keras 데이터 세트에서 MNIST를 읽고 PNG 이미지로 출력하는 절차
하고 싶은 일
MNIST란?
구현
데이터 세트 로드
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
이곳에서
데이터 세트 로드
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
이곳에서
이다.
내용을 확인해 본다
x_train : 학습용 데이터 「이미지」
print(x_train[0])
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 3 18 18 18 126 136 175 26 166 255 247 127 0 0 0 0
[ 0 0 0 0 0 0 0 0 30 36 94 154 170 253 253 253 253 253 225 172 253 242 195 64 0 0 0 0
[ 0 0 0 0 0 0 0 49 238 253 253 253 253 253 253 253 253 251 93 82 82 56 39 0 0 0 0 0
[ 0 0 0 0 0 0 0 18 219 253 253 253 253 253 198 182 247 241 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 80 156 107 253 253 205 11 0 43 154 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 14 1 154 253 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 139 253 190 2 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 11 190 253 70 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 35 241 225 160 108 1 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 81 240 253 253 119 25 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45 186 253 253 150 27 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 93 252 253 187 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249 253 249 64 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46 130 183 253 253 207 2 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 39 148 229 253 253 253 250 182 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 24 114 221 253 253 253 253 201 78 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 23 66 213 253 253 253 253 198 81 2 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 18 171 219 253 253 253 253 195 80 9 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 55 172 226 253 253 253 253 244 133 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 136 253 253 253 212 135 132 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
print(x_train[1])
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 51 159 253 159 50 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 238 252 252 252 237 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 54 227 253 252 239 233 252 57 6 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 10 60 224 252 253 252 202 84 252 253 122 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 163 252 252 252 253 252 252 96 189 253 167 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 51 238 253 253 190 114 253 228 47 79 255 168 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 48 238 252 252 179 12 75 121 21 0 0 253 243 50 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 38 165 253 233 208 84 0 0 0 0 0 0 253 252 165 0 0 0 0 0
[ 0 0 0 0 0 0 0 7 178 252 240 71 19 28 0 0 0 0 0 0 253 252 195 0 0 0 0 0
[ 0 0 0 0 0 0 0 57 252 252 63 0 0 0 0 0 0 0 0 0 253 252 195 0 0 0 0 0
[ 0 0 0 0 0 0 0 198 253 190 0 0 0 0 0 0 0 0 0 0 255 253 196 0 0 0 0 0
[ 0 0 0 0 0 0 76 246 252 112 0 0 0 0 0 0 0 0 0 0 253 252 148 0 0 0 0 0
[ 0 0 0 0 0 0 85 252 230 25 0 0 0 0 0 0 0 0 7 135 253 186 12 0 0 0 0 0
[ 0 0 0 0 0 0 85 252 223 0 0 0 0 0 0 0 0 7 131 252 225 71 0 0 0 0 0 0
[ 0 0 0 0 0 0 85 252 145 0 0 0 0 0 0 0 48 165 252 173 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 86 253 225 0 0 0 0 0 0 114 238 253 162 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 85 252 249 146 48 29 85 178 225 253 223 167 56 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 85 252 252 252 229 215 252 252 252 196 130 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 28 199 252 252 253 252 252 233 145 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 25 128 252 253 252 141 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
y_train : 학습 데이터 "라벨"
print(y_train[0]) 는 5
print(y_train[1]) 는 0
이다. 즉, 그 화상이 「무엇의 숫자인가?」의 라벨이며, 0부터 9까지의 수치이다.
이미지 데이터를 png로 출력하고 라벨 값을 파일 이름에 포함
구현
import tensorflow as tf
from PIL import Image
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
for num in range(10):
label_num = y_train[num]
np_ary = x_train[num]
pil_img = Image.fromarray(np_ary)
pil_img.save("output_" + str(num) + "_label_is_" + str(label_num) + ".png")
출력
10장의 png가 출력되어 라벨 값이 파일명에 포함되어 있다.
