Deteksi Digit Angka Pada Tulisan Tangan dengan Metode Deep Learning Berbasis H2O
Hellan Bagus Sedyansyah
17 Februari 2020
Metode deep learning yang digunakan adalah metode recurrent neural network(RNN). RNN adalah jenis arsitektur jaringan saraf tiruan yang pemrosesannya dipanggil berulang-ulang untuk memroses masukan yang biasanya adalah data sekuensial.
Mengatifkan H2O
library("h2o")##
## ----------------------------------------------------------------------
##
## Your next step is to start H2O:
## > h2o.init()
##
## For H2O package documentation, ask for help:
## > ??h2o
##
## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit http://docs.h2o.ai
##
## ----------------------------------------------------------------------##
## Attaching package: 'h2o'## The following objects are masked from 'package:stats':
##
## cor, sd, var## The following objects are masked from 'package:base':
##
## %*%, %in%, &&, ||, apply, as.factor, as.numeric, colnames,
## colnames<-, ifelse, is.character, is.factor, is.numeric, log,
## log10, log1p, log2, round, signif, trunch2o.init(nthreads=-1,max_mem_size="3G")##
## H2O is not running yet, starting it now...
##
## Note: In case of errors look at the following log files:
## C:\Users\Hellan\AppData\Local\Temp\RtmpSeXob7/h2o_Hellan_started_from_r.out
## C:\Users\Hellan\AppData\Local\Temp\RtmpSeXob7/h2o_Hellan_started_from_r.err
##
##
## Starting H2O JVM and connecting: .. Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 11 seconds 464 milliseconds
## H2O cluster timezone: Asia/Bangkok
## H2O data parsing timezone: UTC
## H2O cluster version: 3.20.0.8
## H2O cluster version age: 1 year, 6 months and 12 days !!!
## H2O cluster name: H2O_started_from_R_Hellan_lxa421
## H2O cluster total nodes: 1
## H2O cluster total memory: 2.67 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## H2O API Extensions: Algos, AutoML, Core V3, Core V4
## R Version: R version 3.5.1 (2018-07-02)## Warning in h2o.clusterInfo():
## Your H2O cluster version is too old (1 year, 6 months and 12 days)!
## Please download and install the latest version from http://h2o.ai/download/Membaca data mnist untuk training dan testing
train_mnist=read.csv("mnist_train_100.csv", header=FALSE)
head(train_mnist)## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
## 1 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V39 V40 V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55 V56
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V57 V58 V59 V60 V61 V62 V63 V64 V65 V66 V67 V68 V69 V70 V71 V72 V73 V74
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V75 V76 V77 V78 V79 V80 V81 V82 V83 V84 V85 V86 V87 V88 V89 V90 V91 V92
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V93 V94 V95 V96 V97 V98 V99 V100 V101 V102 V103 V104 V105 V106 V107 V108
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V109 V110 V111 V112 V113 V114 V115 V116 V117 V118 V119 V120 V121 V122
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V123 V124 V125 V126 V127 V128 V129 V130 V131 V132 V133 V134 V135 V136
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 145 255 211 31 0 0 0 0 0 0 0
## V137 V138 V139 V140 V141 V142 V143 V144 V145 V146 V147 V148 V149 V150
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V151 V152 V153 V154 V155 V156 V157 V158 V159 V160 V161 V162 V163 V164
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 13 29 29 66 28 0 0 10 179 242 47
## 3 0 15 108 233 253 255 180 101 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 85 255 103 1 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 67 232 39
## 6 0 0 32 237 253 252 71 0 0 0 0 0 0 0
## V165 V166 V167 V168 V169 V170 V171 V172 V173 V174 V175 V176 V177 V178
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 62 81 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V179 V180 V181 V182 V183 V184 V185 V186 V187 V188 V189 V190 V191 V192
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 19 144 253 252 252 215 170 82 28 209 253 84
## 3 36 219 252 252 252 253 252 227 50 0 0 0 0 0
## 4 0 0 0 0 0 0 0 205 253 253 30 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 120 180 39
## 6 0 0 11 175 253 252 71 0 0 0 0 0 0 0
## V193 V194 V195 V196 V197 V198 V199 V200 V201 V202 V203 V204 V205 V206
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 85
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 126 163 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V207 V208 V209 V210 V211 V212 V213 V214 V215 V216 V217 V218 V219 V220
