Как связывать имена в MXNET mx.io.internal.arrayiter() вывода?

Question

я переписал пример доступна здесь (ссылка на данные в документе) https://github.com/dmlc/mxnet/blob/master/example/recommenders/demo1-MF.ipynb

library(mxnet) 


DF <- read.delim("./data/ml-100k/u.data", header=F) 
names(DF) <- c("user", "item", "score", "time") 

max_user<- max(DF$user) 
max_item <- max(DF$item) 

DF_mat_x <- data.matrix(t(sapply(DF[,1:2], as.numeric))) 
DF_y <- DF[,3] 

mx.io.arrayiter(DF_mat_x, t(data.matrix(data.frame(score=DF_y)))) 


k <- 64 


## model 
user <- mx.symbol.Variable("user") 
item <- mx.symbol.Variable("item") 
score <- mx.symbol.Variable("label") 
## user feature lookup 
user1 <- mx.symbol.Embedding(data=user, input_dim = max_user, output_dim = k, name="user1") 
## item feature lookup 
item1 <- mx.symbol.Embedding(data=item, input_dim = max_item, output_dim = k, name="item1") 
## predict by the inner product, which is elementwise product and then sum 
pred <- user1 * item1 
pred1 <- mx.symbol.sum_axis(pred, axis = 1, name="pred1") 
pred2 <- mx.symbol.Flatten(pred1, name="pred2") 
## loss layer 
pred3 <- mx.symbol.LinearRegressionOutput(data=pred2, label=score, name="pred3") 


devices <- mx.cpu() 
mx.set.seed(123) 


mx.model.FeedForward.create(pred3, X = mx.io.arrayiter(data=DF_mat_x, label=t(data.matrix(data.frame(score=DF_y)))), 
          ctx=devices, num.round=10, array.batch.size=10, 
          verbose=T, #array.layout="rowmajor", 
          initializer=mx.init.uniform(0.07), learning.rate=0.07, 
          eval.metric=mx.metric.rmse, momentum = 0.9, 
          epoch.end.callback=mx.callback.log.train.metric(1)) 

Получить ошибки

[19:28:45] /root/mxnet/dmlc-core/include/dmlc/logging.h:235: [19:28:45] src/symbol/symbol.cc:155: Symbol.InferShapeKeyword argument name data not found. 
Candidate arguments: 
    [0]user 
    [1]user1_weight 
    [2]item 
    [3]item1_weight 
    [4]label 

Error in symbol$infer.shape(list(...)) : basic_string::resize 
> traceback() 
6: stop(list(message = "basic_string::resize", call = symbol$infer.shape(list(...)), 
     cppstack = NULL)) 
5: .External(list(name = "CppMethod__invoke_notvoid", address = <pointer: 0x11caef0>, 
     dll = list(name = "Rcpp", path = "/usr/local/lib/R/site-library/Rcpp/libs/Rcpp.so", 
      dynamicLookup = TRUE, handle = <pointer: 0x1404520>, 
      info = <pointer: 0x7f348a598d80>), numParameters = -1L), 
     <pointer: 0x3835820>, <pointer: 0x3836540>, .pointer, ...) 
4: symbol$infer.shape(list(...)) 
3: mx.symbol.infer.shape(symbol, data = input.shape) 
2: mx.model.init.params(symbol, input.shape, initializer, mx.cpu()) 
1: mx.model.FeedForward.create(pred3, X = mx.io.arrayiter(data = DF_mat_x, 
     label = t(data.matrix(data.frame(score = DF_y)))), ctx = devices, 
     num.round = 10, array.batch.size = 10, verbose = T, initializer = mx.init.uniform(0.07), 
     learning.rate = 0.07, eval.metric = mx.metric.rmse, momentum = 0.9, 
     epoch.end.callback = mx.callback.log.train.metric(1)) 

