The document discusses MNIST, a simple computer vision dataset for handwritten digits. It describes the dataset contents and file formats, and how to load the MNIST data into Tensorflow for use in building and evaluating convolutional neural networks (CNNs) for digit recognition. Code examples are provided for constructing and training a basic CNN on MNIST, and connecting the trained model to Tensorboard for visualization of results.

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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