deepviz
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andrie
Error in data.frame(...): arguments imply differing number of rows: 2, 0
I tried to reproduce the example in the readme. I encountered this error.
library(reprex)
library(deepviz)
library(tidyverse)
library(keras)
model <- keras_model_sequential() %>%
layer_dense(10, input_shape = 4) %>%
layer_dense(2, activation = "sigmoid")
model %>% plot_model()
#> Error in data.frame(id = 1:n, type = type, label = label, stringsAsFactors = FALSE): arguments imply differing number of rows: 2, 0
Created on 2018-11-14 by the reprex package (v0.2.1)
I'm unable to reproduce this error. I get a different error, though:
Error: Result 1 is not a length 1 atomic vector
This is the same error as reported by #2
Hello @andrie -- I saw the same results as @hermandr. The following code sits in a knitr script.
library(keras)
library(rlang)
devtools::install_github("andrie/deepviz")
library(deepviz)
library(magrittr)
model <- keras_model_sequential() %>%
layer_dense(units = 32, input_shape = c(784)) %>%
layer_activation('relu') %>%
layer_dense(units = 10) %>%
layer_activation('softmax')
model %>% plot_model()
My instance recognizes that deepviz is already properly installed and moves on. Then I get this error:
Error in data.frame(id = 1:n, type = type, label = label, stringsAsFactors = FALSE) :
arguments imply differing number of rows: 2, 0
Object model appears to be properly formed.
Model
__________________________________________________________________________________________________
Layer (type) Output Shape Param #
==================================================================================================
dense_4 (Dense) (None, 32) 25120
__________________________________________________________________________________________________
activation_4 (Activation) (None, 32) 0
__________________________________________________________________________________________________
dense_5 (Dense) (None, 10) 330
__________________________________________________________________________________________________
activation_5 (Activation) (None, 10) 0
==================================================================================================
Total params: 25,450
Trainable params: 25,450
Non-trainable params: 0
__________________________________________________________________________________________________
I tried your code example today, using a fresh install of keras and the underlying keras Python libraries. I could not reproduce.
My hunch is that there was an internal change in the way that keras represents model objects, and a recent version of deepviz accounts for this.
Please reinstall your keras python libraries, as well as the keras package and try again. If the error still persists, please provide your sessioninfo::session_info().
Hello Andrie –
I don’t want you to spend you day debugging my stuff. Here is the story, though.
I updated Conda base. Then I tried to run my code, which gave no untoward indications. When I looked to see what model looked like, I got: <pointer: 0x0>
So I reran the the five lines that define the model and here’s what I got:
Error: lexical error: invalid char in json text.
WARNING: The conda.compat modul
(right here) ------^
And the same error when I tried to execute in R: install_keras(version="default")
So I figured, try updating Conda again. I got the following warning: WARNING: The conda.compat module is deprecated blah blah (which I didn’t get the first time).
Clearly, my problem lies in conda somewhere, not in deepviz. I’ll play around with it.
Here is my session_info anyway.