Вручную я получаю данные один этикетки

> X <- mx.io.arrayiter(data=DF_mat_x, label=t(data.matrix(data.frame(score=DF_y)))) 
> Z <- mxnet:::mx.model.init.iter(X, NULL, batch.size = 120, is.train = TRUE) 
> Z$iter.next() 
[1] TRUE 
> Z$value() 
$data 
    [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] 
[1,] 196 186 22 244 166 298 115 253 305  6 62 286 200 210 
[2,] 242 302 377 51 346 474 265 465 451 86 257 1014 222 40 
    [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] 
[1,] 224 303 122 194 291 234 119 167 299 291 308 95 
[2,] 29 785 387 274 1042 1184 392 486 144 118  1 546 
    [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] 
[1,] 38 102 63 160 50 301 225 290 97 157 181 278 
[2,] 95 768 277 234 246 98 193 88 194 274 1081 603 
    [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] 
[1,] 276  7 10 284 201 276 287 246 242 249 99 178 
[2,] 796 32 16 304 979 564 327 201 1137 241  4 332 
    [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62] 
[1,] 251 81 260 25 59 72 87 290 42 292 115 20 
[2,] 100 432 322 181 196 679 384 143 423 515 20 288 
    [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74] 
[1,] 201 13 246 138 167 60 57 223 189 243 92 246 
[2,] 219 526 919 26 232 427 304 274 512 15 1049 416 
    [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86] 
[1,] 194 241 178 254 293 127 225 299 225 276 291 222 
[2,] 165 690 248 1444  5 229 237 229 480 54 144 366 
    [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98] 
[1,] 267 42 11 95  8 162 87 279 145 119 62 62 
[2,] 518 403 111 625 338 25 1016 154 275 1153 498 382 
    [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108] 
[1,] 28 135  32  90 286 293 216 166 250 271 
[2,] 209  23 294 382 208 685 144 328 496 132 
    [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118] 
[1,] 160 265 198  42 168 110  58  90 271  62 
[2,] 174 118 498  96 151 307 144 648 346  21 
    [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128] 
[1,] 279 237  94 128 298  44 264 194  72 222 
[2,] 832 514 789 485 317 195 200 385 195 750 

$label 
    [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]  [,14] 
    [1,] 3 3 1 2 1 4 2 5 3  3  2  5  5  3 
    [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] 
[1,]  3  3  5  2  4  2  4  4  4  2  4  2 
    [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] 
[1,]  5  2  4  5  3  4  4  4  3  4  1  5 
    [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] 
[1,]  1  4  4  4  2  3  5  5  5  5  5  3 
    [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62] 
[1,]  4  2  4  5  5  2  4  5  5  4  3  1 
    [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74] 
[1,]  4  3  4  5  1  5  5  4  4  3  1  3 
    [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86] 
[1,]  4  2  4  3  3  5  5  3  5  3  5  4 
    [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98] 
[1,]  5  3  4  4  4  4  4  5  2  5  4  3 
    [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108] 
[1,]  4  4  3  5  4  3  4  5  4  5 
    [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118] 
[1,]  5  4  3  5  5  4  4  4  4  3 
    [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126] [,127] [,128] 
[1,]  3  4  4  3  4  5  5  2  5  5 

И модель не см. данные

Оригинальный пример е использует

user = mx.nd.array(user) 
    item = mx.nd.array(item) 
    score = mx.nd.array(score) 
    return mx.io.NDArrayIter(data={'user':user,'item':item},label={'score':score}, 
          batch_size=batch_size, shuffle=True) 

Как сделать это правильно в R?

