人脸检查评定是人脸识别预处理,亿万先生人脸图像采集、检查实验

return [row[0] for row in reader]
def main(argv=None): # pylint: disable=unused-argument
files = []

上学笔记TF05捌:人脸识别,学习笔记tf0伍拾十位脸

人脸识别,基于人脸部特征音讯识别身份的生物识别技术。摄像机、摄像头采集人脸图像或录制流,自动物检疫查评定、跟踪图像中脸部,做脸部相关技能处理,人脸检查实验、人脸关键点检查测试、人脸验证等。《印度孟买理工技(science and technology)评价》(MIT
Technology
Review),20一7年海内外十大突破性技术榜单,支付宝“刷脸支付”(Paying with Your
Face)入围。

人脸识别优势,非强制性(采集方式不简单被发觉,被识外人脸图像可积极获取)、非接触性(用户不需求与设备接触)、并发性(可同时六个人脸检验、跟踪、识别)。深度学习前,人脸识别两手续:高维人工特征提取、降维。古板人脸识别技术基于可知光图像。深度学习+大数目(海量有标注人脸数据)为人脸识别领域主流技术途径。神经网络人脸识别技术,大批量样书图像磨炼识别模型,无需人工选拔特征,样本练习进程自行学习,识别准确率能够直达9九%。

人脸识别技术流程。

人脸图像采集、检查测试。人脸图像采集,录制头把人脸图像采集下来,静态图像、动态图像、分歧职位、不一样表情。用户在搜集设备拍报范围内,采集设置自动寻找并拍照。人脸检查实验属于指标检查实验(object
detection)。对要检查评定对象对象可能率总计,得到待检验对象特征,建立目标检验模型。用模子相配输入图像,输出相配区域。人脸检验是人脸识别预处理,准确标定人脸在图像的职责大小。人脸图像方式特点丰硕,直方图特征、颜色特征、模板特征、结构特征、哈尔特征(Haar-like
feature)。人脸检查评定挑出有用音讯,用特色检测脸部。人脸检查评定算法,模板相称模型、Adaboost模型,Adaboost模型速度。精度综合质量最佳,磨炼慢、检验快,可完结录像流实时检查测试效果。

人脸图像预处理。基于人脸检验结果,处理图像,服务特征提取。系统获得人脸图像遭到各样规范限制、随机困扰,需缩放、旋转、拉伸、光线补偿、灰度变换、直方图均衡化、规范化、几何改良、过滤、锐化等图像预处理。

人脸图像特征提取。人脸图像新闻数字化,人脸图像转变为一串数字(特征向量)。如,眼睛左侧、嘴唇左边、鼻子、下巴位置,特征点间欧氏距离、曲率、角度提取出特色分量,相关特征连接成长特征向量。

人脸图像相称、识别。提取人脸图像特点数据与数据仓库储存款和储蓄人脸特征模板搜索相称,依据相似程度对身份信息进行判断,设定阈值,相似度越过阈值,输出相称结果。确认,一对1(一:1)图像比较,表明“你正是你”,金融核实身份、音讯安全球。辨认,1对多(一:N)图像相称,“N人中找你”,录制流,人走进识别范围就完事辨认,安全防备领域。

人脸识别分类。

人脸检查评定。检测、定位图片人脸,再次回到高业饿啊人脸框坐标。对人脸分析、处理的首先步。“滑动窗口”,选择图像矩形区域作滑动窗口,窗口中领到特征对图像区域描述,依据特征描述判断窗口是不是人脸。不断遍历供给观望窗口。

人脸关键点检查实验。定位、重回人脸五官、概略关键点坐标地方。人脸概略、眼睛、眉毛、嘴唇、鼻子概况。Face++提供高达拾陆点关键点。人脸关键点定位技术,级联形回归(cascaded
shape regression,
CSCR-V)。人脸识别,基于DeepID网络布局。DeepID互联网布局类似卷积神经互连网布局,尾数第二层,有DeepID层,与卷积层4、最大池化层3相连,卷积神经互联网层数越高视野域越大,既思量部分特征,又牵挂全局特征。输入层
3①x3九x1、卷积层1 2八x36x20(卷积核4x四x1)、最大池化层1
1二x18x20(过滤器贰x2)、卷积层二 12×1陆x20(卷积核叁x三x20)、最大池化层贰陆x8x40(过滤器2x二)、卷积层三 肆x陆x60(卷积核3x叁x40)、最大池化层2
二x3x60(过滤器贰x2)、卷积层肆 二x二x80(卷积核二x二x60)、DeepID层
1×160、全连接层 Softmax。《Deep Learning Face Representation from
Predicting 一千0 Classes》
http://mmlab.ie.cuhk.edu.hk/pdf/YiSun\_CVPR14.pdf

人脸验证。分析两张人脸同1人恐怕大小。输入两张人脸,获得置信度分类、相应阈值,评估相似度。

人脸属性检查测试。人脸属性辩识、人脸心思分析。https://www.betaface.com/wpa/
在线人脸识别测试。给出人年龄、是或不是有胡子、激情(高兴、不荒谬、生气、愤怒)、性别、是不是带近视镜、肤色。

人脸识别应用,美图秀秀美颜应用、世纪佳缘查看地下配偶“面相”相似度,支付领域“刷脸支付”,安全防患领域“人脸鉴权”。Face++、商汤科学和技术,提供人脸识别SDK。

人脸检验。https://github.com/davidsandberg/facenet

Florian Schroff、Dmitry Kalenichenko、James Philbin论文《FaceNet: A
Unified Embedding for Face Recognition and Clustering》
https://arxiv.org/abs/1503.03832
https://github.com/davidsandberg/facenet/wiki/Validate-on-lfw

LFW(Labeled Faces in the Wild
Home)数据集。http://vis-www.cs.umass.edu/lfw/
。美利坚合营国西维吉妮亚高校阿姆斯特分校计算机视觉实验室整理。1323三张图片,574九位。409八个人唯有一张图片,16捌15个人多于一张。每张图片尺寸250×250。人脸图片在种种人物名字文件夹下。

数码预处理。校准代码
https://github.com/davidsandberg/facenet/blob/master/src/align/align\_dataset\_mtcnn.py

检查评定所用数据集校准为和预操练模型所用数据集大小同等。
安装环境变量

export PYTHONPATH=[…]/facenet/src

校准命令

for N in {1..4}; do python src/align/align_dataset_mtcnn.py
~/datasets/lfw/raw ~/datasets/lfw/lfw_mtcnnpy_160 –image_size 160
–margin 32 –random_order –gpu_memory_fraction 0.25 & done

预练习模型2017021六-09114玖.zip
https://drive.google.com/file/d/0B5MzpY9kBtDVZ2RpVDYwWmxoSUk
训练集 MS-Celeb-1M数据集
https://www.microsoft.com/en-us/research/project/ms-celeb-1m-challenge-recognizing-one-million-celebrities-real-world/
。微软人脸识别数据库,有名气的人榜选拔前100万名家,搜索引擎采集每一个有名的人十0张人脸图片。预陶冶模型准确率0.993+-0.00四。

检测。python src/validate_on_lfw.py datasets/lfw/lfw_mtcnnpy_160
models
基准比较,采纳facenet/data/pairs.txt,官方随机生成多少,匹配和不相配人名和图表编号。

十折交叉验证(10-fold cross
validation),精度测试方法。数据集分成10份,轮流将当中玖份做演习集,壹份做测试保,1一遍结果均值作算法精度估量。1般须要频仍十折交叉验证求均值。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
import argparse
import facenet
import lfw
import os
import sys
import math
from sklearn import metrics
from scipy.optimize import brentq
from scipy import interpolate

def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:

# Read the file containing the pairs used for testing
# 一. 读入从前的pairs.txt文件
# 读入后如[[‘Abel_Pacheco’,’1′,’4′]]
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
# Get the paths for the corresponding images
# 获取文件路径和是或不是协作关系对
paths, actual_issame =
lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs,
args.lfw_file_ext)
# Load the model
# 二. 加载模型
facenet.load_model(args.model)

# Get input and output tensors
# 获取输入输出张量
images_placeholder =
tf.get_default_graph().get_tensor_by_name(“input:0”)
embeddings =
tf.get_default_graph().get_tensor_by_name(“embeddings:0”)
phase_train_placeholder =
tf.get_default_graph().get_tensor_by_name(“phase_train:0”)

#image_size = images_placeholder.get_shape()[1] # For some reason
this doesn’t work for frozen graphs
image_size = args.image_size
embedding_size = embeddings.get_shape()[1]

# Run forward pass to calculate embeddings
# 三. 使用前向传播验证
print(‘Runnning forward pass on LFW images’)
batch_size = args.lfw_batch_size
nrof_images = len(paths)
nrof_batches = int(math.ceil(1.0*nrof_images / batch_size)) #
总共批次数
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches):
start_index = i*batch_size
end_index = min((i+1)*batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False, image_size)
feed_dict = { images_placeholder:images,
phase_train_placeholder:False }
emb_array[start_index:end_index,:] = sess.run(embeddings,
feed_dict=feed_dict)

