体）简洁，（调用方式）简单，现在我发现基本不可能同时实现了...，我选择了前者，虽然是一个class，实际上各个方法之间基本是线性的，交叉调用很少，数据流基本也就只有一个方向，函数也写的简单易懂（当然和我菜也有关系），相对的，main的调用部分看起来又臭又长，不过套路模式很固定，我感觉还是不错的。
思路是这样的，对于一批图片，
- 我们首先批量裁剪，裁剪为网络需要的大小
- 然后送入预训练好的模型中，完成特征提取（实际就是个数据降维），写入新的二进制文件
- 自写一个分类头（实际就是个单层的神经网络），使用降维特征及标签进行训练
整个流程中我们使用字典来标记数据和标签，作为各个方法之间交流的格式。
代码
对于我的二分类问题，下面的程序会生成如下的目录架构，
其中vgg16文件夹和image文件夹以及其中文件（预训练模型和训练数据）是需要自己事先准备的（废话），其他的都由程序自己生成。
程序如下，
import os
import glob
import pprint
import datetime
import threading
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
os.environ[‘TF_CPP_MIN_LOG_LEVEL‘] = ‘2‘
class image_process():
def __init__(self,
train_path = ‘./image/SNRDetect/train‘,
test_path=‘./image/SNRDetect/val‘):
# train0_path = ‘./image/SNRDetect/train/0‘,
# train1_path = ‘./image/SNRDetect/train/1‘,
# val0_path = ‘./image/SNRDetect/val/0‘,
# val1_path = ‘./image/SNRDetect/val/1‘):
‘‘‘
读入图片名称，每个类别图片名称以一维numpy数组形式存储
为了保证扩展性，本class下各方法尽量使用文件名array作为参数，而不去直接调用内置属性
目录给到分类一级（即path的下一级目录是类别名，里面是该类的文件）
‘‘‘
# self.dict = {}
# self.dict[‘train0_name‘] = [train0_path + ‘/‘ + name for name in np.squeeze([name[2] for name in os.walk(train0_path)])]
# self.dict[‘train1_name‘] = [train1_path + ‘/‘ + name for name in np.squeeze([name[2] for name in os.walk(train1_path)])]
# self.dict[‘val0_name‘] = [val0_path + ‘/‘ + name for name in np.squeeze([name[2] for name in os.walk(val0_path)])]
# self.dict[‘val1_name‘] = [val1_path + ‘/‘ + name for name in np.squeeze([name[2] for name in os.walk(val1_path)])]
# map(np.random.shuffle, [self.dict[‘train0_name‘], self.dict[‘train1_name‘], self.dict[‘val0_name‘], self.dict[‘val1_name‘]])
# np.random.shuffle(self.dict[‘train0_name‘])
# np.random.shuffle(self.dict[‘train1_name‘])
# np.random.shuffle(self.dict[‘val0_name‘])
# np.random.shuffle(self.dict[‘val1_name‘])
dir_train = [f[0] for f in [file for file in os.walk(train_path)][1:]]
file_train = [f[2] for f in [file for file in os.walk(train_path)][1:]]
self.train_dict = {}
for k,v in zip(dir_train,file_train):
np.random.shuffle(v)
self.train_dict[k.split(‘\\\\‘)[-1]] = v
dir_test = [f[0] for f in [file for file in os.walk(test_path)][1:]]
file_test = [f[2] for f in [file for file in os.walk(test_path)][1:]]
self.test_dict = {}
for k,v in zip(dir_test,file_test):
np.random.shuffle(v)
self.test_dict[k.split(‘\\\\‘)[-1]] = v
def reload_dict(self,
train_path = ‘./image/SNRDetect/train‘,
test_path=‘./image/SNRDetect/val‘):
dir_train = [f[0] for f in [file for file in os.walk(train_path)][1:]]
file_train = [f[2] for f in [file for file in os.walk(train_path)][1:]]
self.train_dict = {}
for k,v in zip(dir_train,file_train):
np.random.shuffle(v)
self.train_dict[k.split(‘\\\\‘)[-1]] = v
dir_test = [f[0] for f in [file for file in os.walk(test_path)][1:]]
file_test = [f[2] for f in [file for file in os.walk(test_path)][1:]]
self.test_dict = {}
for k,v in zip(dir_test,file_test):
np.random.shuffle(v)
self.test_dict[k.split(‘\\\\‘)[-1]] = v