## 1 0 0 0 0 7 67 141 205 255 255 153 0 0 0
## 2 0 0 123 252 253 252 252 252 253 240 72 210 253 84
## 3 222 252 233 141 69 79 227 252 160 0 0 0 0 0
## 4 0 0 0 0 0 0 0 205 253 253 30 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 2 153 210 40
## 6 0 0 0 144 253 252 71 0 0 0 0 0 0 0
## V221 V222 V223 V224 V225 V226 V227 V228 V229 V230 V231 V232 V233 V234
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 116
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 220 163 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V235 V236 V237 V238 V239 V240 V241 V242 V243 V244 V245 V246 V247 V248
## 1 0 45 57 121 188 253 253 254 253 253 253 0 0 0
## 2 0 151 246 252 178 28 28 28 253 151 91 252 253 84
## 3 253 235 64 0 0 0 161 252 160 0 0 0 0 0
## 4 0 0 0 0 0 0 44 233 253 244 27 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 27 254 162 0
## 6 0 0 16 191 253 252 71 0 0 0 0 0 0 0
## V249 V250 V251 V252 V253 V254 V255 V256 V257 V258 V259 V260 V261 V262
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 111
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 11
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 222 163 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V263 V264 V265 V266 V267 V268 V269 V270 V271 V272 V273 V274 V275 V276
## 1 198 241 253 254 253 253 215 179 253 253 165 0 0 0
## 2 16 179 253 178 0 0 0 0 166 91 229 253 201 0
## 3 128 18 0 0 0 22 244 252 108 0 0 0 0 0
## 4 0 0 0 0 0 0 135 253 253 100 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 183 254 125 0
## 6 0 0 26 221 253 252 124 31 0 0 0 0 0 0
## V277 V278 V279 V280 V281 V282 V283 V284 V285 V286 V287 V288 V289 V290
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 242
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 46 245 163 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V291 V292 V293 V294 V295 V296 V297 V298 V299 V300 V301 V302 V303 V304
## 1 253 253 253 191 116 28 16 79 253 253 40 0 0 0
## 2 47 196 252 103 0 0 0 0 16 215 252 252 0 0
## 3 0 0 0 0 0 97 253 184 0 0 0 0 0 0
## 4 0 0 0 0 0 0 153 253 240 76 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 198 254 56 0
## 6 0 0 0 125 253 252 252 108 0 0 0 0 0 0
## V305 V306 V307 V308 V309 V310 V311 V312 V313 V314 V315 V316 V317 V318
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 64
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 120 254 163 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V319 V320 V321 V322 V323 V324 V325 V326 V327 V328 V329 V330 V331 V332
## 1 114 114 13 0 0 0 0 142 254 207 13 0 0 0
## 2 0 131 252 228 38 0 0 0 204 252 252 127 0 0
## 3 0 0 0 38 99 253 244 98 0 0 0 0 0 0
## 4 0 0 0 0 0 12 208 253 166 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 23 231 254 29 0
## 6 0 0 0 0 253 252 252 108 0 0 0 0 0 0
## V333 V334 V335 V336 V337 V338 V339 V340 V341 V342 V343 V344 V345 V346
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 159 254 120 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V347 V348 V349 V350 V351 V352 V353 V354 V355 V356 V357 V358 V359 V360
## 1 0 0 0 0 0 0 26 217 253 143 0 0 0 0
## 2 0 32 228 252 226 38 38 213 253 252 127 3 0 0
## 3 0 13 153 240 252 253 240 101 13 0 0 0 0 0
## 4 0 0 0 0 0 69 253 253 142 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 163 254 216 16 0
## 6 0 0 0 0 255 253 253 108 0 0 0 0 0 0
## V361 V362 V363 V364 V365 V366 V367 V368 V369 V370 V371 V372 V373 V374
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 159 254 67 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V375 V376 V377 V378 V379 V380 V381 V382 V383 V384 V385 V386 V387 V388
## 1 0 0 0 0 0 0 151 254 234 37 0 0 0 0
## 2 0 0 85 253 255 203 253 253 214 38 0 0 0 0
## 3 0 99 252 252 252 253 252 252 215 19 0 0 0 0
## 4 0 0 0 0 14 110 253 235 33 0 0 0 0 0
## 5 0 0 0 0 0 0 14 86 178 248 254 91 0 0
## 6 0 0 0 0 253 252 252 108 0 0 0 0 0 0
## V389 V390 V391 V392 V393 V394 V395 V396 V397 V398 V399 V400 V401 V402
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 159 254 85 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V403 V404 V405 V406 V407 V408 V409 V410 V411 V412 V413 V414 V415 V416
## 1 0 0 0 0 0 0 226 254 197 0 0 0 0 0
## 2 0 0 28 184 253 252 252 202 0 0 0 0 0 0
## 3 0 26 221 210 137 23 96 221 252 128 0 0 0 0
## 4 0 0 0 0 63 223 235 130 0 0 0 0 0 0
## 5 47 49 116 144 150 241 243 234 179 241 252 40 0 0
## 6 0 0 0 0 253 252 252 108 0 0 0 0 0 0
## V417 V418 V419 V420 V421 V422 V423 V424 V425 V426 V427 V428 V429 V430
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 150 253 237 207 207 207
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V431 V432 V433 V434 V435 V436 V437 V438 V439 V440 V441 V442 V443 V444