# 4. 计量准确率、验证率,10折交叉验证措施
tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(emb_array,
actual_issame, nrof_folds=args.lfw_nrof_folds)
print(‘Accuracy: %1.3f+-%1.3f’ % (np.mean(accuracy),
np.std(accuracy)))
print(‘Validation rate: %2.5f+-%2.5f @ FAR=%2.5f’ % (val, val_std,
far))
# 得到auc值
auc = metrics.auc(fpr, tpr)
print(‘Area Under Curve (AUC): %1.3f’ % auc)
# 1获得错误率(eer)
eer = brentq(lambda x: 1. – x – interpolate.interp1d(fpr, tpr)(x), 0.,
1.)
print(‘Equal Error Rate (EER): %1.3f’ % eer)

def parse_arguments(argv):
parser = argparse.ArgumentParser()

parser.add_argument(‘lfw_dir’, type=str,
help=’Path to the data directory containing aligned LFW face
patches.’)
parser.add_argument(‘–lfw_batch_size’, type=int,
help=’Number of images to process in a batch in the LFW test set.’,
default=100)
parser.add_argument(‘model’, type=str,
help=’Could be either a directory containing the meta_file and
ckpt_file or a model protobuf (.pb) file’)
parser.add_argument(‘–image_size’, type=int,
help=’Image size (height, width) in pixels.’, default=160)
parser.add_argument(‘–lfw_pairs’, type=str,
help=’The file containing the pairs to use for validation.’,
default=’data/pairs.txt’)
parser.add_argument(‘–lfw_file_ext’, type=str,
help=’The file extension for the LFW dataset.’, default=’png’,
choices=[‘jpg’, ‘png’])
parser.add_argument(‘–lfw_nrof_folds’, type=int,
help=’Number of folds to use for cross validation. Mainly used for
testing.’, default=10)
return parser.parse_args(argv)
if __name__ == ‘__main__’:
main(parse_arguments(sys.argv[1:]))

性别、年龄识别。https://github.com/dpressel/rude-carnie

Adience
数据集。http://www.openu.ac.il/home/hassner/Adience/data.html\#agegender
。26580张图片,228四类,年龄限制几个区段(0~2、4~6、8~13、15~20、25~32、38~43、48~53、60~),含有噪声、姿势、光照变化。aligned
# 经过剪裁对齐多少,faces #
原始数据。fold_0_data.txt至fold_4_data.txt
全部数目符号。fold_frontal_0_data.txt至fold_frontal_4_data.txt
仅用类似正面态度面部标记。数据结构 user_id
用户Flickr帐户ID、original_image 图片文件名、face_id
人标识符、age、gender、x、y、dx、dy 人脸边框、tilt_ang
切斜角度、fiducial_yaw_angle 基准偏移角度、fiducial_score
基准分数。https://www.flickr.com/

数据预处理。脚本把多少处理成TFRecords格式。https://github.com/dpressel/rude-carnie/blob/master/preproc.py
https://github.com/GilLevi/AgeGenderDeepLearning/tree/master/Folds文件夹,已经对训练集、测试集划分、标注。gender\_train.txt、gender\_val.txt
图片列表 Adience 数据集处理TFRecords文件。图片处理为大小25陆x256JPEG编码凯雷德GB图像。tf.python_io.TFRecordWriter写入TFRecords文件,输出文件output_file。

营造立模型型。年龄、性别训练模型,Gil Levi、Tal Hassner杂文《Age and Gender
Classification Using Convolutional Neural
Networks》http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.722.9654&rank=1
。模型 https://github.com/dpressel/rude-carnie/blob/master/model.py
。tenforflow.contrib.slim。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import time
import os
import numpy as np
import tensorflow as tf
from data import distorted_inputs
import re
from tensorflow.contrib.layers import *
from tensorflow.contrib.slim.python.slim.nets.inception_v3 import
inception_v3_base
TOWER_NAME = ‘tower’
def select_model(name):
if name.startswith(‘inception’):
print(‘selected (fine-tuning) inception model’)
return inception_v3
elif name == ‘bn’:
print(‘selected batch norm model’)
return levi_hassner_bn
print(‘selected default model’)
return levi_hassner
def get_checkpoint(checkpoint_path, requested_step=None,
basename=’checkpoint’):
if requested_step is not None:
model_checkpoint_path = ‘%s/%s-%s’ % (checkpoint_path, basename,
requested_step)
if os.path.exists(model_checkpoint_path) is None:
print(‘No checkpoint file found at [%s]’ % checkpoint_path)
exit(-1)
print(model_checkpoint_path)
print(model_checkpoint_path)
return model_checkpoint_path, requested_step
ckpt = tf.train.get_checkpoint_state(checkpoint_path)
if ckpt and ckpt.model_checkpoint_path:
# Restore checkpoint as described in top of this program
print(ckpt.model_checkpoint_path)
global_step =
ckpt.model_checkpoint_path.split(‘/’)[-1].split(‘-‘)[-1]
return ckpt.model_checkpoint_path, global_step
else:
print(‘No checkpoint file found at [%s]’ % checkpoint_path)
exit(-1)
def _activation_summary(x):
tensor_name = re.sub(‘%s_[0-9]*/’ % TOWER_NAME, ”, x.op.name)
tf.summary.histogram(tensor_name + ‘/activations’, x)
tf.summary.scalar(tensor_name + ‘/sparsity’, tf.nn.zero_fraction(x))
def inception_v3(nlabels, images, pkeep, is_training):
batch_norm_params = {
“is_training”: is_training,
“trainable”: True,
# Decay for the moving averages.
“decay”: 0.9997,
# Epsilon to prevent 0s in variance.
“epsilon”: 0.001,
# Collection containing the moving mean and moving variance.
“variables_collections”: {
“beta”: None,
“gamma”: None,
“moving_mean”: [“moving_vars”],
“moving_variance”: [“moving_vars”],
}
}
weight_decay = 0.00004
stddev=0.1
weights_regularizer =
tf.contrib.layers.l2_regularizer(weight_decay)
with tf.variable_scope(“InceptionV3”, “InceptionV3”, [images]) as
scope:
with tf.contrib.slim.arg_scope(
[tf.contrib.slim.conv2d, tf.contrib.slim.fully_connected],
weights_regularizer=weights_regularizer,
trainable=True):
with tf.contrib.slim.arg_scope(
[tf.contrib.slim.conv2d],
weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
activation_fn=tf.nn.relu,
normalizer_fn=batch_norm,
normalizer_params=batch_norm_params):
net, end_points = inception_v3_base(images, scope=scope)
with tf.variable_scope(“logits”):
shape = net.get_shape()
net = avg_pool2d(net, shape[1:3], padding=”VALID”, scope=”pool”)
net = tf.nn.dropout(net, pkeep, name=’droplast’)
net = flatten(net, scope=”flatten”)

with tf.variable_scope(‘output’) as scope:

weights = tf.Variable(tf.truncated_normal([2048, nlabels], mean=0.0,
stddev=0.01), name=’weights’)
biases = tf.Variable(tf.constant(0.0, shape=[nlabels],
dtype=tf.float32), name=’biases’)
output = tf.add(tf.matmul(net, weights), biases, name=scope.name)
_activation_summary(output)
return output
def levi_hassner_bn(nlabels, images, pkeep, is_training):
batch_norm_params = {
“is_training”: is_training,
“trainable”: True,
# Decay for the moving averages.
“decay”: 0.9997,
# Epsilon to prevent 0s in variance.
“epsilon”: 0.001,
# Collection containing the moving mean and moving variance.
“variables_collections”: {
“beta”: None,
“gamma”: None,
“moving_mean”: [“moving_vars”],
“moving_variance”: [“moving_vars”],
}
}
weight_decay = 0.0005
weights_regularizer =
tf.contrib.layers.l2_regularizer(weight_decay)
with tf.variable_scope(“LeviHassnerBN”, “LeviHassnerBN”, [images]) as
scope:
with tf.contrib.slim.arg_scope(
[convolution2d, fully_connected],
weights_regularizer=weights_regularizer,
biases_initializer=tf.constant_initializer(1.),
weights_initializer=tf.random_normal_initializer(stddev=0.005),
trainable=True):
with tf.contrib.slim.arg_scope(
[convolution2d],
weights_initializer=tf.random_normal_initializer(stddev=0.01),
normalizer_fn=batch_norm,
normalizer_params=batch_norm_params):
conv1 = convolution2d(images, 96, [7,7], [4, 4], padding=’VALID’,
biases_initializer=tf.constant_initializer(0.), scope=’conv1′)
pool1 = max_pool2d(conv1, 3, 2, padding=’VALID’, scope=’pool1′)
conv2 = convolution2d(pool1, 256, [5, 5], [1, 1], padding=’SAME’,
scope=’conv2′)
pool2 = max_pool2d(conv2, 3, 2, padding=’VALID’, scope=’pool2′)
conv3 = convolution2d(pool2, 384, [3, 3], [1, 1], padding=’SAME’,
biases_initializer=tf.constant_initializer(0.), scope=’conv3′)
pool3 = max_pool2d(conv3, 3, 2, padding=’VALID’, scope=’pool3′)
# can use tf.contrib.layer.flatten
flat = tf.reshape(pool3, [-1, 384*6*6], name=’reshape’)
full1 = fully_connected(flat, 512, scope=’full1′)
drop1 = tf.nn.dropout(full1, pkeep, name=’drop1′)
full2 = fully_connected(drop1, 512, scope=’full2′)
drop2 = tf.nn.dropout(full2, pkeep, name=’drop2′)
with tf.variable_scope(‘output’) as scope:

