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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/05/11 16:19:34 UTC
[GitHub] [incubator-mxnet] scotty3005 opened a new issue #14933: predict
error in MXFeedForwardModel
scotty3005 opened a new issue #14933: predict error in MXFeedForwardModel
URL: https://github.com/apache/incubator-mxnet/issues/14933
## Description
Trying to get predictions for a simple regression model I geet the error posted in the relative section. I am working with R on ArchLinux.
## Environment info (Required)
R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Arch Linux
Matrix products: default
BLAS: /usr/lib/libopenblasp-r0.3.5.so
LAPACK: /usr/lib/liblapack.so.3.8.0
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB.UTF-8 LC_COLLATE=C
[5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
[7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] mxnet_1.5.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.1 plyr_1.8.4 pillar_1.3.1 compiler_3.5.3
[5] RColorBrewer_1.1-2 influenceR_0.1.0 viridis_0.5.1 tools_3.5.3
[9] digest_0.6.18 jsonlite_1.6 viridisLite_0.3.0 tibble_2.1.1
[13] gtable_0.2.0 rgexf_0.15.3 pkgconfig_2.0.2 rlang_0.3.4
[17] igraph_1.2.4.1 rstudioapi_0.10 gridExtra_2.3 downloader_0.4
[21] DiagrammeR_1.0.1 dplyr_0.8.0.1 stringr_1.4.0 htmlwidgets_1.3
[25] hms_0.4.2 grid_3.5.3 tidyselect_0.2.5 glue_1.3.1
[29] R6_2.4.0 Rook_1.1-1 XML_3.98-1.16 readr_1.3.1
[33] purrr_0.3.2 tidyr_0.8.3 ggplot2_3.0.0 magrittr_1.5
[37] codetools_0.2-16 scales_1.0.0 htmltools_0.3.6 assertthat_0.2.1
[41] colorspace_1.3-2 brew_1.0-6 stringi_1.4.3 visNetwork_2.0.6
[45] lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4
## Build info (Required if built from source)
Compiler: gcc
MXNet commit hash:
b22ee951ae45f7d34b9ae79433f318db5b6bc5ac
Build config:
ifndef CC
export CC = gcc
endif
ifndef CXX
export CXX = g++
endif
ifndef NVCC
export NVCC = nvcc
endif
DEV = 0
DEBUG = 0
USE_SIGNAL_HANDLER =
ADD_LDFLAGS =
ADD_CFLAGS =
E_CUDA = 0
USE_CUDA_PATH = NONE
ENABLE_CUDA_RTC = 1
USE_CUDNN = 0
USE_NVTX = 0
USE_NCCL = 0
USE_NCCL_PATH = NONE
USE_OPENCV = 1
USE_OPENCV_INC_PATH = NONE
USE_OPENCV_LIB_PATH = NONE
USE_LIBJPEG_TURBO = 0
USE_LIBJPEG_TURBO_PATH = NONE
USE_OPENMP = 1
USE_MKLDNN =
USE_NNPACK = 0
UNAME_S := $(shell uname -s)
ifeq ($(UNAME_S), Darwin)
USE_BLAS = apple
else
USE_BLAS = atlas
endif
USE_LAPACK = 1
USE_LAPACK_PATH =
USE_INTEL_PATH = NONE
ifeq ($(USE_BLAS), mkl)
USE_STATIC_MKL = 1
else
USE_STATIC_MKL = NONE
endif
ARCH := $(shell uname -a)
ifneq (,$(filter $(ARCH), armv6l armv7l powerpc64le ppc64le aarch64))
USE_SSE=0
USE_F16C=0
else
USE_SSE=1
endif
USE_F16C =
USE_DIST_KVSTORE = 0
USE_HDFS = 0
LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
USE_S3 = 0
USE_OPERATOR_TUNING = 1
USE_GPERFTOOLS = 0
USE_GPERFTOOLS_PATH =
USE_GPERFTOOLS_STATIC =
USE_JEMALLOC = 1
USE_JEMALLOC_PATH =
USE_JEMALLOC_STATIC =
EXTRA_OPERATORS =
USE_CPP_PACKAGE = 0
USE_INT64_TENSOR_SIZE = 0
## Error Message:
Error in symbol$infer.shape(list(...)) :
Error in operator logisticregressionoutput0: [17:10:24] include/mxnet/./tuple.h:202: Check failed: i >= 0 && i < ndim(): index = 0 must be in range [0, -1)
Stack trace:
[bt] (0) /home/filippo/R/x86_64-pc-linux-gnu-library/3.5/mxnet/libs/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x43) [0x7fd298e1d563]
[bt] (1) /home/filippo/R/x86_64-pc-linux-gnu-library/3.5/mxnet/libs/libmxnet.so(mxnet::op::RegressionOpShape(nnvm::NodeAttrs const&, std::vector<mxnet::TShape, std::allocator<mxnet::TShape> >*, std::vector<mxnet::TShape, std::allocator<mxnet::TShape> >*)+0x621) [0x7fd29b2f9b91]
[bt] (2) /home/filippo/R/x86_64-pc-linux-gnu-library/3.5/mxnet/libs/libmxnet.so(+0x24ddd09) [0x7fd29ad21d09]
[bt] (3) /home/filippo/R/x86_64-pc-linux-gnu-library/3.5/mxnet/libs/libmxnet.so(mxnet::exec::InferShape(nnvm::Graph&&, std::vector<mxnet::TShape, std::allocator<mxnet::TShape> >&&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)+0x1677) [0x
## Minimum reproducible example
library(mxnet)
X0 = matrix(rnorm(2000, -10), ncol=2)
X1 = matrix(rnorm(2000, 10), ncol=2)
X = rbind(X0, X1)
Y = c(rep(0, 1000), rep(1, 1000))
ii = sample(1:nrow(X))
X = X[ii,]
Y = Y[ii]
mdl = mx.symbol.Variable('data')
mdl = mx.symbol.FullyConnected(mdl, num_hidden=1)
mdl = mx.symbol.LogisticRegressionOutput(mdl)
mdl = mx.model.FeedForward.create(
mdl,
X = X,
y=Y,
array.batch.size=100,
num.round=100,
#eval.data=list(data = X, label = Y),
eval.metric = mx.metric.logloss,
array.layout = 'rowmajor'
)
predict(mdl, X, array.layout='rowmajor')
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