## 1 0 0 0 0 0 48 242 252 75 0 0 0 0 0
## 2 0 0 29 184 253 252 252 28 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 70 253 253 64 0 0 0
## 4 0 0 0 0 186 253 235 37 0 0 0 0 0 0
## 5 253 254 250 240 198 143 91 28 5 233 250 0 0 0
## 6 0 0 0 0 253 252 252 108 0 0 0 0 0 0
## V445 V446 V447 V448 V449 V450 V451 V452 V453 V454 V455 V456 V457 V458
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 119 177 177 177 177
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V459 V460 V461 V462 V463 V464 V465 V466 V467 V468 V469 V470 V471 V472
## 1 0 0 0 0 0 160 253 201 0 0 0 0 0 0
## 2 0 26 159 252 253 252 252 178 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 25 223 252 116 0 0 0
## 4 0 0 0 17 145 253 231 35 0 0 0 0 0 0
## 5 177 98 56 0 0 0 0 0 102 254 220 0 0 0
## 6 0 0 0 0 255 253 253 170 0 0 0 0 0 0
## V473 V474 V475 V476 V477 V478 V479 V480 V481 V482 V483 V484 V485 V486
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V487 V488 V489 V490 V491 V492 V493 V494 V495 V496 V497 V498 V499 V500
## 1 0 0 0 0 45 241 253 114 0 0 0 0 0 0
## 2 0 120 253 253 114 194 253 253 63 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 207 252 116 0 0 0
## 4 0 0 0 69 220 231 123 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 169 254 137 0 0 0
## 6 0 0 0 0 253 252 252 252 42 0 0 0 0 0
## V501 V502 V503 V504 V505 V506 V507 V508 V509 V510 V511 V512 V513 V514
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V515 V516 V517 V518 V519 V520 V521 V522 V523 V524 V525 V526 V527 V528
## 1 0 0 0 0 57 253 253 114 0 0 0 0 0 0
## 2 170 225 233 96 0 131 252 252 38 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 207 252 116 0 0 0
## 4 0 0 18 205 253 176 27 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 169 254 57 0 0 0
## 6 0 0 0 0 149 252 252 252 144 0 0 0 0 0
## V529 V530 V531 V532 V533 V534 V535 V536 V537 V538 V539 V540 V541 V542
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 89
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V543 V544 V545 V546 V547 V548 V549 V550 V551 V552 V553 V554 V555 V556
## 1 0 0 0 4 180 254 242 0 0 0 0 0 0 0
## 2 253 252 80 0 13 206 252 202 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 64 248 252 116 0 0 0
## 4 0 17 125 253 185 39 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 169 254 57 0 0 0
## 6 0 0 0 0 109 252 252 252 144 0 0 0 0 0
## V557 V558 V559 V560 V561 V562 V563 V564 V565 V566 V567 V568 V569 V570
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 38 225
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V571 V572 V573 V574 V575 V576 V577 V578 V579 V580 V581 V582 V583 V584
## 1 0 0 0 54 253 253 116 0 0 0 0 0 0 0
## 2 253 102 6 0 13 206 252 102 0 0 0 0 0 0
## 3 0 0 0 0 0 0 5 138 253 253 53 0 0 0
## 4 0 71 214 231 41 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 169 255 94 0 0 0
## 6 0 0 0 0 0 218 253 253 255 35 0 0 0 0
## V585 V586 V587 V588 V589 V590 V591 V592 V593 V594 V595 V596 V597 V598
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 86 253
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V599 V600 V601 V602 V603 V604 V605 V606 V607 V608 V609 V610 V611 V612
## 1 0 0 0 141 253 253 28 0 0 0 0 0 0 0
## 2 251 75 0 0 104 253 206 13 0 0 0 0 0 0
## 3 5 47 34 0 0 5 136 252 252 157 0 0 0 0
## 4 0 167 253 225 33 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 169 254 96 0 0 0
## 6 0 0 0 0 0 175 252 252 253 35 0 0 0 0
## V613 V614 V615 V616 V617 V618 V619 V620 V621 V622 V623 V624 V625 V626
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 110 252
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V627 V628 V629 V630 V631 V632 V633 V634 V635 V636 V637 V638 V639 V640
## 1 0 0 0 141 253 177 3 0 0 0 0 0 0 0
## 2 244 144 95 169 253 252 142 0 0 0 0 0 0 0
## 3 24 252 234 90 70 191 252 252 227 16 0 0 0 0
## 4 72 205 207 14 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 169 254 153 0 0 0
## 6 0 0 0 0 0 73 252 252 253 35 0 0 0 0
## V641 V642 V643 V644 V645 V646 V647 V648 V649 V650 V651 V652 V653 V654
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 110 252
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 30
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V655 V656 V657 V658 V659 V660 V661 V662 V663 V664 V665 V666 V667 V668
## 1 0 0 0 205 254 56 0 0 0 0 0 0 0 0
## 2 253 252 252 252 244 93 13 0 0 0 0 0 0 0
## 3 24 252 252 252 252 253 235 128 29 0 0 0 0 0
## 4 249 233 49 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 169 255 153 0 0 0
## 6 0 0 0 0 0 31 211 252 253 35 0 0 0 0
## V669 V670 V671 V672 V673 V674 V675 V676 V677 V678 V679 V680 V681 V682
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 10 128
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 32
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V683 V684 V685 V686 V687 V688 V689 V690 V691 V692 V693 V694 V695 V696