weights = tf.Variable(tf.random_normal([512, nlabels], mean=0.0,
stddev=0.01), name=’weights’)
biases = tf.Variable(tf.constant(0.0, shape=[nlabels],
dtype=tf.float32), name=’biases’)
output = tf.add(tf.matmul(drop2, weights), biases, name=scope.name)
return output
def levi_hassner(nlabels, images, pkeep, is_training):
weight_decay = 0.0005
weights_regularizer =
tf.contrib.layers.l2_regularizer(weight_decay)
with tf.variable_scope(“LeviHassner”, “LeviHassner”, [images]) as
scope:
with tf.contrib.slim.arg_scope(
[convolution2d, fully_connected],
weights_regularizer=weights_regularizer,
biases_initializer=tf.constant_initializer(1.),
weights_initializer=tf.random_normal_initializer(stddev=0.005),
trainable=True):
with tf.contrib.slim.arg_scope(
[convolution2d],
weights_initializer=tf.random_normal_initializer(stddev=0.01)):
conv1 = convolution2d(images, 96, [7,7], [4, 4], padding=’VALID’,
biases_initializer=tf.constant_initializer(0.), scope=’conv1′)
pool1 = max_pool2d(conv1, 3, 2, padding=’VALID’, scope=’pool1′)
norm1 = tf.nn.local_response_normalization(pool1, 5, alpha=0.0001,
beta=0.75, name=’norm1′)
conv2 = convolution2d(norm1, 256, [5, 5], [1, 1], padding=’SAME’,
scope=’conv2′)
pool2 = max_pool2d(conv2, 3, 2, padding=’VALID’, scope=’pool2′)
norm2 = tf.nn.local_response_normalization(pool2, 5, alpha=0.0001,
beta=0.75, name=’norm2′)
conv3 = convolution2d(norm2, 384, [3, 3], [1, 1],
biases_initializer=tf.constant_initializer(0.), padding=’SAME’,
scope=’conv3′)
pool3 = max_pool2d(conv3, 3, 2, padding=’VALID’, scope=’pool3′)
flat = tf.reshape(pool3, [-1, 384*6*6], name=’reshape’)
full1 = fully_connected(flat, 512, scope=’full1′)
drop1 = tf.nn.dropout(full1, pkeep, name=’drop1′)
full2 = fully_connected(drop1, 512, scope=’full2′)
drop2 = tf.nn.dropout(full2, pkeep, name=’drop2′)
with tf.variable_scope(‘output’) as scope:

weights = tf.Variable(tf.random_normal([512, nlabels], mean=0.0,
stddev=0.01), name=’weights’)
biases = tf.Variable(tf.constant(0.0, shape=[nlabels],
dtype=tf.float32), name=’biases’)
output = tf.add(tf.matmul(drop2, weights), biases, name=scope.name)
return output

磨炼模型。https://github.com/dpressel/rude-carnie/blob/master/train.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange
from datetime import datetime
import time
import os
import numpy as np
import tensorflow as tf
from data import distorted_inputs
from model import select_model
import json
import re
LAMBDA = 0.01
MOM = 0.9
tf.app.flags.DEFINE_string(‘pre_checkpoint_path’, ”,
“””If specified, restore this pretrained model “””
“””before beginning any training.”””)
tf.app.flags.DEFINE_string(‘train_dir’,
‘/home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/test_fold_is_0’,
‘Training directory’)
tf.app.flags.DEFINE_boolean(‘log_device_placement’, False,
“””Whether to log device placement.”””)
tf.app.flags.DEFINE_integer(‘num_preprocess_threads’, 4,
‘Number of preprocessing threads’)
tf.app.flags.DEFINE_string(‘optim’, ‘Momentum’,
‘Optimizer’)
tf.app.flags.DEFINE_integer(‘image_size’, 227,
‘Image size’)
tf.app.flags.DEFINE_float(‘eta’, 0.01,
‘Learning rate’)
tf.app.flags.DEFINE_float(‘pdrop’, 0.,
‘Dropout probability’)
tf.app.flags.DEFINE_integer(‘max_steps’, 40000,
‘Number of iterations’)
tf.app.flags.DEFINE_integer(‘steps_per_decay’, 10000,
‘Number of steps before learning rate decay’)
tf.app.flags.DEFINE_float(‘eta_decay_rate’, 0.1,
‘Learning rate decay’)
tf.app.flags.DEFINE_integer(‘epochs’, -1,
‘Number of epochs’)
tf.app.flags.DEFINE_integer(‘batch_size’, 128,
‘Batch size’)
tf.app.flags.DEFINE_string(‘checkpoint’, ‘checkpoint’,
‘Checkpoint name’)
tf.app.flags.DEFINE_string(‘model_type’, ‘default’,
‘Type of convnet’)
tf.app.flags.DEFINE_string(‘pre_model’,
”,#’./inception_v3.ckpt’,
‘checkpoint file’)
FLAGS = tf.app.flags.FLAGS
# Every 5k steps cut learning rate in half
def exponential_staircase_decay(at_step=10000, decay_rate=0.1):
print(‘decay [%f] every [%d] steps’ % (decay_rate, at_step))
def _decay(lr, global_step):
return tf.train.exponential_decay(lr, global_step,
at_step, decay_rate, staircase=True)
return _decay
def optimizer(optim, eta, loss_fn, at_step, decay_rate):
global_step = tf.Variable(0, trainable=False)
optz = optim
if optim == ‘Adadelta’:
optz = lambda lr: tf.train.AdadeltaOptimizer(lr, 0.95, 1e-6)
lr_decay_fn = None
elif optim == ‘Momentum’:
optz = lambda lr: tf.train.MomentumOptimizer(lr, MOM)
lr_decay_fn = exponential_staircase_decay(at_step, decay_rate)
return tf.contrib.layers.optimize_loss(loss_fn, global_step, eta,
optz, clip_gradients=4., learning_rate_decay_fn=lr_decay_fn)
def loss(logits, labels):
labels = tf.cast(labels, tf.int32)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels, name=’cross_entropy_per_example’)
cross_entropy_mean = tf.reduce_mean(cross_entropy,
name=’cross_entropy’)
tf.add_to_collection(‘losses’, cross_entropy_mean)
losses = tf.get_collection(‘losses’)
regularization_losses =
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = cross_entropy_mean + LAMBDA *
sum(regularization_losses)
tf.summary.scalar(‘tl (raw)’, total_loss)
#total_loss = tf.add_n(losses + regularization_losses,
name=’total_loss’)
loss_averages = tf.train.ExponentialMovingAverage(0.9, name=’avg’)
loss_averages_op = loss_averages.apply(losses + [total_loss])
for l in losses + [total_loss]:
tf.summary.scalar(l.op.name + ‘ (raw)’, l)
tf.summary.scalar(l.op.name, loss_averages.average(l))
with tf.control_dependencies([loss_averages_op]):
total_loss = tf.identity(total_loss)
return total_loss
def main(argv=None):
with tf.Graph().as_default():
model_fn = select_model(FLAGS.model_type)
# Open the metadata file and figure out nlabels, and size of epoch
#
打开元数据文件md.json,这些文件是在预处理多少时生成。找出nlabels、epoch大小
input_file = os.path.join(FLAGS.train_dir, ‘md.json’)
print(input_file)
with open(input_file, ‘r’) as f:
md = json.load(f)
images, labels, _ = distorted_inputs(FLAGS.train_dir,
FLAGS.batch_size, FLAGS.image_size, FLAGS.num_preprocess_threads)
logits = model_fn(md[‘nlabels’], images, 1-FLAGS.pdrop, True)
total_loss = loss(logits, labels)
train_op = optimizer(FLAGS.optim, FLAGS.eta, total_loss,
FLAGS.steps_per_decay, FLAGS.eta_decay_rate)
saver = tf.train.Saver(tf.global_variables())
summary_op = tf.summary.merge_all()
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
tf.global_variables_initializer().run(session=sess)
# This is total hackland, it only works to fine-tune iv3
# 本例可以输入预练习模型英斯ption V三,可用来微调 英斯ption V三
if FLAGS.pre_model:
inception_variables = tf.get_collection(
tf.GraphKeys.VARIABLES, scope=”InceptionV3″)
restorer = tf.train.Saver(inception_variables)
restorer.restore(sess, FLAGS.pre_model)
if FLAGS.pre_checkpoint_path:
if tf.gfile.Exists(FLAGS.pre_checkpoint_path) is True:
print(‘Trying to restore checkpoint from %s’ %
FLAGS.pre_checkpoint_path)
restorer = tf.train.Saver()
tf.train.latest_checkpoint(FLAGS.pre_checkpoint_path)
print(‘%s: Pre-trained model restored from %s’ %
(datetime.now(), FLAGS.pre_checkpoint_path))
# 将ckpt文件存款和储蓄在run-(pid)目录
run_dir = ‘%s/run-%d’ % (FLAGS.train_dir, os.getpid())
checkpoint_path = ‘%s/%s’ % (run_dir, FLAGS.checkpoint)
if tf.gfile.Exists(run_dir) is False:
print(‘Creating %s’ % run_dir)
tf.gfile.MakeDirs(run_dir)
tf.train.write_graph(sess.graph_def, run_dir, ‘model.pb’,
as_text=True)
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.summary.FileWriter(run_dir, sess.graph)
steps_per_train_epoch = int(md[‘train_counts’] /
FLAGS.batch_size)
num_steps = FLAGS.max_steps if FLAGS.epochs < 1 else FLAGS.epochs
* steps_per_train_epoch
print(‘Requested number of steps [%d]’ % num_steps)

for step in xrange(num_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, total_loss])
duration = time.time() – start_time
assert not np.isnan(loss_value), ‘Model diverged with loss = NaN’
# 每拾步记录2遍摘要文件,保存1个检查点文件
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)

format_str = (‘%s: step %d, loss = %.3f (%.1f examples/sec; %.3f ‘
‘sec/batch)’)
print(format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
# Loss only actually evaluated every 100 steps?
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)

if step % 1000 == 0 or (step + 1) == num_steps:
saver.save(sess, checkpoint_path, global_step=step)
if __name__ == ‘__main__’:
tf.app.run()