# def re_load(self,
# train0_path = ‘./image/SNRDetect/train/0‘,
# train1_path = ‘./image/SNRDetect/train/1‘,
# val0_path = ‘./image/SNRDetect/val/0‘,
# val1_path = ‘./image/SNRDetect/val/1‘):
# self.dict[‘train0_name‘] = [train0_path + ‘/‘ + name for name in
# np.squeeze([name[2] for name in os.walk(train0_path)])]
# self.dict[‘train1_name‘] = [train1_path + ‘/‘ + name for name in
# np.squeeze([name[2] for name in os.walk(train1_path)])]
# self.dict[‘val0_name‘] = [val0_path + ‘/‘ + name for name in
# np.squeeze([name[2] for name in os.walk(val0_path)])]
# self.dict[‘val1_name‘] = [val1_path + ‘/‘ + name for name in
# np.squeeze([name[2] for name in os.walk(val1_path)])]
# map(np.random.shuffle,
# [self.dict[‘train0_name‘],self.dict[‘train1_name‘],self.dict[‘val0_name‘],self.dict[‘val1_name‘]])
def resize_img(self,
name_list,
img_path=‘./image/SNRDetect/train/0‘,
file_name=‘test/0‘,
size=(224,224)):
self._name_list = name_list
def resize_img_thread():
print(‘图片尺寸调整子线程(代号{})启动‘.format(threading.current_thread().name))
if not os.path.exists(‘resize_img‘):
os.mkdir(‘./resize_img‘)
if not os.path.exists(‘resize_img/train‘):
os.mkdir(‘./resize_img/train‘)
if not os.path.exists(‘resize_img/test‘):
os.mkdir(‘./resize_img/test‘)
# if not os.path.exists(‘./resize_img/{}‘.format(file_name.split(‘/‘)[-1])):
if not os.path.exists(‘./resize_img/{}‘.format(file_name)):
os.mkdir(‘./resize_img/{}‘.format(file_name))
with tf.Session() as sess:
for img in self._name_list:
self._name_list.remove(img)
print(‘\\r‘+img)
print(os.path.join(img_path,img))
img_raw = tf.gfile.FastGFile(os.path.join(img_path,img), ‘rb‘).read()
img_0 = tf.image.decode_jpeg(img_raw)
img_1 = tf.image.resize_images(img_0, size, method=3)
re_img = np.asarray(sess.run(img_1),dtype=‘uint8‘)
# print(img.split(‘/‘)[-1].split(‘.‘))
with tf.gfile.FastGFile(‘./resize_img/{2}/{0}_{1}.jpeg‘.format(
img.split(‘/‘)[-1].split(‘.‘)[0],
size[0],
file_name) ,‘wb‘) as f:
f.write(sess.run(tf.image.encode_jpeg(re_img)))
f.flush()
# 多线程调整图片尺寸
threads = []
t1 = threading.Thread(target=resize_img_thread, name=‘resize_img_thread:0‘)
threads.append(t1)
t2 = threading.Thread(target=resize_img_thread, name=‘resize_img_thread:1‘)
threads.append(t2)
t3 = threading.Thread(target=resize_img_thread, name=‘resize_img_thread:2‘)
threads.append(t3)
for t in threads:
t.start()
for t in threads:
t.join()
def bottlenecks_tensor_write(self,
name_list,
img_path=‘./image/SNRDetect/train/0‘,
file_name = ‘train/0‘,
model_name = ‘vgg16/vgg16.tfmodel‘):
‘‘‘
张量瓶颈层文件写入方法，路径为：bottleneck/类名/文件名
:param name_list: 图片名称列表
:param img_path: 图片路径（对应上面的列表）
:param file_name: 保存张量的文件名称
:param model_dir: 模型文件夹
:param model_name: 模型文件名
:return:
‘‘‘
self._name_list = name_list
if not os.path.exists(‘bottleneck‘):
os.mkdir(‘./bottleneck‘)
if not os.path.exists(‘bottleneck/train‘):
os.mkdir(‘./bottleneck/train‘)
if not os.path.exists(‘bottleneck/test‘):
os.mkdir(‘./bottleneck/test‘)
if not os.path.exists(‘./bottleneck/{}‘.format(file_name)):
os.mkdir(‘./bottleneck/{}‘.format(file_name))
def bottlenecks_tensor_get_thread():
print(‘瓶颈层张量获取子线程(代号{})启动‘.format(threading.current_thread().name))
Pp = pprint.PrettyPrinter()
g = tf.Graph()
with g.as_default():
with open(os.path.join(model_name),‘rb‘) as f: # 阅读器上下文
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# 获取输入层tensor和瓶颈层tensor
image_input_tensor, bottleneck_tensor = tf.import_graph_def(graph_def,
return_elements=[‘images:0‘,
‘MatMul:0‘])
#‘conv5_3/conv5_3:0‘])
# Pp.pprint(g.get_operations())
with tf.Session() as sess:
Pp.pprint(self._name_list)
for img in self._name_list:
self._name_list.remove(img)
print(‘\\r‘ + os.path.join(img_path,img))
image_data = open(os.path.join(img_path,img),‘rb‘).read()
# 防止array被压缩
np.set_printoptions(threshold=np.nan)
# 张量对象是不能作为feed对象的，而且维度必须是4维
bottleneck_values = sess.run(bottleneck_tensor,
feed_dict={image_input_tensor:sess.run(tf.expand_dims(
tf.image.decode_jpeg(image_data),axis=0))})
# bottleneck_string = ‘,‘.join(str(x) for x in bottleneck_values)
# with open(‘./bottleneck/{1}/{0}.txt‘.format(
# img.split(‘/‘)[-1].split(‘.‘)[0], file_name) ,‘w‘) as f:
# f.write(bottleneck_string)
# f.flush()
print(‘./bottleneck/{1}/{0}‘.format(img.split(‘/‘)[-1].split(‘.‘)[0], file_name))
np.save(‘./bottleneck/{1}/{0}‘.format(img.split(‘/‘)[-1].split(‘.‘)[0], file_name),bottleneck_values)
# 多线程书写张量文件
threads = []
t1 = threading.Thread(target=bottlenecks_tensor_get_thread, name=‘bottlenecks_tensor_get_thread:0‘)
threads.append(t1)
t2 = threading.Thread(target=bottlenecks_tensor_get_thread, name=‘bottlenecks_tensor_get_thread:1‘)
threads.append(t2)
t3 = threading.Thread(target=bottlenecks_tensor_get_thread, name=‘bottlenecks_tensor_get_thread:2‘)
threads.append(t3)
for t in threads:
t.start()
for t in threads:
t.join()
def bottlenecks_dict_get(self, path = ‘C:\\Projects\\python3_5\\SNR_DET\\\\bottleneck‘):
‘‘‘
生成bottlenecks文件字典{类名:[文件名]}
:param path:
:return:
‘‘‘
dir = [f[0] for f in [file for file in os.walk(path)][1:]]
file = [f[2] for f in [file for file in os.walk(path)][1:]]
self.bottlenecks_dict = {}
for k,v in zip(dir, file):
np.random.shuffle(v)
if v != []:
self.bottlenecks_dict[k.split(‘\\\\‘)[-1]] = v
def bottlenecks_tensor_get(self,
dict,
batch_size,
train=True,
base_path=‘./‘):
‘‘‘
随机获取batch_size的瓶颈层张量
:param dict:
:param batch_size:
:param base_path:
:return: 两个list，张量list和标签list[array（one_hot编码）]
‘‘‘
bottlenecks = []
ground_truths = []
n_class = len(dict)
for i in range(batch_size):
label_index = np.random.randint(n_class)
label_name = list(dict.keys())[label_index]
bottlenecks_tensor_name = np.random.choice(dict[label_name])
if train == True:
train_or_test = ‘train‘
else:
train_or_test = ‘test‘
bottleneck_string = np.load(os.path.join(base_path,
‘bottleneck‘,
train_or_test,
label_name,
bottlenecks_tensor_name))
bottlenecks.append(bottleneck_string)
ground_truth = np.zeros(n_class,dtype=np.float32)
ground_truth[label_index] = 1.0
ground_truths.append(ground_truth)
return bottlenecks, ground_truths
def class_head(self, n_class=2,batch_size=100,steps=20000):
‘‘‘
分类头
:param n_class:
:return:
‘‘‘
# 添加占位符
X = tf.placeholder(tf.float32,[None,None],name=‘BottleneckInputPlaceholder‘)
y = tf.placeholder(tf.float32,[None,n_class],name=‘label‘)
# 全连接层
with tf.name_scope(‘final_train_ops‘):
Weights = tf.Variable(tf.truncated_normal([4096,n_class],stddev=0.001))
biases = tf.Variable(tf.zeros([n_class]))
logits = tf.matmul(X,Weights) + biases
final_tensor = tf.nn.softmax(logits)
# 交叉熵损失函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=y))
# 优化器
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)
# 准确率计算
with tf.name_scope(‘evaluation‘):
correct_prediction = tf.equal(tf.argmax(final_tensor,1),tf.argmax(y,1))
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
self.bottlenecks_dict_get()
for i in range(steps):