## 1 0 0 26 254 253 81 0 0 0 0 0 0 0 0
## 2 253 252 202 102 25 0 0 0 0 0 0 0 0 0
## 3 13 211 252 252 252 137 60 0 0 0 0 0 0 0
## 4 253 89 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 96 254 153 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V697 V698 V699 V700 V701 V702 V703 V704 V705 V706 V707 V708 V709 V710
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V711 V712 V713 V714 V715 V716 V717 V718 V719 V720 V721 V722 V723 V724
## 1 0 0 25 254 253 235 22 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V725 V726 V727 V728 V729 V730 V731 V732 V733 V734 V735 V736 V737 V738
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V739 V740 V741 V742 V743 V744 V745 V746 V747 V748 V749 V750 V751 V752
## 1 0 0 0 204 228 103 3 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V753 V754 V755 V756 V757 V758 V759 V760 V761 V762 V763 V764 V765 V766
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V767 V768 V769 V770 V771 V772 V773 V774 V775 V776 V777 V778 V779 V780
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V781 V782 V783 V784 V785
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0train_mnist[10,-1]## V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37 V38 V39
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V40 V41 V42 V43 V44 V45 V46 V47 V48 V49 V50 V51 V52 V53 V54 V55 V56 V57
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V58 V59 V60 V61 V62 V63 V64 V65 V66 V67 V68 V69 V70 V71 V72 V73 V74 V75
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V76 V77 V78 V79 V80 V81 V82 V83 V84 V85 V86 V87 V88 V89 V90 V91 V92 V93
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V94 V95 V96 V97 V98 V99 V100 V101 V102 V103 V104 V105 V106 V107 V108
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V109 V110 V111 V112 V113 V114 V115 V116 V117 V118 V119 V120 V121 V122
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V123 V124 V125 V126 V127 V128 V129 V130 V131 V132 V133 V134 V135 V136
## 10 0 0 0 0 0 0 51 159 253 159 50 0 0 0
## V137 V138 V139 V140 V141 V142 V143 V144 V145 V146 V147 V148 V149 V150
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V151 V152 V153 V154 V155 V156 V157 V158 V159 V160 V161 V162 V163 V164
## 10 0 0 0 0 0 48 238 252 252 252 237 0 0 0
## V165 V166 V167 V168 V169 V170 V171 V172 V173 V174 V175 V176 V177 V178
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V179 V180 V181 V182 V183 V184 V185 V186 V187 V188 V189 V190 V191 V192
## 10 0 0 0 0 54 227 253 252 239 233 252 57 6 0
## V193 V194 V195 V196 V197 V198 V199 V200 V201 V202 V203 V204 V205 V206
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V207 V208 V209 V210 V211 V212 V213 V214 V215 V216 V217 V218 V219 V220
## 10 0 0 10 60 224 252 253 252 202 84 252 253 122 0
## V221 V222 V223 V224 V225 V226 V227 V228 V229 V230 V231 V232 V233 V234
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V235 V236 V237 V238 V239 V240 V241 V242 V243 V244 V245 V246 V247 V248
## 10 0 0 163 252 252 252 253 252 252 96 189 253 167 0
## V249 V250 V251 V252 V253 V254 V255 V256 V257 V258 V259 V260 V261 V262
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V263 V264 V265 V266 V267 V268 V269 V270 V271 V272 V273 V274 V275 V276
## 10 0 51 238 253 253 190 114 253 228 47 79 255 168 0
## V277 V278 V279 V280 V281 V282 V283 V284 V285 V286 V287 V288 V289 V290
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V291 V292 V293 V294 V295 V296 V297 V298 V299 V300 V301 V302 V303 V304
## 10 48 238 252 252 179 12 75 121 21 0 0 253 243 50
## V305 V306 V307 V308 V309 V310 V311 V312 V313 V314 V315 V316 V317 V318
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 38
## V319 V320 V321 V322 V323 V324 V325 V326 V327 V328 V329 V330 V331 V332
## 10 165 253 233 208 84 0 0 0 0 0 0 253 252 165
## V333 V334 V335 V336 V337 V338 V339 V340 V341 V342 V343 V344 V345 V346
## 10 0 0 0 0 0 0 0 0 0 0 0 0 7 178
## V347 V348 V349 V350 V351 V352 V353 V354 V355 V356 V357 V358 V359 V360
## 10 252 240 71 19 28 0 0 0 0 0 0 253 252 195
## V361 V362 V363 V364 V365 V366 V367 V368 V369 V370 V371 V372 V373 V374
## 10 0 0 0 0 0 0 0 0 0 0 0 0 57 252
## V375 V376 V377 V378 V379 V380 V381 V382 V383 V384 V385 V386 V387 V388
## 10 252 63 0 0 0 0 0 0 0 0 0 253 252 195
## V389 V390 V391 V392 V393 V394 V395 V396 V397 V398 V399 V400 V401 V402
## 10 0 0 0 0 0 0 0 0 0 0 0 0 198 253
## V403 V404 V405 V406 V407 V408 V409 V410 V411 V412 V413 V414 V415 V416
## 10 190 0 0 0 0 0 0 0 0 0 0 255 253 196
## V417 V418 V419 V420 V421 V422 V423 V424 V425 V426 V427 V428 V429 V430
## 10 0 0 0 0 0 0 0 0 0 0 0 76 246 252
## V431 V432 V433 