证实模型。https://github.com/dpressel/rude-carnie/blob/master/guess.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import math
import time
from data import inputs
import numpy as np
import tensorflow as tf
from model import select_model, get_checkpoint
from utils import *
import os
import json
import csv
RESIZE_FINAL = 227
GENDER_LIST =[‘M’,’F’]
AGE_LIST = [‘(0, 2)’,'(4, 6)’,'(8, 12)’,'(15, 20)’,'(25, 32)’,'(38,
43)’,'(48, 53)’,'(60, 100)’]
MAX_BATCH_SZ = 128
tf.app.flags.DEFINE_string(‘model_dir’, ”,
‘Model directory (where training data lives)’)
tf.app.flags.DEFINE_string(‘class_type’, ‘age’,
‘Classification type (age|gender)’)
tf.app.flags.DEFINE_string(‘device_id’, ‘/cpu:0’,
‘What processing unit to execute inference on’)
tf.app.flags.DEFINE_string(‘filename’, ”,
‘File (Image) or File list (Text/No header TSV) to process’)
tf.app.flags.DEFINE_string(‘target’, ”,
‘CSV file containing the filename processed along with best guess and
score’)
tf.app.flags.DEFINE_string(‘checkpoint’, ‘checkpoint’,
‘Checkpoint basename’)
tf.app.flags.DEFINE_string(‘model_type’, ‘default’,
‘Type of convnet’)
tf.app.flags.DEFINE_string(‘requested_step’, ”, ‘Within the model
directory, a requested step to restore e.g., 9000’)
tf.app.flags.DEFINE_boolean(‘single_look’, False, ‘single look at the
image or multiple crops’)
tf.app.flags.DEFINE_string(‘face_detection_model’, ”, ‘Do frontal
face detection with model specified’)
tf.app.flags.DEFINE_string(‘face_detection_type’, ‘cascade’, ‘Face
detection model type (yolo_tiny|cascade)’)
FLAGS = tf.app.flags.FLAGS
def one_of(fname, types):
return any([fname.endswith(‘.’ + ty) for ty in types])
def resolve_file(fname):
if os.path.exists(fname): return fname
for suffix in (‘.jpg’, ‘.png’, ‘.JPG’, ‘.PNG’, ‘.jpeg’):
cand = fname + suffix
if os.path.exists(cand):
return cand
return None
def classify_many_single_crop(sess, label_list, softmax_output,
coder, images, image_files, writer):
try:
num_batches = math.ceil(len(image_files) / MAX_BATCH_SZ)
pg = ProgressBar(num_batches)
for j in range(num_batches):
start_offset = j * MAX_BATCH_SZ
end_offset = min((j + 1) * MAX_BATCH_SZ, len(image_files))

batch_image_files = image_files[start_offset:end_offset]
print(start_offset, end_offset, len(batch_image_files))
image_batch = make_multi_image_batch(batch_image_files, coder)
batch_results = sess.run(softmax_output,
feed_dict={images:image_batch.eval()})
batch_sz = batch_results.shape[0]
for i in range(batch_sz):
output_i = batch_results[i]
best_i = np.argmax(output_i)
best_choice = (label_list[best_i], output_i[best_i])
print(‘Guess @ 1 %s, prob = %.2f’ % best_choice)
if writer is not None:
f = batch_image_files[i]
writer.writerow((f, best_choice[0], ‘%.2f’ % best_choice[1]))
pg.update()
pg.done()
except Exception as e:
print(e)
print(‘Failed to run all images’)
def classify_one_multi_crop(sess, label_list, softmax_output,
coder, images, image_file, writer):
try:
print(‘Running file %s’ % image_file)
image_batch = make_multi_crop_batch(image_file, coder)
batch_results = sess.run(softmax_output,
feed_dict={images:image_batch.eval()})
output = batch_results[0]
batch_sz = batch_results.shape[0]

for i in range(1, batch_sz):
output = output + batch_results[i]

output /= batch_sz
best = np.argmax(output) # 最或者质量分类
best_choice = (label_list[best], output[best])
print(‘Guess @ 1 %s, prob = %.2f’ % best_choice)

nlabels = len(label_list)
if nlabels > 2:
output[best] = 0
second_best = np.argmax(output)
print(‘Guess @ 2 %s, prob = %.2f’ % (label_list[second_best],
output[second_best]))
if writer is not None:
writer.writerow((image_file, best_choice[0], ‘%.2f’ %
best_choice[1]))
except Exception as e:
print(e)
print(‘Failed to run image %s ‘ % image_file)
def list_images(srcfile):
with open(srcfile, ‘r’) as csvfile:
delim = ‘,’ if srcfile.endswith(‘.csv’) else ‘\t’
reader = csv.reader(csvfile, delimiter=delim)
if srcfile.endswith(‘.csv’) or srcfile.endswith(‘.tsv’):
print(‘skipping header’)
_ = next(reader)

return [row[0] for row in reader]
def main(argv=None): # pylint: disable=unused-argument
files = []

if FLAGS.face_detection_model:
print(‘Using face detector (%s) %s’ % (FLAGS.face_detection_type,
FLAGS.face_detection_model))
face_detect = face_detection_model(FLAGS.face_detection_type,
FLAGS.face_detection_model)
face_files, rectangles = face_detect.run(FLAGS.filename)
print(face_files)
files += face_files
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
label_list = AGE_LIST if FLAGS.class_type == ‘age’ else
GENDER_LIST
nlabels = len(label_list)
print(‘Executing on %s’ % FLAGS.device_id)
model_fn = select_model(FLAGS.model_type)
with tf.device(FLAGS.device_id):

images = tf.placeholder(tf.float32, [None, RESIZE_FINAL,
RESIZE_FINAL, 3])
logits = model_fn(nlabels, images, 1, False)
init = tf.global_variables_initializer()

requested_step = FLAGS.requested_step if FLAGS.requested_step else
None

checkpoint_path = ‘%s’ % (FLAGS.model_dir)
model_checkpoint_path, global_step =
get_checkpoint(checkpoint_path, requested_step, FLAGS.checkpoint)

saver = tf.train.Saver()
saver.restore(sess, model_checkpoint_path)

softmax_output = tf.nn.softmax(logits)
coder = ImageCoder()
# Support a batch mode if no face detection model
if len(files) == 0:
if (os.path.isdir(FLAGS.filename)):
for relpath in os.listdir(FLAGS.filename):
abspath = os.path.join(FLAGS.filename, relpath)

if os.path.isfile(abspath) and any([abspath.endswith(‘.’ + ty) for ty
in (‘jpg’, ‘png’, ‘JPG’, ‘PNG’, ‘jpeg’)]):
print(abspath)
files.append(abspath)
else:
files.append(FLAGS.filename)
# If it happens to be a list file, read the list and clobber the
files
if any([FLAGS.filename.endswith(‘.’ + ty) for ty in (‘csv’, ‘tsv’,
‘txt’)]):
files = list_images(FLAGS.filename)

writer = None
output = None
if FLAGS.target:
print(‘Creating output file %s’ % FLAGS.target)
output = open(FLAGS.target, ‘w’)
writer = csv.writer(output)
writer.writerow((‘file’, ‘label’, ‘score’))
image_files = list(filter(lambda x: x is not None, [resolve_file(f)
for f in files]))
print(image_files)
if FLAGS.single_look:
classify_many_single_crop(sess, label_list, softmax_output, coder,
images, image_files, writer)
else:
for image_file in image_files:
classify_one_multi_crop(sess, label_list, softmax_output, coder,
images, image_file, writer)
if output is not None:
output.close()

if __name__ == ‘__main__’:
tf.app.run()

微软脸部图片识别性别、年龄网址 http://how-old.net/
。图片识别年龄、性别。根据标题查找图片。

参考资料:
《TensorFlow技术解析与实战》

迎接推荐北京机械学习工作机会,作者的微信:qingxingfengzi

http://www.bkjia.com/Pythonjc/1231904.htmlwww.bkjia.comtruehttp://www.bkjia.com/Pythonjc/1231904.htmlTechArticle学习笔记TF058:人脸识别,学习笔记tf058人脸
人脸识别,基于人脸部特征新闻识别身份的生物识别技术。摄像机、录制头采集人脸图像或录制…

