tfk 6618 tensor_flow로얼굴인식구현_r10_mariocho
TRANSCRIPT
Who am I ?Development Experience◆ Image Recognition using Neural Network◆ Bio-Medical Data Processing◆ Human Brain Mapping on High Performance
Computing◆ Medical Image Reconstruction
(Computer Tomography) ◆Enterprise System◆Open Source Software Developer
Open Source Software Developer◆ Linux Kernel & LLVM ◆ OPNFV (NFV&SDN) & OpenStack◆ Machine Learning (TensorFlow)
Book◆ Unix V6 Kernel
Korea Open Source Software Lab.Mario Cho
Contents
• 우리가 본다!
• 기초부터 MNIST
• CIF영상인식
• 학습데이터만들기
• Tensorflow에 적용
• 그리고 전설로.
Human Intelligence
Human Brain
Source: https://www.ll.mit.edu/publications/journal/pdf/vol 04_no2/4.2.5.neuralnetwork.pdf
Neural network vs Learning network
Neural Network Deep Learning Network
Human Neural network vs. Machine Learning network
Traditional learning vs Deep Machine Learning
Eiffel Tower
Eiffel Tower
RAW data
RAW data
Deep Learning Network
FeatureExtraction
Vectored Classification
Traditional Learning
Deep Learning
Caffe
U.C. Berkley 개발C++, Python, MATLAB대표적인기계학습을위한공개소프트웨어
* Source: http://caffe.berkeleyvision.org
TensorFlow
* Source: https://www.tensorflow.org/
Hierarchical Representation of Deep Learning
* Source: : Honglak Lee and colleagues (2011) as published in “Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks”.
Hello World on TensorFlow
Image recognition in Google Map
* Source: Oriol Vinyals – Research Scientist at Google Brain
Hello World == MNIST
Data Sets data_sets.train 55000 images & labelsdata_sets.validation 5000 images & labelsdata_sets.test 10000 images & labels
MNIST (predict number of image)
MNIST beginner code
Face recognition?
CIFAR-10
• CIFAR-10� 32x32 크기로 컬러 영상 � 10종류로 구분됨. � TensorFlow 예제: tensorflow/models/image/cifar10/
CIFAR-10 Model
Data Sets data_sets.train 50000 images & labelsdata_sets.validation 1000 images & labelsdata_sets.test 10000 images & labels
Source: http://www.cs.toronto.edu/~kriz/cifar.html
CIFAR-10 data• Image Data의 특징
� 10종류로 32x32로된 인식 +이미지로 구성� 학습 데이터(Training data)
� data_batch_1.bin, data_batch_2.bin, … , data_batch_5.bin� 테스트 데이터(Test data)
� test_batch.bin
• 1개의 이미지파일(image file)� 32(가로) x 32(세로) x 3(RGB) = 3072 byte� 3 tensor image (RGB)
• *참조: 텐서 플로우의 이미지 형식
tf.image_summary(tag, tensor, max_images=3, collections = None, Name=None) � [0, 255].unint8 � 1 tensor: Grayscale � 3 tensor: RGB � 4 tensor: RGBA
CIFAR-10 learning networkLayer Name Descriptionconv1 convolution and rectified linear activation.
pool1 max pooling.
norm1 local response normalization.
conv2 convolution and rectified linear activation.norm2 local response normalization.pool2 max pooling.local3 fully connected layer with rectified linear activation.local4 fully connected layer with rectified linear activation.softmax_linear Linear transformation to produce logits
Source data: 얼굴 인식에 사용할 사진을 모으자!
Prepare: 사진에서얼굴만추출하자.
OpenCV
Face Detection using Haar Cascades
Source: http://opencv-python-tutroals.r eadthedocs.io/en/latest/py_tutorials/py_objdetect/pya_face_detection/py_face_detec tion.html#face-detection
Open CV: face dectec code
CIFAR-10 data• TFRecode file
� TensorFlow에 TFRcords를이용하여 binary 데이터를연속된 데이터로변형.� 연속된데이터를 Reader, Writer를이용해서 읽고쓸수 있다.� 고정길이가 아닌구조형 데이터도읽고쓰는 처리가능
� 1 image file 은� 32(가로) x 32(세로) x 3(RGB) = 3072 byte
� 1 label + 1 file image file 은� < 1 x label > < 3072 x pixel>
� 1000 label + 1000 image file � 32 * 32 * 3 * 1000 = 3 072 000 (3MB)
� Filenames, shuffling� [“files0”, “file1”], � (“file%d” % i) for I in range(2)
• tf.train.input_producer(input_tensor, element_shape=None, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, summary_name=None, name=None)� input_tensor: 1차열로구성� element_shape: (Optional.) 입력 텐서의형태� num_epochs: (Optional.) � shuffle: (Optional.):데이터를 임으로섞어 놓을지?� seed: (Optional.):데이터의 씨앗을사용?� capacity: (Optional.):버퍼를사용한 입력.� name: (Optional.) 큐의이름
CIFAR-10 data sets 형식으로바꾸자.
CIFAR-10 에서대상을얼굴추출자료로바꾸자.
TensorFlow Training
� 학습이 잘 된 것처럼 보이나되어서 평가를 해보면
CNN layer를 바꿔가며 자신만의 방법을…• X2 conv layers
96 x 96 x 3
48 x 48 x 3224 x 24 x 64
12 x 12 x 128
6 x 6 x 256
(6 x 6 x 256) * 1024
1024 x 256
256 x 5
Cf. weight compare- cifar10:
- (5 x 5 x 3 x 64) + (5 x 5 x 64 x 64) + (4096 * 384) + (384 * 192) + (192 * 6)
= 1754944- X2 conv:
- (3 x 3 x 3 x 32) + (3 x 3x 32 x 64) + (3 x 3 x 64 x 128) + (3 x 3 x 128 x 256) + (9216 x 1024) + (1024 x 256) + (256 x 5)
= 10088544 (5.7x cifar10 )
CNN 2x code
소결!
• CIFAR-10을 응용하여 얼굴 인식을 해보자.
•이쁜 언니들 얼굴 자료를 모으고
•전체 사진에서 Open CV를 이용해서 CIFAR-10에 맞게 얼굴만을 데이터를 수정해서
• CIFAR-10 형식으로 데이터 변환했다.
•그리고 python cifar-10을 실행하니
•학습까지 잘되었다? 진짜!?!
뭐가문제냐?
face extraction
#1. No. of Face data
# 2. Face extraction method
#3. advanced face detect
#4 View side effect
Source: http://socialsciences.uow.edu.au/psychology/research/pcl/members/UOW040587.html
#5 Emphasis?
#6. Eigen faces
Source: http://jbhuang0604.blogspot.kr/2013/04/miss-korea-2013-contestants-face.html
#6. Converging on the same face
Source: http://jbhuang0604.blogspot.kr/2013/04/miss-korea-2013-contestants-face.html
Make Her Beautiful?
Source: http://www.estherhonig.com
Thanks you!
Q&A