UPDATE

Новая сеть

data <- mx.symbol.Variable("data") 
user <- mx.symbol.transpose(mx.symbol.slice_axis(data=data, axis=0, begin=1, end=2, name="user")) 
item <- mx.symbol.transpose(mx.symbol.slice_axis(data=data, axis=0, begin=0, end=1, name="item")) 
score <- mx.symbol.Variable("label") 
## user feature lookup 
user1 <- mx.symbol.Embedding(data=user, input_dim = max_user, output_dim = k, name="user1") 
## item feature lookup 
item1 <- mx.symbol.Embedding(data=item, input_dim = max_item, output_dim = k, name="item1") 
## predict by the inner product, which is elementwise product and then sum 
pred <- user1 * item1 
pred1 <- mx.symbol.sum_axis(pred, axis = 1, name="pred1") 
pred2 <- mx.symbol.Flatten(pred1, name="pred2") 
## loss layer 
pred3 <- mx.symbol.LinearRegressionOutput(data=pred2, label=score, name="pred3") 


X.train.iter <- mx.io.arrayiter(data=DF_mat_x, label=t(data.matrix(data.frame(score=DF_y)))) 

mx.model.FeedForward.create(pred3, X = X.train.iter , 
          ctx=devices, num.round=10, array.batch.size=10, 
          verbose=T, #array.layout="colmajor", 
          initializer=mx.init.uniform(0.07), learning.rate=0.07, 
          eval.metric=mx.metric.rmse, momentum = 0.9, 
          epoch.end.callback=mx.callback.log.train.metric(1)) 

Ошибка создается

Start training with 1 devices 
[22:43:54] /root/mxnet/dmlc-core/include/dmlc/logging.h:235: [22:43:54] src/ndarray/ndarray.cc:231: Check failed: from.shape() == to->shape() operands shape mismatch 
Error in exec$update.arg.arrays(arg.arrays, match.name, skip.null) : 
    basic_string::resize 
traceback() 
6: stop(list(message = "basic_string::resize", call = exec$update.arg.arrays(arg.arrays, 
     match.name, skip.null), cppstack = NULL)) 
5: .External(list(name = "CppMethod__invoke_void", address = <pointer: 0x18be2c0>, 
     dll = list(name = "Rcpp", path = "/usr/local/lib/R/site-library/Rcpp/libs/Rcpp.so", 
      dynamicLookup = TRUE, handle = <pointer: 0x2a27840>, 
      info = <pointer: 0x7f021bba2d80>), numParameters = -1L), 
    <pointer: 0x3c71d10>, <pointer: 0x3c61310>, .pointer, ...) 
4: exec$update.arg.arrays(arg.arrays, match.name, skip.null) 
3: mx.exec.update.arg.arrays(train.execs[[i]], s, match.name = TRUE) 
2: mx.model.train(symbol, ctx, input.shape, params$arg.params, params$aux.params, 
     begin.round, num.round, optimizer = optimizer, train.data = X, 
     eval.data = eval.data, metric = eval.metric, epoch.end.callback = epoch.end.callback, 
     batch.end.callback = batch.end.callback, kvstore = kvstore, 
    verbose = verbose) 
1: mx.model.FeedForward.create(pred3, X = X.train.iter, ctx = devices, 
     num.round = 10, array.batch.size = 10, verbose = T, initializer = mx.init.uniform(0.07), 
     learning.rate = 0.07, eval.metric = mx.metric.rmse, momentum = 0.9, 
     epoch.end.callback = mx.callback.log.train.metric(1)) 

модель, но не работает.

Answer 1

Ошибка

src/symbol/symbol.cc:155: Symbol.InferShapeKeyword argument name data not found. Candidate arguments: 
[0]user 
[1]user1_weight 
[2]item 
[3]item1_weight 
[4]label 

показывает, что умозаключение форма символа в коде R пытается ссылаться на переменную «данные», но только переменные, перечисленные выше (которые вы использовали в вашей сети) доступны. См. Эту строку: https://github.com/dmlc/mxnet/blob/master/R-package/R/model.R#L264

В примере на Python используется подклассовый итератор, поэтому переменные символа можно назвать чем угодно, не приводя к полученной ошибке.

Я отправил новый вопрос, чтобы изменить это здесь: https://github.com/dmlc/mxnet/issues/4462