# 四. 计量准确率、验证率,拾折交叉验证措施
tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(emb_array,
actual_issame, nrof_folds=args.lfw_nrof_folds)
print(‘Accuracy: %1.3f+-%1.3f’ % (np.mean(accuracy),
np.std(accuracy)))
print(‘Validation rate: %2.5f+-%2.5f @ FAR=%2.5f’ % (val, val_std,
far))
# 得到auc值
auc = metrics.auc(fpr, tpr)
print(‘Area Under Curve (AUC): %1.3f’ % auc)
# 一获得错误率(eer)
eer = brentq(lambda x: 1. – x – interpolate.interp1d(fpr, tpr)(x), 0.,
1.)
print(‘Equal Error Rate (EER): %1.3f’ % eer)

数量预处理。校准代码
https://github.com/davidsandberg/facenet/blob/master/src/align/align\_dataset\_mtcnn.py

检查实验所用数据集校准为和预练习模型所用数据集大小同等。
安装环境变量

人脸图像相称、识别。提取人脸图像特点数据与数据库存款和储蓄人脸特征模板搜索相称,依据相似程度对地位音讯举办判定,设定阈值,相似度越过阈值,输出相配结果。确认,一对一(壹:1)图像比较,注解“你正是您”,金融核实身份、音信安全领域。辨认,一对多(1:N)图像相称,“N人中找你”,录像流,人走进识别范围就形成辨认,安全防范领域。

参考资料:
《TensorFlow技术解析与实战》

人脸关键点检查实验。定位、再次回到人脸五官、概略关键点坐标地点。人脸轮廓、眼睛、眉毛、嘴唇、鼻子概况。Face++提供高达10陆点关键点。人脸关键点定位技术,级联形回归(cascaded
shape regression,
CSKoleos)。人脸识别,基于DeepID互联网布局。DeepID互联网布局类似卷积神经互联网布局,尾数第三层,有DeepID层,与卷积层肆、最大池化层三相连,卷积神经互连网层数越高视野域越大,既思考部分特征,又记挂全局特征。输入层
3一x3玖x一、卷积层一 2八x3陆x20(卷积核四x四x壹)、最大池化层1
1二x18x20(过滤器二x2)、卷积层贰 1二x1六x20(卷积核叁x3x20)、最大池化层2
6x八x40(过滤器二x2)、卷积层三 四x陆x60(卷积核3x叁x40)、最大池化层2
2x三x60(过滤器二x二)、卷积层肆 贰x二x80(卷积核二x贰x60)、DeepID层
壹x160、全连接层 Softmax。《Deep Learning Face Representation from
Predicting 一千0 Classes》
http://mmlab.ie.cuhk.edu.hk/pdf/YiSun\_CVPR14.pdf

人脸图像采集、检查测试。人脸图像采集,摄像头把人脸图像采集下来,静态图像、动态图像、不一致地方、分歧表情。用户在征集设备拍报范围内,采集设置自动物检疫索并录制。人脸检查测试属于目的检查评定(object
detection)。对要检查评定对象对象可能率总结,得到待检查评定对象特征,建立指标检查实验模型。用模子相配输入图像,输出相配区域。人脸检验是人脸识别预处理,准确标定人脸在图像的职位大小。人脸图像格局特点丰盛,直方图特征、颜色特征、模板特征、结构特征、哈尔特征(Haar-like
feature)。人脸检查测试挑出有用音讯,用特色检验脸部。人脸检查测试算法,模板相配模型、Adaboost模型,Adaboost模型速度。精度综合品质最棒,陶冶慢、检验快,可高达摄像流实时检查评定效果。

人脸识别,基于人脸部特征音信识别身份的浮游生物识别技术。录制机、摄像头采集人脸图像或录制流,自动物检疫查测试、跟踪图像中脸部,做脸部相关技能处理,人脸检查测试、人脸关键点检验、人脸验证等。《俄亥俄州立科学技术评价》(MIT
Technology
Review),20一柒年整个世界10大突破性技术榜单,支付宝“刷脸支付”(Paying with Your
Face)入围。

writer = None
output = None
if FLAGS.target:
print(‘Creating output file %s’ % FLAGS.target)
output = open(FLAGS.target, ‘w’)
writer = csv.writer(output)
writer.writerow((‘file’, ‘label’, ‘score’))
image_files = list(filter(lambda x: x is not None, [resolve_file(f)
for f in files]))
print(image_files)
if FLAGS.single_look:
classify_many_single_crop(sess, label_list, softmax_output, coder,
images, image_files, writer)
else:
for image_file in image_files:
classify_one_multi_crop(sess, label_list, softmax_output, coder,
images, image_file, writer)
if output is not None:
output.close()

# Read the file containing the pairs used for testing
# 1. 读入在此之前的pairs.txt文件
# 读入后如[[‘Abel_Pacheco’,’1′,’4′]]
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
# Get the paths for the corresponding images
# 获取文件路径和是或不是协作关系对
paths, actual_issame =
lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs,
args.lfw_file_ext)
# Load the model
# 二. 加载模型
facenet.load_model(args.model)

output /= batch_sz
best = np.argmax(output) # 最或者性能分类
best_choice = (label_list[best], output[best])
print(‘Guess @ 1 %s, prob = %.2f’ % best_choice)

#image_size = images_placeholder.get_shape()[1] # For some reason
this doesn’t work for frozen graphs
image_size = args.image_size
embedding_size = embeddings.get_shape()[1]

weights = tf.Variable(tf.random_normal([512, nlabels], mean=0.0,
stddev=0.01), name=’weights’)
biases = tf.Variable(tf.constant(0.0, shape=[nlabels],
dtype=tf.float32), name=’biases’)
output = tf.add(tf.matmul(drop2, weights), biases, name=scope.name)
return output

requested_step = FLAGS.requested_step if FLAGS.requested_step else
None

nlabels = len(label_list)
if nlabels > 2:
output[best] = 0
second_best = np.argmax(output)
print(‘Guess @ 2 %s, prob = %.2f’ % (label_list[second_best],
output[second_best]))
if writer is not None:
writer.writerow((image_file, best_choice[0], ‘%.2f’ %
best_choice[1]))
except Exception as e:
print(e)
print(‘Failed to run image %s ‘ % image_file)
def list_images(srcfile):
with open(srcfile, ‘r’) as csvfile:
delim = ‘,’ if srcfile.endswith(‘.csv’) else ‘\t’
reader = csv.reader(csvfile, delimiter=delim)
if srcfile.endswith(‘.csv’) or srcfile.endswith(‘.tsv’):
print(‘skipping header’)
_ = next(reader)

练习模型。https://github.com/dpressel/rude-carnie/blob/master/train.py

for step in xrange(num_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, total_loss])
duration = time.time() – start_time
assert not np.isnan(loss_value), ‘Model diverged with loss = NaN’
# 每十步记录贰遍摘要文件,保存一个检查点文件
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import time
import os
import numpy as np
import tensorflow as tf
from data import distorted_inputs
import re
from tensorflow.contrib.layers import *
from tensorflow.contrib.slim.python.slim.nets.inception_v3 import
inception_v3_base
TOWER_NAME = ‘tower’
def select_model(name):
if name.startswith(‘inception’):
print(‘selected (fine-tuning) inception model’)
return inception_v3
elif name == ‘bn’:
print(‘selected batch norm model’)
return levi_hassner_bn
print(‘selected default model’)
return levi_hassner
def get_checkpoint(checkpoint_path, requested_step=None,
basename=’checkpoint’):
if requested_step is not None:
model_checkpoint_path = ‘%s/%s-%s’ % (checkpoint_path, basename,
requested_step)
if os.path.exists(model_checkpoint_path) is None:
print(‘No checkpoint file found at [%s]’ % checkpoint_path)
exit(-1)
print(model_checkpoint_path)
print(model_checkpoint_path)
return model_checkpoint_path, requested_step
ckpt = tf.train.get_checkpoint_state(checkpoint_path)
if ckpt and ckpt.model_checkpoint_path:
# Restore checkpoint as described in top of this program
print(ckpt.model_checkpoint_path)
global_step =
ckpt.model_checkpoint_path.split(‘/’)[-1].split(‘-‘)[-1]
return ckpt.model_checkpoint_path, global_step
else:
print(‘No checkpoint file found at [%s]’ % checkpoint_path)
exit(-1)
def _activation_summary(x):
tensor_name = re.sub(‘%s_[0-9]*/’ % TOWER_NAME, ”, x.op.name)
tf.summary.histogram(tensor_name + ‘/activations’, x)
tf.summary.scalar(tensor_name + ‘/sparsity’, tf.nn.zero_fraction(x))
def inception_v3(nlabels, images, pkeep, is_training):
batch_norm_params = {
“is_training”: is_training,
“trainable”: True,
# Decay for the moving averages.
“decay”: 0.9997,
# Epsilon to prevent 0s in variance.
“epsilon”: 0.001,
# Collection containing the moving mean and moving variance.
“variables_collections”: {
“beta”: None,
“gamma”: None,
“moving_mean”: [“moving_vars”],
“moving_variance”: [“moving_vars”],
}
}
weight_decay = 0.00004
stddev=0.1
weights_regularizer =
tf.contrib.layers.l2_regularizer(weight_decay)
with tf.variable_scope(“InceptionV3”, “InceptionV3”, [images]) as
scope:
with tf.contrib.slim.arg_scope(
[tf.contrib.slim.conv2d, tf.contrib.slim.fully_connected],
weights_regularizer=weights_regularizer,
trainable=True):
with tf.contrib.slim.arg_scope(
[tf.contrib.slim.conv2d],
weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
activation_fn=tf.nn.relu,
normalizer_fn=batch_norm,
normalizer_params=batch_norm_params):
net, end_points = inception_v3_base(images, scope=scope)
with tf.variable_scope(“logits”):
shape = net.get_shape()
net = avg_pool2d(net, shape[1:3], padding=”VALID”, scope=”pool”)
net = tf.nn.dropout(net, pkeep, name=’droplast’)
net = flatten(net, scope=”flatten”)