Reference
이 문제에 관하여(keras 데이터 세트에서 MNIST를 읽고 PNG 이미지로 출력하는 절차), 우리는 이곳에서 더 많은 자료를 발견하고 링크를 클릭하여 보았다
https://qiita.com/kenichiro-yamato/items/2e1dbacf89e9ff32ba21
텍스트를 자유롭게 공유하거나 복사할 수 있습니다.하지만 이 문서의 URL은 참조 URL로 남겨 두십시오.
우수한 개발자 콘텐츠 발견에 전념
(Collection and Share based on the CC Protocol.)
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 3 18 18 18 126 136 175 26 166 255 247 127 0 0 0 0
[ 0 0 0 0 0 0 0 0 30 36 94 154 170 253 253 253 253 253 225 172 253 242 195 64 0 0 0 0
[ 0 0 0 0 0 0 0 49 238 253 253 253 253 253 253 253 253 251 93 82 82 56 39 0 0 0 0 0
[ 0 0 0 0 0 0 0 18 219 253 253 253 253 253 198 182 247 241 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 80 156 107 253 253 205 11 0 43 154 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 14 1 154 253 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 139 253 190 2 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 11 190 253 70 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 35 241 225 160 108 1 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 81 240 253 253 119 25 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45 186 253 253 150 27 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 93 252 253 187 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249 253 249 64 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46 130 183 253 253 207 2 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 39 148 229 253 253 253 250 182 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 24 114 221 253 253 253 253 201 78 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 23 66 213 253 253 253 253 198 81 2 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 18 171 219 253 253 253 253 195 80 9 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 55 172 226 253 253 253 253 244 133 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 136 253 253 253 212 135 132 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 51 159 253 159 50 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 238 252 252 252 237 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 54 227 253 252 239 233 252 57 6 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 10 60 224 252 253 252 202 84 252 253 122 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 163 252 252 252 253 252 252 96 189 253 167 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 51 238 253 253 190 114 253 228 47 79 255 168 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 48 238 252 252 179 12 75 121 21 0 0 253 243 50 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 38 165 253 233 208 84 0 0 0 0 0 0 253 252 165 0 0 0 0 0
[ 0 0 0 0 0 0 0 7 178 252 240 71 19 28 0 0 0 0 0 0 253 252 195 0 0 0 0 0
[ 0 0 0 0 0 0 0 57 252 252 63 0 0 0 0 0 0 0 0 0 253 252 195 0 0 0 0 0
[ 0 0 0 0 0 0 0 198 253 190 0 0 0 0 0 0 0 0 0 0 255 253 196 0 0 0 0 0
[ 0 0 0 0 0 0 76 246 252 112 0 0 0 0 0 0 0 0 0 0 253 252 148 0 0 0 0 0
[ 0 0 0 0 0 0 85 252 230 25 0 0 0 0 0 0 0 0 7 135 253 186 12 0 0 0 0 0
[ 0 0 0 0 0 0 85 252 223 0 0 0 0 0 0 0 0 7 131 252 225 71 0 0 0 0 0 0
[ 0 0 0 0 0 0 85 252 145 0 0 0 0 0 0 0 48 165 252 173 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 86 253 225 0 0 0 0 0 0 114 238 253 162 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 85 252 249 146 48 29 85 178 225 253 223 167 56 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 85 252 252 252 229 215 252 252 252 196 130 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 28 199 252 252 253 252 252 233 145 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 25 128 252 253 252 141 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
구현
import tensorflow as tf
from PIL import Image
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
for num in range(10):
label_num = y_train[num]
np_ary = x_train[num]
pil_img = Image.fromarray(np_ary)
pil_img.save("output_" + str(num) + "_label_is_" + str(label_num) + ".png")
출력
10장의 png가 출력되어 라벨 값이 파일명에 포함되어 있다.
Reference
이 문제에 관하여(keras 데이터 세트에서 MNIST를 읽고 PNG 이미지로 출력하는 절차), 우리는 이곳에서 더 많은 자료를 발견하고 링크를 클릭하여 보았다 https://qiita.com/kenichiro-yamato/items/2e1dbacf89e9ff32ba21텍스트를 자유롭게 공유하거나 복사할 수 있습니다.하지만 이 문서의 URL은 참조 URL로 남겨 두십시오.
우수한 개발자 콘텐츠 발견에 전념 (Collection and Share based on the CC Protocol.)