batch_X, batch_y = self.bottlenecks_tensor_get(self.bottlenecks_dict,
batch_size,
base_path=‘./‘)
sess.run(train_step,feed_dict={X:np.squeeze(np.asarray(batch_X)), y:batch_y})
if i % 500 == 0:
print(‘当前为第{0}轮，\\r误差值为:‘.format(i),
sess.run(cross_entropy,feed_dict={X:np.squeeze(np.asarray(batch_X)), y:batch_y}))
print(‘准确率为：{:.2f}%‘.format(
sess.run(evaluation_step,feed_dict={X:np.squeeze(np.asarray(batch_X))*100, y:batch_y})))
def graph_view(self,
log_dir = ‘logs‘,
model_name = ‘vgg16/vgg16.tfmodel‘):
‘‘‘可视化图，并输出节点‘‘‘
Pp = pprint.PrettyPrinter()
g = tf.Graph()
with g.as_default():
with open(model_name,‘rb‘) as f: # 阅读器上下文
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def)
Pp.pprint(g.get_operations())
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(log_dir,sess.graph)
if __name__==‘__main__‘:
SNR = image_process()
start_time = datetime.datetime.now()
‘‘‘
图片裁剪
图片裁剪时建议注释掉本部分之后的程序，在裁剪完成后注释掉本部分单独运行后面的瓶颈张量部分
另注，本方法保存目录一级名称必须是train或者test中的一个，二级目录自取，建议使用类名
‘‘‘
SNR.resize_img(SNR.train_dict[‘0‘],img_path=‘./image/SNRDetect/train/0‘,file_name=‘train/0‘,
size=(224,224))
SNR.resize_img(SNR.train_dict[‘1‘],img_path=‘./image/SNRDetect/train/1‘,file_name=‘train/1‘,
size=(224,224))
SNR.resize_img(SNR.test_dict[‘0‘],img_path=‘./image/SNRDetect/val/0‘,file_name=‘test/0‘,
size=(224,224))
SNR.resize_img(SNR.test_dict[‘1‘],img_path=‘./image/SNRDetect/test/1‘,file_name=‘test/1‘,
size=(224,224))
‘‘‘
瓶颈层张量获取
‘‘‘
# 重载裁剪后的图片
# SNR.reload_dict(train_path=‘C:/Projects/python3_5/SNR_DET/resize_img/train‘,
# test_path=‘C:/Projects/python3_5/SNR_DET/resize_img/test‘)
# 瓶颈层张量文件写入
# SNR.bottlenecks_tensor_write(
# SNR.train_dict[‘0‘],
# img_path=‘./resize_img/train/0‘,
# file_name=‘train/0‘,
# model_name=‘vgg16/vgg16.tfmodel‘)
# SNR.bottlenecks_tensor_write(
# SNR.train_dict[‘1‘],
# img_path=‘./resize_img/train/1‘,
# file_name=‘train/1‘,
# model_name=‘vgg16/vgg16.tfmodel‘)
# SNR.bottlenecks_tensor_write(
# SNR.test_dict[‘0‘],
# img_path=‘./resize_img/test/0‘,
# file_name=‘test/0‘,
# model_name=‘vgg16/vgg16.tfmodel‘)
# SNR.bottlenecks_tensor_write(
# SNR.test_dict[‘1‘],
# img_path=‘./resize_img/test/1‘,
# file_name=‘test/1‘,
# model_name=‘vgg16/vgg16.tfmodel‘)
# 瓶颈层张量文件字典获取
# SNR.bottlenecks_dict_get()
# 利用瓶颈层张量文件和字典训练分类器
# SNR.class_head(n_class=2)
end_time = datetime.datetime.now()
time = end_time - start_time
print(‘\\n总计耗时{}‘.format(time))
注意一下，main部分中大块的注释不是废弃代码，是因为我pycharm的设置，导致不能一次运行全部，实际我的操作是先运行图片裁剪（后面的注释掉，如上面所示），然后注释掉裁剪部分，取消注释下面的部分，重新运行，训练分类器。
简单介绍一下几个之前用的不太熟的函数
os.walk()
[i for i in os.walk(‘C:\\Projects\\python3_5\\Gephi‘)] Out[6]: [(‘C:\\\\Projects\\\\python3_5\\\\Gephi‘, [‘.ipynb_checkpoints‘], [‘17级学硕导师情况.csv‘, ‘17级学硕导师情况.xlsx‘, ‘bear.py‘, ‘bear.txt‘, ‘csv_init.py‘, ‘EuroSiS Generale Pays.gexf‘, ‘kmeans.py‘, ‘lesmiserables.gml‘, ‘network_x.py‘, ‘pd_nx_test.py‘, ‘result.csv‘, ‘result.txt‘, ‘Untitled.ipynb‘, ‘西游记.csv‘]), (‘C:\\\\Projects\\\\python3_5\\\\Gephi\\\\.ipynb_checkpoints‘, [], [‘Untitled-checkpoint.ipynb‘])]
首先，它返回一个迭代器，其次，每一层（tuple）有三个元素（list），如下：
[
([本层目录名],
[本目录下的目录],
[本目录下的文件]),
... ...
]
os.mkdir()
这个函数一次只能建立一级目录，所以想要建立多级目录需要连用,
os.mkdir(‘./file1‘)
os.mkdir(‘./file1/file2‘)
另外经常和os.path.exists()连用。
更新v1
好吧，我得承认，我愚蠢了，实际上os.makedirs()是可以同时创建多级目录的，
os.makedirs(‘./1/2‘)
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