thirdwing
commented
7 years ago Please provide a reproducible example. We don't know what df2 is.
Closed kaushalx closed 6 years ago
thirdwing
commented
7 years ago Please provide a reproducible example. We don't know what df2 is.
anirudhacharya
commented
6 years ago @sandeep-krishnamurthy please close this issue as the OP has been inactive for more than a year, and there is no reproducible example for this.
Hi, I am trying to implement CNN in retail data to extract feature and item demand prediction. Data: has columns Price, Sale_flat,page, item size, Category, item id, Quantity( target var)..etc dim(train_mat) [1] 3184 104 dim(train.y) [1] 3184 1
For bugs or installation issues, please provide the following information. The more information you provide, the more likely people will be able to help you.
Environment info
Operating System: Window
Compiler: Rstudio
Package used (Python/R/Scala/Julia): R
library(caret) library(glmnet) library(maxnet) library(mxnet) library(ggplot2) library(dummies) library(clusterSim)
MXNet version:
Or if installed from source:
MXNet commit hash (
git rev-parse HEAD):If you are using python package, please provide
Python version and distribution:
If you are using R package, please provide
R
sessionInfo():R version 3.4.0 (2017-04-21) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows >= 8 x64 (build 9200)Matrix products: default
locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages: [1] reshape_0.8.6 clusterSim_0.45-2 MASS_7.3-47 cluster_2.0.6 maxnet_0.1.2
[6] glmnet_2.0-10 foreach_1.4.3 Matrix_1.2-9 caret_6.0-76 dummies_1.5.6
[11] mxnet_0.10.1 ggplot2_2.2.1 lattice_0.20-35
loaded via a namespace (and not attached): [1] viridis_0.4.0 jsonlite_1.5 viridisLite_0.2.0 splines_3.4.0 modeest_2.1
[6] shiny_1.0.3 assertthat_0.2.0 stats4_3.4.0 quantreg_5.33 glue_1.1.1
[11] digest_0.6.12 RColorBrewer_1.1-2 R2HTML_2.3.2 minqa_1.2.4 colorspace_1.3-2
[16] htmltools_0.3.6 httpuv_1.3.5 plyr_1.8.4 XML_3.98-1.9 pkgconfig_2.0.1
[21] SparseM_1.77 DiagrammeR_0.9.0 xtable_1.8-2 scales_0.4.1 brew_1.0-6
[26] lme4_1.1-13 MatrixModels_0.4-1 tibble_1.3.3 mgcv_1.8-17 car_2.1-4
[31] influenceR_0.1.0 nnet_7.3-12 lazyeval_0.2.0 pbkrtest_0.4-7 rgexf_0.15.3
[36] mime_0.5 magrittr_1.5 nlme_3.1-131 class_7.3-14 Rook_1.1-1
[41] tools_3.4.0 stringr_1.2.0 munsell_0.4.3 bindrcpp_0.2 ade4_1.7-6
[46] compiler_3.4.0 e1071_1.6-8 rlang_0.1.1 grid_3.4.0 nloptr_1.0.4
[51] iterators_1.0.8 rstudioapi_0.6 htmlwidgets_0.8 visNetwork_2.0.0 igraph_1.0.1
[56] gtable_0.2.0 ModelMetrics_1.1.0 codetools_0.2-15 reshape2_1.4.2 R6_2.2.2
[61] gridExtra_2.2.1 knitr_1.16 dplyr_0.7.1 bindr_0.1 stringi_1.1.5
[66] parallel_3.4.0 Rcpp_0.12.11 rgl_0.98.1
Error Message: Error in mx.io.internal.arrayiter(as.array(data), as.array(label), unif.rnds, :
io.cc:54: Data and label shape in-consistent
Please paste the full error message, including stack trace. Error in mx.io.internal.arrayiter(as.array(data), as.array(label), unif.rnds, : io.cc:54: Data and label shape in-consistent
Error in mx.io.internal.arrayiter(as.array(data), as.array(label), unif.rnds, : io.cc:54: Data and label shape in-consistent 6: stop(list(message = "io.cc:54: Data and label shape in-consistent\n", call = mx.io.internal.arrayiter(as.array(data), as.array(label), unif.rnds, batch.size, shuffle), cppstack = list(file = "", line = -1L, stack = "C++ stack not available on this system"))) 5: .External(list(name = "InternalFunction_invoke", address = <pointer: 0x000000000c6d9ae0>, dll = list(name = "Rcpp", path = "C:/Users/Sanjeev/Documents/R/win-library/3.4/Rcpp/libs/x64/Rcpp.dll", dynamicLookup = TRUE, handle = <pointer: 0x000000006abc0000>, info = <pointer: 0x00000000026553b0>), numParameters = -1L), <pointer: 0x0000000002c86790>, ...) 4: mx.io.internal.arrayiter(as.array(data), as.array(label), unif.rnds, batch.size, shuffle) 3: mx.io.arrayiter(X, y, batch.size = batch.size, shuffle = is.train) 2: mx.model.init.iter(X, y, batch.size = array.batch.size, is.train = TRUE) 1: mx.model.FeedForward.create(symbol = output, X = as.matrix(train_mat), y = train.y, ctx = mx.cpu(), num.round = 100, optimizer = "sgd", array.batch.size = 50, learning.rate = 0.01, momentum = 0.9, eval.metric = mx.metric.rmse, array.layout = "rowmajor", verbose = TRUE)## Minimum reproducible example if you are using your own code, please provide a short script that reproduces the error. train_ind <- sample(1:nrow(df2), round(.75*(nrow(df2))))
train.x <- as.matrix(df2[train_ind,!(names(df2)%in% Noinputcolname)])
Normlizing training data
train.x <-data.Normalization(train.x,type = "n4")
test.x <- as.matrix(df2[-train_ind,!(names(df2)%in% Noinputcolname)])
Normalizing test data
test.x <- data.Normalization(test.x,type = "n4")
train.y <- as.matrix(df2[train_ind,target])
Normlizing train target/label data
train.y <-data.Normalization(train.y,type = "n4")
test.y <- as.numeric(df2[-train_ind,target])
Normlizing test target data
test.y <- data.Normalization(test.y,type="n4")
######## Defining dim arrary for input to CNN model
library(reshape2)
library(reshape) train_mat <- train.x
dim(train_mat) <- c(1,104,1,nrow(train.x))
dim(train_mat) <- c(104,nrow(train.x))
dim(test.array)<- c(1,104,1,nrow(test.x))
mx.set.seed(123)
model <- mx.model.FeedForward.create(symbol = output, X = as.matrix(train_mat), y = train.y, ctx = mx.cpu(), num.round = 100, optimizer = "sgd", array.batch.size = 50, learning.rate = 0.01, momentum = 0.9, eval.metric = mx.metric.rmse, array.layout = "rowmajor", verbose = TRUE )
Steps to reproduce
or if you are running standard examples, please provide the commands you have run that lead to the error.
1. 2. 3.
What have you tried to solve it?