MNIST 소개 및 tensorboard 연결하기 소스코드 : https://github.com/AhnSeongHyun/mnist_cnn_tensorboard

1. MNIST, tensorboard
안성현
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2. MNIST
• MNIST는 간단한 컴퓨터 비전 데이터셋
• train-images-idx3-ubyte.gz: training set images (9912422 bytes)
train-labels-idx1-ubyte.gz: training set labels (28881 bytes)
t10k-images-idx3-ubyte.gz: test set images (1648877 bytes)
t10k-labels-idx1-ubyte.gz: test set labels (4542 bytes
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3. MNIST Images
• 파일이 아님.
• 하나의 이미지를 784개의 데이터로 표현
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4. MNIST Label
• 글자 이미지가 실제 어떤 글자인지를 나타낸다.
• [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] => 각 인덱스가 숫자를 나타냄
• 1 = [0, 1, 0, 0, 0, 0, 0, 0, 0, 0] , 9 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
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5. Tensorflow 설치하기
-pip install --upgrade tensorflow
-소스빌드해서 설치가능
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6. MNIST Tensorflow 에서 불러오기
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/")
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7. MNIST Tensorflow 에서 불러오기
# -*- coding:utf-8 -*-
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
for key in ['train', 'test', 'validation']:
print("mnist.%s :" % key)
minst_dataset= getattr(mnist, key)
print("tnum_examples : %d" % minst_dataset.num_examples)
print("timages count : %d" % len(minst_dataset.images))
print("tlabels count : %d" % len(minst_dataset.labels))
print("timages[0] len : %d" % len(minst_dataset.images[0]))
print("tlabels[0] len : %d" % len(minst_dataset.labels[0]))
print("tlabels[0] : %s" % str(minst_dataset.labels[0]))
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8. MNIST Tensorflow 에서 불러오기
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
for key in ['train', 'test', 'validation']:
print("mnist.%s :" % key)
minst_dataset= getattr(mnist, key)
print("tnum_examples : %d" % minst_dataset.num_examples)
print("timages count : %d" % len(minst_dataset.images))
print("tlabels count : %d" % len(minst_dataset.labels))
print("timages[0] len : %d" % len(minst_dataset.images[0]))
print("tlabels[0] len : %d" % len(minst_dataset.labels[0]))
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9. urllib.error.URLError: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:7)
0SX) Python3.x SSL Error
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10. tensorBoard 띄우기
Usage : tensorboard --help
$ usage: tensorboard [-h]
[--logdir LOGDIR]
[--debug DEBUG]
[--nodebug]
[--host HOST]
[--port PORT]
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11. tensorboard 띄우기
$ tensorboard --logdir=log
$ TensorBoard b'54' at http://anui-MacBook-Pro.local:6006
- http://127.0.0.1:6006 접속
$ tensorboard --logdir=log --host 0.0.0.0 --port 6006
- 외부에서 접속가능
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12. CNN 구성
https://hunkim.github.io/ml/
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13. CNN 구성
# L1 ImgIn shape=(?, 28, 28, 1)
# 필터의 크기 3X3, 32개의 필터
W1 = tf.Variable(tf.random_normal([3, 3, 1, 32], stddev=0.01))
# Conv -> (?, 28, 28, 32)
# Pool -> (?, 14, 14, 32)
# 이동거리 1
L1 = tf.nn.conv2d(X_img, W1, strides=[1, 1, 1, 1], padding='SAME')
L1 = tf.nn.relu(L1)
L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
'''
Tensor("Conv2D:0", shape=(?, 28, 28, 32), dtype=float32)
Tensor("Relu:0", shape=(?, 28, 28, 32), dtype=float32)
Tensor("MaxPool:0", shape=(?, 14, 14, 32), dtype=float32)
'''
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14. CNN 구성
# L2 ImgIn shape=(?, 14, 14, 32)
W2 = tf.Variable(tf.random_normal([3, 3, 32, 64], stddev=0.01))
# Conv ->(?, 14, 14, 64)
# Pool ->(?, 7, 7, 64)
L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')
L2 = tf.nn.relu(L2)
L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
L2_flat = tf.reshape(L2, [-1, 7 * 7 * 64])
W3 = tf.get_variable("W3", shape=[7 * 7 * 64, 10],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.Variable(tf.random_normal([10]), name='b')
logits = tf.matmul(L2_flat, W3) + b
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
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15. CNN-MNIST 결과 tensorboard 연결하기
saver 를 이용해서 model 을 --logdir path 에 저장
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16. CNN-MNIST 결과 tensorboard 연결하기
• Data Point <= Label, Images
• metadata file
• Label : .tsv 파일
• Images : 단일 sprite image, 8192 X 8192 까지 지원
• projector_config.pbtxt
• API 를 이용해서 생성
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17. embedding = tf.Variable(tf.zeros([1024, 10]), name="test_embedding")
assignment = embedding.assign(logits)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter(LOGDIR + "/embedding")
writer.add_graph(sess.graph)
config = tf.contrib.tensorboard.plugins.projector.ProjectorConfig()
embedding_config = config.embeddings.add()
embedding_config.tensor_name = embedding.name
embedding_config.metadata_path = LOGDIR + 'labels_1024.tsv‘ # 메타데이터 지정
embedding_config.sprite.image_path = LOGDIR + 'sprite_1024.png‘ # sprite 이미지 파일 지정
embedding_config.sprite.single_image_dim.extend([28, 28]) # 이미지 사이즈 지정
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(writer, config)
if i % 500 == 0:
sess.run(assignment, feed_dict={X: mnist.test.images[:1024], Y: mnist.test.labels[:1024]})
saver.save(sess, os.path.join(LOGDIR, "model.ckpt"), i) 17
생성 : projector_config.pbtxt

18. CNN-MNIST 결과 tensorboard 연결하기
• https://github.com/jireh-father/tensorboard-embedding-visualization
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19. Reference
• https://tensorflowkorea.gitbooks.io/tensorflow-kr/content/g3doc/tutorials/mnist/beginners/
• http://bcho.tistory.com/1154
• https://www.google.co.kr/search?q=image+to+mnist+format&oq=image+to+mnisr+&aqs=chrome.1.69i57j0l2.6888j0j4&sourceid=chrom
e&ie=UTF-8
• http://goodtogreate.tistory.com/entry/Hands-on-TensorBoard-TensorFlow-Dev-Summit2017
• 모두의 딥러닝 : https://hunkim.github.io/ml/
• http://sanghyukchun.github.io/75/
• http://yujuwon.tistory.com/entry/TENSORFLOWCNN2
• http://yujuwon.tistory.com/entry/TENSORFLOW-Image-AutoEncoder
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