欢迎推荐北京机械学习工作机会,笔者的微信:qingxingfengzi

format_str = (‘%s: step %d, loss = %.3f (%.1f examples/sec; %.3f ‘
‘sec/batch)’)
print(format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
# Loss only actually evaluated every 100 steps?
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)

多少预处理。脚本把数据处理成TFRecords格式。https://github.com/dpressel/rude-carnie/blob/master/preproc.py
https://github.com/GilLevi/AgeGenderDeepLearning/tree/master/Folds文件夹,已经对训练集、测试集划分、标注。gender\_train.txt、gender\_val.txt
图片列表 Adience 数据集处理TFRecords文件。图片处理为大小256×25六JPEG编码ENCOREGB图像。tf.python_io.TFRecordWriter写入TFRecords文件,输出文件output_file。

with tf.variable_scope(‘output’) as scope:

softmax_output = tf.nn.softmax(logits)
coder = ImageCoder()
# Support a batch mode if no face detection model
if len(files) == 0:
if (os.path.isdir(FLAGS.filename)):
for relpath in os.listdir(FLAGS.filename):
abspath = os.path.join(FLAGS.filename, relpath)

校准命令

人脸识别技术流程。

人脸图像特征提取。人脸图像音信数字化,人脸图像转变为1串数字(特征向量)。如,眼睛左边、嘴唇左边、鼻子、下巴地方,特征点间欧氏距离、曲率、角度提取出特色分量,相关特征连接成长特征向量。

性别、年龄识别。https://github.com/dpressel/rude-carnie

检测。python src/validate_on_lfw.py datasets/lfw/lfw_mtcnnpy_160
models
基准比较,接纳facenet/data/pairs.txt,官方随机生成多少,匹配和不相称人名和图纸编号。

images = tf.placeholder(tf.float32, [None, RESIZE_FINAL,
RESIZE_FINAL, 3])
logits = model_fn(nlabels, images, 1, False)
init = tf.global_variables_initializer()

人脸属性检查测试。人脸属性辩识、人脸心境分析。https://www.betaface.com/wpa/
在线人脸识别测试。给出人年龄、是还是不是有胡子、心情(心满意足、平常、生气、愤怒)、性别、是不是带老花镜、肤色。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange
from datetime import datetime
import time
import os
import numpy as np
import tensorflow as tf
from data import distorted_inputs
from model import select_model
import json
import re
LAMBDA = 0.01
MOM = 0.9
tf.app.flags.DEFINE_string(‘pre_checkpoint_path’, ”,
“””If specified, restore this pretrained model “””
“””before beginning any training.”””)
tf.app.flags.DEFINE_string(‘train_dir’,
‘/home/dpressel/dev/work/AgeGenderDeepLearning/Folds/tf/test_fold_is_0’,
‘Training directory’)
tf.app.flags.DEFINE_boolean(‘log_device_placement’, False,
“””Whether to log device placement.”””)
tf.app.flags.DEFINE_integer(‘num_preprocess_threads’, 4,
‘Number of preprocessing threads’)
tf.app.flags.DEFINE_string(‘optim’, ‘Momentum’,
‘Optimizer’)
tf.app.flags.DEFINE_integer(‘image_size’, 227,
‘Image size’)
tf.app.flags.DEFINE_float(‘eta’, 0.01,
‘Learning rate’)
tf.app.flags.DEFINE_float(‘pdrop’, 0.,
‘Dropout probability’)
tf.app.flags.DEFINE_integer(‘max_steps’, 40000,
‘Number of iterations’)
tf.app.flags.DEFINE_integer(‘steps_per_decay’, 10000,
‘Number of steps before learning rate decay’)
tf.app.flags.DEFINE_float(‘eta_decay_rate’, 0.1,
‘Learning rate decay’)
tf.app.flags.DEFINE_integer(‘epochs’, -1,
‘Number of epochs’)
tf.app.flags.DEFINE_integer(‘batch_size’, 128,
‘Batch size’)
tf.app.flags.DEFINE_string(‘checkpoint’, ‘checkpoint’,
‘Checkpoint name’)
tf.app.flags.DEFINE_string(‘model_type’, ‘default’,
‘Type of convnet’)
tf.app.flags.DEFINE_string(‘pre_model’,
”,#’./inception_v3.ckpt’,
‘checkpoint file’)
FLAGS = tf.app.flags.FLAGS
# Every 5k steps cut learning rate in half
def exponential_staircase_decay(at_step=10000, decay_rate=0.1):
print(‘decay [%f] every [%d] steps’ % (decay_rate, at_step))
def _decay(lr, global_step):
return tf.train.exponential_decay(lr, global_step,
at_step, decay_rate, staircase=True)
return _decay
def optimizer(optim, eta, loss_fn, at_step, decay_rate):
global_step = tf.Variable(0, trainable=False)
optz = optim
if optim == ‘Adadelta’:
optz = lambda lr: tf.train.AdadeltaOptimizer(lr, 0.95, 1e-6)
lr_decay_fn = None
elif optim == ‘Momentum’:
optz = lambda lr: tf.train.MomentumOptimizer(lr, MOM)
lr_decay_fn = exponential_staircase_decay(at_step, decay_rate)
return tf.contrib.layers.optimize_loss(loss_fn, global_step, eta,
optz, clip_gradients=4., learning_rate_decay_fn=lr_decay_fn)
def loss(logits, labels):
labels = tf.cast(labels, tf.int32)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels, name=’cross_entropy_per_example’)
cross_entropy_mean = tf.reduce_mean(cross_entropy,
name=’cross_entropy’)
tf.add_to_collection(‘losses’, cross_entropy_mean)
losses = tf.get_collection(‘losses’)
regularization_losses =
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = cross_entropy_mean + LAMBDA *
sum(regularization_losses)
tf.summary.scalar(‘tl (raw)’, total_loss)
#total_loss = tf.add_n(losses + regularization_losses,
name=’total_loss’)
loss_averages = tf.train.ExponentialMovingAverage(0.9, name=’avg’)
loss_averages_op = loss_averages.apply(losses + [total_loss])
for l in losses + [total_loss]:
tf.summary.scalar(l.op.name + ‘ (raw)’, l)
tf.summary.scalar(l.op.name, loss_averages.average(l))
with tf.control_dependencies([loss_averages_op]):
total_loss = tf.identity(total_loss)
return total_loss
def main(argv=None):
with tf.Graph().as_default():
model_fn = select_model(FLAGS.model_type)
# Open the metadata file and figure out nlabels, and size of epoch
#
打开元数据文件md.json,那些文件是在预处理多少时生成。找出nlabels、epoch大小
input_file = os.path.join(FLAGS.train_dir, ‘md.json’)
print(input_file)
with open(input_file, ‘r’) as f:
md = json.load(f)
images, labels, _ = distorted_inputs(FLAGS.train_dir,
FLAGS.batch_size, FLAGS.image_size, FLAGS.num_preprocess_threads)
logits = model_fn(md[‘nlabels’], images, 1-FLAGS.pdrop, True)
total_loss = loss(logits, labels)
train_op = optimizer(FLAGS.optim, FLAGS.eta, total_loss,
FLAGS.steps_per_decay, FLAGS.eta_decay_rate)
saver = tf.train.Saver(tf.global_variables())
summary_op = tf.summary.merge_all()
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
tf.global_variables_initializer().run(session=sess)
# This is total hackland, it only works to fine-tune iv3
# 本例能够输入预磨炼模型英斯ption V三,可用来微调 英斯ption V叁
if FLAGS.pre_model:
inception_variables = tf.get_collection(
tf.GraphKeys.VARIABLES, scope=”InceptionV3″)
restorer = tf.train.Saver(inception_variables)
restorer.restore(sess, FLAGS.pre_model)
if FLAGS.pre_checkpoint_path:
if tf.gfile.Exists(FLAGS.pre_checkpoint_path) is True:
print(‘Trying to restore checkpoint from %s’ %
FLAGS.pre_checkpoint_path)
restorer = tf.train.Saver()
tf.train.latest_checkpoint(FLAGS.pre_checkpoint_path)
print(‘%s: Pre-trained model restored from %s’ %
(datetime.now(), FLAGS.pre_checkpoint_path))
# 将ckpt文件存款和储蓄在run-(pid)目录
run_dir = ‘%s/run-%d’ % (FLAGS.train_dir, os.getpid())
checkpoint_path = ‘%s/%s’ % (run_dir, FLAGS.checkpoint)
if tf.gfile.Exists(run_dir) is False:
print(‘Creating %s’ % run_dir)
tf.gfile.MakeDirs(run_dir)
tf.train.write_graph(sess.graph_def, run_dir, ‘model.pb’,
as_text=True)
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.summary.FileWriter(run_dir, sess.graph)
steps_per_train_epoch = int(md[‘train_counts’] /
FLAGS.batch_size)
num_steps = FLAGS.max_steps if FLAGS.epochs < 1 else FLAGS.epochs
* steps_per_train_epoch
print(‘Requested number of steps [%d]’ % num_steps)