V434 V435 V436 V437 V438 V439 V440 V441 V442 V443 V444
## 10 112 0 0 0 0 0 0 0 0 0 0 253 252 148
## V445 V446 V447 V448 V449 V450 V451 V452 V453 V454 V455 V456 V457 V458
## 10 0 0 0 0 0 0 0 0 0 0 0 85 252 230
## V459 V460 V461 V462 V463 V464 V465 V466 V467 V468 V469 V470 V471 V472
## 10 25 0 0 0 0 0 0 0 0 7 135 253 186 12
## V473 V474 V475 V476 V477 V478 V479 V480 V481 V482 V483 V484 V485 V486
## 10 0 0 0 0 0 0 0 0 0 0 0 85 252 223
## V487 V488 V489 V490 V491 V492 V493 V494 V495 V496 V497 V498 V499 V500
## 10 0 0 0 0 0 0 0 0 7 131 252 225 71 0
## V501 V502 V503 V504 V505 V506 V507 V508 V509 V510 V511 V512 V513 V514
## 10 0 0 0 0 0 0 0 0 0 0 0 85 252 145
## V515 V516 V517 V518 V519 V520 V521 V522 V523 V524 V525 V526 V527 V528
## 10 0 0 0 0 0 0 0 48 165 252 173 0 0 0
## V529 V530 V531 V532 V533 V534 V535 V536 V537 V538 V539 V540 V541 V542
## 10 0 0 0 0 0 0 0 0 0 0 0 86 253 225
## V543 V544 V545 V546 V547 V548 V549 V550 V551 V552 V553 V554 V555 V556
## 10 0 0 0 0 0 0 114 238 253 162 0 0 0 0
## V557 V558 V559 V560 V561 V562 V563 V564 V565 V566 V567 V568 V569 V570
## 10 0 0 0 0 0 0 0 0 0 0 0 85 252 249
## V571 V572 V573 V574 V575 V576 V577 V578 V579 V580 V581 V582 V583 V584
## 10 146 48 29 85 178 225 253 223 167 56 0 0 0 0
## V585 V586 V587 V588 V589 V590 V591 V592 V593 V594 V595 V596 V597 V598
## 10 0 0 0 0 0 0 0 0 0 0 0 85 252 252
## V599 V600 V601 V602 V603 V604 V605 V606 V607 V608 V609 V610 V611 V612
## 10 252 229 215 252 252 252 196 130 0 0 0 0 0 0
## V613 V614 V615 V616 V617 V618 V619 V620 V621 V622 V623 V624 V625 V626
## 10 0 0 0 0 0 0 0 0 0 0 0 28 199 252
## V627 V628 V629 V630 V631 V632 V633 V634 V635 V636 V637 V638 V639 V640
## 10 252 253 252 252 233 145 0 0 0 0 0 0 0 0
## V641 V642 V643 V644 V645 V646 V647 V648 V649 V650 V651 V652 V653 V654
## 10 0 0 0 0 0 0 0 0 0 0 0 0 25 128
## V655 V656 V657 V658 V659 V660 V661 V662 V663 V664 V665 V666 V667 V668
## 10 252 253 252 141 37 0 0 0 0 0 0 0 0 0
## V669 V670 V671 V672 V673 V674 V675 V676 V677 V678 V679 V680 V681 V682
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V683 V684 V685 V686 V687 V688 V689 V690 V691 V692 V693 V694 V695 V696
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V697 V698 V699 V700 V701 V702 V703 V704 V705 V706 V707 V708 V709 V710
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V711 V712 V713 V714 V715 V716 V717 V718 V719 V720 V721 V722 V723 V724
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V725 V726 V727 V728 V729 V730 V731 V732 V733 V734 V735 V736 V737 V738
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V739 V740 V741 V742 V743 V744 V745 V746 V747 V748 V749 V750 V751 V752
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V753 V754 V755 V756 V757 V758 V759 V760 V761 V762 V763 V764 V765 V766
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V767 V768 V769 V770 V771 V772 V773 V774 V775 V776 V777 V778 V779 V780
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## V781 V782 V783 V784 V785
## 10 0 0 0 0 0test_mnist=read.csv("mnist_test_10.csv", header=FALSE)Membangun data matrix
m = matrix(unlist(train_mnist[10,-1]),
nrow = 28,
byrow = TRUE)Visualisasi dari data m
image(m,col=grey.colors(255))
Membalikan visualisasi dari data m
rotate = function(x) t(apply(x, 2, rev))
image(rotate(m),col=grey.colors(255))
Visualisasi dari data train_mnist
par(mfrow=c(2,3))
lapply(1:6,
function(x) image(
rotate(matrix(unlist(train_mnist[x,-1]),
nrow = 28,
byrow = TRUE)),
col=grey.colors(255),
xlab=train_mnist[x,1]
)
)
## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## NULL
##
## [[4]]
## NULL
##
## [[5]]
## NULL
##
## [[6]]
## NULLpar(mfrow=c(1,1))
str(train_mnist)## 'data.frame': 100 obs. of 785 variables:
## $ V1 : int 7 8 3 1 4 1 1 4 3 0 ...
## $ V2 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V4 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V5 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V6 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V7 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V8 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V9 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V10 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V11 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V12 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V13 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V14 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V15 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V16 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V17 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V18 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V19 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V20 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V21 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V22 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V23 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V24 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V25 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V26 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V27 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V28 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V29 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V30 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V31 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V32 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V33 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V34 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V35 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V36 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V37 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V38 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V39 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V40 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V41 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V42 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V43 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V44 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V45 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V46 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V47 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V48 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V49 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V50 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V51 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V52 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V53 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V54 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V55 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V56 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V57 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V58 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V59 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V60 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V61 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V62 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V63 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V64 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V65 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V66 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V67 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V68 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V69 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V70 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V71 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V72 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V73 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V74 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V75 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V76 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V77 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V78 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V79 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V80 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V81 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V82 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V83 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V84 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V85 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V86 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V87 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V88 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V89 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V90 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V91 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V92 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V93 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V94 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V95 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V96 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V97 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V98 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ V99 : int 0 0 0 0 0 0 0 0 0 0 ...