batch_image_files = image_files[start_offset:end_offset]
print(start_offset, end_offset, len(batch_image_files))
image_batch = make_multi_image_batch(batch_image_files, coder)
batch_results = sess.run(softmax_output,
feed_dict={images:image_batch.eval()})
batch_sz = batch_results.shape[0]
for i in range(batch_sz):
output_i = batch_results[i]
best_i = np.argmax(output_i)
best_choice = (label_list[best_i], output_i[best_i])
print(‘Guess @ 1 %s, prob = %.2f’ % best_choice)
if writer is not None:
f = batch_image_files[i]
writer.writerow((f, best_choice[0], ‘%.2f’ % best_choice[1]))
pg.update()
pg.done()
except Exception as e:
print(e)
print(‘Failed to run all images’)
def classify_one_multi_crop(sess, label_list, softmax_output,
coder, images, image_file, writer):
try:
print(‘Running file %s’ % image_file)
image_batch = make_multi_crop_batch(image_file, coder)
batch_results = sess.run(softmax_output,
feed_dict={images:image_batch.eval()})
output = batch_results[0]
batch_sz = batch_results.shape[0]

weights = tf.Variable(tf.truncated_normal([2048, nlabels], mean=0.0,
stddev=0.01), name=’weights’)
biases = tf.Variable(tf.constant(0.0, shape=[nlabels],
dtype=tf.float32), name=’biases’)
output = tf.add(tf.matmul(net, weights), biases, name=scope.name)
_activation_summary(output)
return output
def levi_hassner_bn(nlabels, images, pkeep, is_training):
batch_norm_params = {
“is_training”: is_training,
“trainable”: True,
# Decay for the moving averages.
“decay”: 0.9997,
# Epsilon to prevent 0s in variance.
“epsilon”: 0.001,
# Collection containing the moving mean and moving variance.
“variables_collections”: {
“beta”: None,
“gamma”: None,
“moving_mean”: [“moving_vars”],
“moving_variance”: [“moving_vars”],
}
}
weight_decay = 0.0005
weights_regularizer =
tf.contrib.layers.l2_regularizer(weight_decay)
with tf.variable_scope(“LeviHassnerBN”, “LeviHassnerBN”, [images]) as
scope:
with tf.contrib.slim.arg_scope(
[convolution2d, fully_connected],
weights_regularizer=weights_regularizer,
biases_initializer=tf.constant_initializer(1.),
weights_initializer=tf.random_normal_initializer(stddev=0.005),
trainable=True):
with tf.contrib.slim.arg_scope(
[convolution2d],
weights_initializer=tf.random_normal_initializer(stddev=0.01),
normalizer_fn=batch_norm,
normalizer_params=batch_norm_params):
conv1 = convolution2d(images, 96, [7,7], [4, 4], padding=’VALID’,
biases_initializer=tf.constant_initializer(0.), scope=’conv1′)
pool1 = max_pool2d(conv1, 3, 2, padding=’VALID’, scope=’pool1′)
conv2 = convolution2d(pool1, 256, [5, 5], [1, 1], padding=’SAME’,
scope=’conv2′)
pool2 = max_pool2d(conv2, 3, 2, padding=’VALID’, scope=’pool2′)
conv3 = convolution2d(pool2, 384, [3, 3], [1, 1], padding=’SAME’,
biases_initializer=tf.constant_initializer(0.), scope=’conv3′)
pool3 = max_pool2d(conv3, 3, 2, padding=’VALID’, scope=’pool3′)
# can use tf.contrib.layer.flatten
flat = tf.reshape(pool3, [-1, 384*6*6], name=’reshape’)
full1 = fully_connected(flat, 512, scope=’full1′)
drop1 = tf.nn.dropout(full1, pkeep, name=’drop1′)
full2 = fully_connected(drop1, 512, scope=’full2′)
drop2 = tf.nn.dropout(full2, pkeep, name=’drop2′)
with tf.variable_scope(‘output’) as scope:

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import math
import time
from data import inputs
import numpy as np
import tensorflow as tf
from model import select_model, get_checkpoint
from utils import *
import os
import json
import csv
RESIZE_FINAL = 227
GENDER_LIST =[‘M’,’F’]
AGE_LIST = [‘(0, 2)’,'(4, 6)’,'(8, 12)’,'(15, 20)’,'(25, 32)’,'(38,
43)’,'(48, 53)’,'(60, 100)’]
MAX_BATCH_SZ = 128
tf.app.flags.DEFINE_string(‘model_dir’, ”,
‘Model directory (where training data lives)’)
tf.app.flags.DEFINE_string(‘class_type’, ‘age’,
‘Classification type (age|gender)’)
tf.app.flags.DEFINE_string(‘device_id’, ‘/cpu:0’,
‘What processing unit to execute inference on’)
tf.app.flags.DEFINE_string(‘filename’, ”,
‘File (Image) or File list (Text/No header TSV) to process’)
tf.app.flags.DEFINE_string(‘target’, ”,
‘CSV file containing the filename processed along with best guess and
score’)
tf.app.flags.DEFINE_string(‘checkpoint’, ‘checkpoint’,
‘Checkpoint basename’)
tf.app.flags.DEFINE_string(‘model_type’, ‘default’,
‘Type of convnet’)
tf.app.flags.DEFINE_string(‘requested_step’, ”, ‘Within the model
directory, a requested step to restore e.g., 9000’)
tf.app.flags.DEFINE_boolean(‘single_look’, False, ‘single look at the
image or multiple crops’)
tf.app.flags.DEFINE_string(‘face_detection_model’, ”, ‘Do frontal
face detection with model specified’)
tf.app.flags.DEFINE_string(‘face_detection_type’, ‘cascade’, ‘Face
detection model type (yolo_tiny|cascade)’)
FLAGS = tf.app.flags.FLAGS
def one_of(fname, types):
return any([fname.endswith(‘.’ + ty) for ty in types])
def resolve_file(fname):
if os.path.exists(fname): return fname
for suffix in (‘.jpg’, ‘.png’, ‘.JPG’, ‘.PNG’, ‘.jpeg’):
cand = fname + suffix
if os.path.exists(cand):
return cand
return None
def classify_many_single_crop(sess, label_list, softmax_output,
coder, images, image_files, writer):
try:
num_batches = math.ceil(len(image_files) / MAX_BATCH_SZ)
pg = ProgressBar(num_batches)
for j in range(num_batches):
start_offset = j * MAX_BATCH_SZ
end_offset = min((j + 1) * MAX_BATCH_SZ, len(image_files))

证实模型。https://github.com/dpressel/rude-carnie/blob/master/guess.py

预演习模型20170216-091149.zip
https://drive.google.com/file/d/0B5MzpY9kBtDVZ2RpVDYwWmxoSUk
训练集 MS-Celeb-1M数据集
https://www.microsoft.com/en-us/research/project/ms-celeb-1m-challenge-recognizing-one-million-celebrities-real-world/
。微软人脸识别数据库,有名气的人榜采取前100万球星,搜索引擎采集种种有名的人十0张人脸图片。预陶冶模型准确率0.9九三+-0.004。

Florian Schroff、Dmitry Kalenichenko、James Philbin论文《FaceNet: A
Unified Embedding for Face Recognition and Clustering》
https://arxiv.org/abs/1503.03832
https://github.com/davidsandberg/facenet/wiki/Validate-on-lfw

for i in range(1, batch_sz):
output = output + batch_results[i]

parser.add_argument(‘lfw_dir’, type=str,
help=’Path to the data directory containing aligned LFW face
patches.’)
parser.add_argument(‘–lfw_batch_size’, type=int,
help=’Number of images to process in a batch in the LFW test set.’,
default=100)
parser.add_argument(‘model’, type=str,
help=’Could be either a directory containing the meta_file and
ckpt_file or a model protobuf (.pb) file’)
parser.add_argument(‘–image_size’, type=int,
help=’Image size (height, width) in pixels.’, default=160)
parser.add_argument(‘–lfw_pairs’, type=str,
help=’The file containing the pairs to use for validation.’,
default=’data/pairs.txt’)
parser.add_argument(‘–lfw_file_ext’, type=str,
help=’The file extension for the LFW dataset.’, default=’png’,
choices=[‘jpg’, ‘png’])
parser.add_argument(‘–lfw_nrof_folds’, type=int,
help=’Number of folds to use for cross validation. Mainly used for
testing.’, default=10)
return parser.parse_args(argv)
if __name__ == ‘__main__’:
main(parse_arguments(sys.argv[1:]))

for N in {1..4}; do python src/align/align_dataset_mtcnn.py
~/datasets/lfw/raw ~/datasets/lfw/lfw_mtcnnpy_160 –image_size 160
–margin 32 –random_order –gpu_memory_fraction 0.25 & done

10折交叉验证(10-fold cross
validation),精度测试方法。数据集分成十份,轮流将内部玖份做陶冶集,一份做测试保,1二回结果均值作算法精度测度。1般需求反复十折交叉验证求均值。

人脸识别分类。

塑造立模型型。年龄、性别磨练模型,Gil Levi、Tal Hassner杂文《Age and Gender
Classification Using Convolutional Neural
Networks》http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.722.9654&rank=1
。模型 https://github.com/dpressel/rude-carnie/blob/master/model.py
。tenforflow.contrib.slim。

if FLAGS.face_detection_model:
print(‘Using face detector (%s) %s’ % (FLAGS.face_detection_type,
FLAGS.face_detection_model))
face_detect = face_detection_model(FLAGS.face_detection_type,
FLAGS.face_detection_model)
face_files, rectangles = face_detect.run(FLAGS.filename)
print(face_files)
files += face_files
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
label_list = AGE_LIST if FLAGS.class_type == ‘age’ else
GENDER_LIST
nlabels = len(label_list)
print(‘Executing on %s’ % FLAGS.device_id)
model_fn = select_model(FLAGS.model_type)
with tf.device(FLAGS.device_id):

人脸验证。分析两张人脸同壹人恐怕大小。输入两张人脸,获得置信度分类、相应阈值,评估相似度。

人脸图像预处理。基于人脸检验结果,处理图像,服务特征提取。系统获得人脸图像遭到各类口径限制、随机烦扰,需缩放、旋转、拉伸、光线补偿、灰度变换、直方图均衡化、规范化、几何勘误、过滤、锐化等图像预处理。

# Get input and output tensors
# 获取输入输出张量
images_placeholder =
tf.get_default_graph().get_tensor_by_name(“input:0”)
embeddings =
tf.get_default_graph().get_tensor_by_name(“embeddings:0”)
phase_train_placeholder =
tf.get_default_graph().get_tensor_by_name(“phase_train:0”)

export PYTHONPATH=[…]/facenet/src

checkpoint_path = ‘%s’ % (FLAGS.model_dir)
model_checkpoint_path, global_step =
get_checkpoint(checkpoint_path, requested_step, FLAGS.checkpoint)

微软脸部图片识别性别、年龄网址 http://how-old.net/
。图片识别年龄、性别。依据难题查找图片。

if os.path.isfile(abspath) and any([abspath.endswith(‘.’ + ty) for ty
in (‘jpg’, ‘png’, ‘JPG’, ‘PNG’, ‘jpeg’)]):
print(abspath)
files.append(abspath)
else:
files.append(FLAGS.filename)
# If it happens to be a list file, read the list and clobber the
files
if any([FLAGS.filename.endswith(‘.’ + ty) for ty in (‘csv’, ‘tsv’,
‘txt’)]):
files = list_images(FLAGS.filename)

Adience
数据集。http://www.openu.ac.il/home/hassner/Adience/data.html\#agegender
。26580张图纸,2284类,年龄限制8个区段(0~2、4~6、8~13、15~20、25~32、38~43、48~53、60~),含有噪声、姿势、光照变化。aligned
# 经过剪裁对齐多少,faces #
原始数据。fold_0_data.txt至fold_4_data.txt
全部数量符号。fold_frontal_0_data.txt至fold_frontal_4_data.txt
仅用类似正面态度面部标记。数据结构 user_id
用户Flickr帐户ID、original_image 图片文件名、face_id
人标识符、age、gender、x、y、dx、dy 人脸边框、tilt_ang
切斜角度、fiducial_yaw_angle 基准偏移角度、fiducial_score
基准分数。https://www.flickr.com/

人脸识别应用,美图秀秀美颜应用、世纪佳缘查看地下配偶“面相”相似度,支付领域“刷脸支付”,安全防范领域“人脸鉴权”。Face++、商汤科技(science and technology),提供人脸识别SDK。

人脸检验。https://github.com/davidsandberg/facenet

人脸检查实验。检查测试、定位图片人脸,再次来到高业饿啊人脸框坐标。对人脸分析、处理的首先步。“滑动窗口”,采用图像矩形区域作滑动窗口,窗口中领到特征对图像区域描述,依照特征描述判断窗口是不是人脸。不断遍历须要旁观窗口。

人脸识别优势,非强制性(采集格局不便于被察觉,被识旁人脸图像可积极获取)、非接触性(用户不须要与装备接触)、并发性(可同时多个人脸检查测试、跟踪、识别)。深度学习前,人脸识别两步骤:高维人工特征提取、降维。古板人脸识别技术基于可知光图像。深度学习+大数量(海量有标注人脸数据)为人脸识别领域主流技术路线。神经互联网人脸识别技术,多量样本图像磨练识别模型,无需人工选拔特征,样本陶冶进程自行学习,识别准确率能够直达9玖%。

def parse_arguments(argv):
parser = argparse.ArgumentParser()

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
import argparse
import facenet
import lfw
import os
import sys
import math
from sklearn import metrics
from scipy.optimize import brentq
from scipy import interpolate

if step % 1000 == 0 or (step + 1) == num_steps:
saver.save(sess, checkpoint_path, global_step=step)
if __name__ == ‘__main__’:
tf.app.run()

# Run forward pass to calculate embeddings
# 三. 使用前向传播验证
print(‘Runnning forward pass on LFW images’)
batch_size = args.lfw_batch_size
nrof_images = len(paths)
nrof_batches = int(math.ceil(1.0*nrof_images / batch_size)) #
总共批次数
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches):
start_index = i*batch_size
end_index = min((i+1)*batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False, image_size)
feed_dict = { images_placeholder:images,
phase_train_placeholder:False }
emb_array[start_index:end_index,:] = sess.run(embeddings,
feed_dict=feed_dict)

LFW(Labeled Faces in the Wild
Home)数据集。http://vis-www.cs.umass.edu/lfw/
。U.S.密苏里高校阿姆斯特分校总计机视觉实验室整理。1323叁张图片,5745个人。40玖七位唯有一张图纸,16捌拾三位多于一张。每张图片尺寸250×250。人脸图片在各类人物名字文件夹下。

weights = tf.Variable(tf.random_normal([512, nlabels], mean=0.0,
stddev=0.01), name=’weights’)
biases = tf.Variable(tf.constant(0.0, shape=[nlabels],
dtype=tf.float32), name=’biases’)
output = tf.add(tf.matmul(drop2, weights), biases, name=scope.name)
return output
def levi_hassner(nlabels, images, pkeep, is_training):
weight_decay = 0.0005
weights_regularizer =
tf.contrib.layers.l2_regularizer(weight_decay)
with tf.variable_scope(“LeviHassner”, “LeviHassner”, [images]) as
scope:
with tf.contrib.slim.arg_scope(
[convolution2d, fully_connected],
weights_regularizer=weights_regularizer,
biases_initializer=tf.constant_initializer(1.),
weights_initializer=tf.random_normal_initializer(stddev=0.005),
trainable=True):
with tf.contrib.slim.arg_scope(
[convolution2d],
weights_initializer=tf.random_normal_initializer(stddev=0.01)):
conv1 = convolution2d(images, 96, [7,7], [4, 4], padding=’VALID’,
biases_initializer=tf.constant_initializer(0.), scope=’conv1′)
pool1 = max_pool2d(conv1, 3, 2, padding=’VALID’, scope=’pool1′)
norm1 = tf.nn.local_response_normalization(pool1, 5, alpha=0.0001,
beta=0.75, name=’norm1′)
conv2 = convolution2d(norm1, 256, [5, 5], [1, 1], padding=’SAME’,
scope=’conv2′)
pool2 = max_pool2d(conv2, 3, 2, padding=’VALID’, scope=’pool2′)
norm2 = tf.nn.local_response_normalization(pool2, 5, alpha=0.0001,
beta=0.75, name=’norm2′)
conv3 = convolution2d(norm2, 384, [3, 3], [1, 1],
biases_initializer=tf.constant_initializer(0.), padding=’SAME’,
scope=’conv3′)
pool3 = max_pool2d(conv3, 3, 2, padding=’VALID’, scope=’pool3′)
flat = tf.reshape(pool3, [-1, 384*6*6], name=’reshape’)
full1 = fully_connected(flat, 512, scope=’full1′)
drop1 = tf.nn.dropout(full1, pkeep, name=’drop1′)
full2 = fully_connected(drop1, 512, scope=’full2′)
drop2 = tf.nn.dropout(full2, pkeep, name=’drop2′)
with tf.variable_scope(‘output’) as scope:

def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:

saver = tf.train.Saver()
saver.restore(sess, model_checkpoint_path)

if __name__ == ‘__main__’:
tf.app.run()