## [list output truncated]x=2:785
y=1
table(train_mnist[,y])##
## 0 1 2 3 4 5 6 7 8 9
## 13 14 6 11 11 5 11 10 8 11Membuat Model Deep Learning
model=h2o.deeplearning(x,
y,
as.h2o(train_mnist),
model_id="MNIST_deeplearning",
seed=405,
activation="RectifierWithDropout",
l1=0.00001,
input_dropout_ratio=0.2,
classification_stop = -1,
epochs=2500
)##
|
| | 0%
|
|=================================================================| 100%## Warning in .h2o.startModelJob(algo, params, h2oRestApiVersion): Dropping bad and constant columns: [V256, V254, V253, V252, V119, V118, V117, V116, V479, V481, V480, V368, V367, V366, V365, V122, V364, V121, V363, V120, V706, V705, V704, V703, V702, V701, V700, V707, V393, V392, V391, V396, V395, V394, V139, V138, V140, V144, V143, V142, V141, V728, V727, V726, V725, V724, V723, V729, V172, V171, V170, V731, V730, V2, V3, V4, V5, V6, V7, V8, V9, V282, V281, V280, V169, V168, V167, V166, V284, V508, V507, V749, V506, V505, V504, V503, V509, V753, V752, V751, V750, V199, V198, V197, V196, V618, V617, V616, V615, V734, V733, V732, V619, V10, V12, V11, V14, V621, V13, V620, V16, V15, V18, V17, V19, V649, V648, V769, V647, V768, V646, V767, V645, V644, V21, V20, V23, V22, V25, V533, V775, V24, V532, V774, V27, V531, V773, V26, V772, V29, V771, V28, V770, V759, V758, V757, V756, V755, V754, V30, V32, V31, V34, V33, V36, V643, V35, V642, V38, V762, V37, V761, V760, V39, V309, V308, V307, V424, V41, V40, V43, V42, V45, V44, V47, V676, V46, V312, V675, V49, V311, V674, V48, V310, V673, V672, V671, V670, V537, V779, V536, V778, V535, V777, V534, V776, V50, V52, V51, V54, V53, V56, V55, V58, V423, V57, V422, V785, V421, V784, V59, V420, V783, V782, V781, V780, V449, V448, V61, V60, V63, V62, V65, V64, V67, V66, V69, V335, V698, V68, V453, V452, V451, V450, V559, V677, V70, V71, V79, V565, V564, V200, V563, V562, V561, V560, V109, V108, V107, V228, V227, V226, V589, V81, V80, V83, V591, V82, V590, V85, V84, V87, V86, V89, V88, V115, V478, V114, V477, V113, V476, V112, V475, V111, V110, V593, V592, V339, V338, V337, V336, V699, V90, V92, V91, V94, V93, V95, V225, V588, V224, V587, V340].##
|
| | 0%
|
|= | 1%
|
|= | 2%
|
|== | 3%
|
|== | 4%
|
|=== | 4%
|
|==== | 6%
|
|===== | 7%
|
|===== | 8%
|
|====== | 9%
|
|====== | 10%
|
|======= | 11%
|
|======== | 12%
|
|========= | 14%
|
|========== | 15%
|
|=========== | 16%
|
|=========== | 18%
|
|============ | 18%
|
|============= | 20%
|
|============== | 21%
|
|=============== | 22%
|
|=============== | 24%
|
|================ | 24%
|
|================ | 25%
|
|================= | 26%
|
|================== | 28%
|
|=================== | 30%
|
|==================== | 30%
|
|===================== | 32%
|
|===================== | 33%
|
|====================== | 34%
|
|======================= | 35%
|
|======================= | 36%
|
|======================== | 36%
|
|======================== | 38%
|
|========================= | 39%
|
|=========================== | 41%
|
|============================ | 42%
|
|=================================================================| 100%Data Statistik model
summary(model)## Model Details:
## ==============
##
## H2ORegressionModel: deeplearning
## Model Key: MNIST_deeplearning
## Status of Neuron Layers: predicting V1, regression, gaussian distribution, Quadratic loss, 140.801 weights/biases, 1,7 MB, 107.000 training samples, mini-batch size 1
## layer units type dropout l1 l2 mean_rate
## 1 1 501 Input 20.00 % NA NA NA
## 2 2 200 RectifierDropout 50.00 % 0.000010 0.000000 0.065506
## 3 3 200 RectifierDropout 50.00 % 0.000010 0.000000 0.023710
## 4 4 1 Linear NA 0.000010 0.000000 0.001079
## rate_rms momentum mean_weight weight_rms mean_bias bias_rms
## 1 NA NA NA NA NA NA
## 2 0.065640 0.000000 0.004261 0.054040 0.423469 0.053351
## 3 0.019530 0.000000 -0.018238 0.067772 0.960785 0.053868
## 4 0.000374 0.000000 0.001479 0.039133 -0.063746 0.000000
##
## H2ORegressionMetrics: deeplearning
## ** Reported on training data. **
## ** Metrics reported on full training frame **
##
## MSE: 0.1424873
## RMSE: 0.3774749
## MAE: 0.3111871
## RMSLE: 0.1275395
## Mean Residual Deviance : 0.1424873
##
##
##
##
##
## Scoring History:
## timestamp duration training_speed epochs
## 1 2020-04-02 23:19:37 0.000 sec NA 0.00000
## 2 2020-04-02 23:19:39 8.717 sec 641 obs/sec 10.00000
## 3 2020-04-02 23:19:49 17.717 sec 1185 obs/sec 120.00000
## 4 2020-04-02 23:19:54 23.132 sec 1323 obs/sec 200.00000
## 5 2020-04-02 23:20:00 28.724 sec 1385 obs/sec 280.00000
## 6 2020-04-02 23:20:05 34.079 sec 1429 obs/sec 360.00000
## 7 2020-04-02 23:20:11 39.583 sec 1484 obs/sec 450.00000
## 8 2020-04-02 23:20:16 44.841 sec 1506 obs/sec 530.00000
## 9 2020-04-02 23:20:21 50.085 sec 1546 obs/sec 620.00000
## 10 2020-04-02 23:20:26 55.198 sec 1584 obs/sec 710.00000
## 11 2020-04-02 23:20:31 1 min 0.291 sec 1614 obs/sec 800.00000
## 12 2020-04-02 23:20:37 1 min 5.479 sec 1637 obs/sec 890.00000
## 13 2020-04-02 23:20:42 1 min 10.688 sec 1654 obs/sec 980.00000
## 14 2020-04-02 23:20:47 1 min 15.758 sec 1672 obs/sec 1070.00000
## 15 2020-04-02 23:20:47 1 min 16.157 sec 1671 obs/sec 1070.00000
## iterations samples training_rmse training_deviance training_mae
## 1 0 0.000000 NA NA NA
## 2 1 1000.000000 2.10449 4.42889 1.62811
## 3 12 12000.000000 0.48041 0.23079 0.34755
## 4 20 20000.000000 0.42530 0.18088 0.35386
## 5 28 28000.000000 0.39593 0.15676 0.32210
## 6 36 36000.000000 0.44419 0.19731 0.37665
## 7 45 45000.000000 0.37747 0.14249 0.31119
## 8 53 53000.000000 0.44452 0.19759 0.37912
## 9 62 62000.000000 0.41428 0.17162 0.34445
## 10 71 71000.000000 0.50424 0.25425 0.42524
## 11 80 80000.000000 0.57661 0.33247 0.48326
## 12 89 89000.000000 0.61142 0.37384 0.47504
## 13 98 98000.000000 0.74022 0.54792 0.59955
## 14 107 107000.000000 0.83277 0.69350 0.64620
## 15 107 107000.000000 0.37747 0.14249 0.31119
## training_r2
## 1 NA
## 2 0.50992
## 3 0.97446
## 4 0.97998
## 5 0.98265
## 6 0.97817
## 7 0.98423
## 8 0.97814
## 9 0.98101
## 10 0.97187
## 11 0.96321
## 12 0.95863
## 13 0.93937
## 14 0.92326
## 15 0.98423
##
## Variable Importances: (Extract with `h2o.varimp`)
## =================================================
##
## Variable Importances:
## variable relative_importance scaled_importance percentage
## 1 V265 1.000000 1.000000 0.002680
## 2 V321 0.985851 0.985851 0.002642
## 3 V297 0.969178 0.969178 0.002598
## 4 V410 0.961629 0.961629 0.002578
## 5 V382 0.955033 0.955033 0.002560
##
## ---
## variable relative_importance scaled_importance percentage
## 496 V482 0.599362 0.599362 0.001607
## 497 V425 0.596363 0.596363 0.001598
## 498 V283 0.583113 0.583113 0.001563
## 499 V419 0.575406 0.575406 0.001542
## 500 V538 0.571190 0.571190 0.001531
## 501 V748 0.559620 0.559620 0.001500Prediksi data testing
preds=h2o.predict(model,
as.h2o(test_mnist))##
|
| | 0%
|
|=================================================================| 100%
##
|
| | 0%
|
|=================================================================| 100%head(preds)## predict
## 1 7.155077
## 2 3.686206
## 3 1.294710
## 4 0.557270
## 5 4.313547
## 6 1.204334Performa dari prediksi data testing
perfoma=h2o.performance(model,
as.h2o(test_mnist))##
|
| | 0%
|
|=================================================================| 100%perfoma## H2ORegressionMetrics: deeplearning
##
## MSE: 3.549605
## RMSE: 1.884039
## MAE: 1.195061
## RMSLE: 0.3033348
## Mean Residual Deviance : 3.549605Membuat data untuk hasil prediksi
predictions = cbind(as.data.frame(seq(1,10)),
test_mnist[,1],
as.data.frame(preds[,1]))
names(predictions) = c("Number","Actual","Predicted")
as.matrix(predictions)## Number Actual Predicted
## [1,] 1 7 7.155077
## [2,] 2 3 3.686206
## [3,] 3 1 1.294710
## [4,] 4 0 0.557270
## [5,] 5 6 4.313547
## [6,] 6 1 1.204334
## [7,] 7 6 6.037937
## [8,] 8 9 3.951905
## [9,] 9 5 4.012503
## [10,] 10 9 6.706971