> sessioninfo::session_info()
- Session info ---------------------------------------------------------------------------------
setting value
version R version 3.5.2 (2018-12-20)
os Windows >= 8 x64
system x86_64, mingw32
ui RStudio
language (EN)
collate English_United States.1252
ctype English_United States.1252
tz America/New_York
date 2019-04-08
- Packages -------------------------------------------------------------------------------------
package * version date lib source
assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.5.3)
backports 1.1.3 2018-12-14 [1] CRAN (R 3.5.2)
base64enc 0.1-3 2015-07-28 [1] CRAN (R 3.5.0)
brew 1.0-6 2011-04-13 [1] CRAN (R 3.5.0)
callr 3.2.0 2019-03-15 [1] CRAN (R 3.5.3)
cli 1.1.0 2019-03-19 [1] CRAN (R 3.5.3)
colorspace 1.4-1 2019-03-18 [1] CRAN (R 3.5.3)
crayon 1.3.4 2017-09-16 [1] CRAN (R 3.3.3)
deepviz * 0.0.1.9000 2019-04-07 [1] Github (andrie/deepviz@2a35de6)
desc 1.2.0 2018-05-01 [1] CRAN (R 3.5.0)
devtools 2.0.1 2018-10-26 [1] CRAN (R 3.5.2)
DiagrammeR 1.0.0 2018-03-01 [1] CRAN (R 3.5.3)
digest 0.6.18 2018-10-10 [1] CRAN (R 3.5.2)
downloader 0.4 2015-07-09 [1] CRAN (R 3.5.3)
dplyr 0.8.0.1 2019-02-15 [1] CRAN (R 3.5.3)
farver 1.1.0 2018-11-20 [1] CRAN (R 3.5.3)
fs 1.2.7 2019-03-19 [1] CRAN (R 3.5.3)
generics 0.0.2 2018-11-29 [1] CRAN (R 3.5.2)
ggforce 0.2.1 2019-03-12 [1] CRAN (R 3.5.3)
ggplot2 3.1.1 2019-04-07 [1] CRAN (R 3.5.2)
ggraph 1.0.2 2018-07-07 [1] CRAN (R 3.5.3)
ggrepel 0.8.0 2018-05-09 [1] CRAN (R 3.5.3)
glue 1.3.1 2019-03-12 [1] CRAN (R 3.5.3)
gridExtra 2.3 2017-09-09 [1] CRAN (R 3.3.3)
gtable 0.3.0 2019-03-25 [1] CRAN (R 3.5.3)
hms 0.4.2 2018-03-10 [1] CRAN (R 3.3.3)
htmltools 0.3.6 2017-04-28 [1] CRAN (R 3.5.0)
htmlwidgets 1.3 2018-09-30 [1] CRAN (R 3.5.2)
igraph 1.2.4 2019-02-13 [1] CRAN (R 3.5.3)
influenceR 0.1.0 2015-09-03 [1] CRAN (R 3.5.3)
jsonlite 1.6 2018-12-07 [1] CRAN (R 3.5.2)
keras * 2.2.4.1 2019-04-05 [1] CRAN (R 3.5.2)
lattice 0.20-38 2018-11-04 [2] CRAN (R 3.5.2)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 3.5.3)
magrittr * 1.5 2014-11-22 [1] CRAN (R 3.3.3)
MASS 7.3-51.3 2019-03-31 [1] CRAN (R 3.5.3)
Matrix 1.2-15 2018-11-01 [2] CRAN (R 3.5.2)
memoise 1.1.0 2017-04-21 [1] CRAN (R 3.5.0)
munsell 0.5.0 2018-06-12 [1] CRAN (R 3.5.0)
pillar 1.3.1 2018-12-15 [1] CRAN (R 3.5.2)
pkgbuild 1.0.3 2019-03-20 [1] CRAN (R 3.5.3)
pkgconfig 2.0.2 2018-08-16 [1] CRAN (R 3.5.2)
pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.5.2)
plyr 1.8.4 2016-06-08 [1] CRAN (R 3.5.0)
polyclip 1.10-0 2019-03-14 [1] CRAN (R 3.5.3)
prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.5.2)
processx 3.3.0 2019-03-10 [1] CRAN (R 3.5.3)
ps 1.3.0 2018-12-21 [1] CRAN (R 3.5.2)
purrr 0.3.2 2019-03-15 [1] CRAN (R 3.5.3)
R6 2.4.0 2019-02-14 [1] CRAN (R 3.5.3)
RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 3.3.2)
Rcpp 1.0.1 2019-03-17 [1] CRAN (R 3.5.3)
readr 1.3.1 2018-12-21 [1] CRAN (R 3.5.2)
remotes 2.0.2 2018-10-30 [1] CRAN (R 3.5.2)
reticulate 1.11.1 2019-03-06 [1] CRAN (R 3.5.3)
rgexf 0.15.3 2015-03-24 [1] CRAN (R 3.5.3)
rlang * 0.3.3 2019-03-29 [1] CRAN (R 3.5.3)
Rook 1.1-1 2014-10-20 [1] CRAN (R 3.5.2)
rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.3.3)
rstudioapi 0.10 2019-03-19 [1] CRAN (R 3.5.3)
scales 1.0.0 2018-08-09 [1] CRAN (R 3.5.2)
sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.5.2)
stringi 1.4.3 2019-03-12 [1] CRAN (R 3.5.3)
stringr 1.4.0 2019-02-10 [1] CRAN (R 3.5.3)
tensorflow 1.13.1 2019-04-05 [1] CRAN (R 3.5.2)
testthat 2.0.1 2018-10-13 [1] CRAN (R 3.5.3)
tfruns 1.4 2018-08-25 [1] CRAN (R 3.5.1)
tibble 2.1.1 2019-03-16 [1] CRAN (R 3.5.3)
tidygraph 1.1.2 2019-02-18 [1] CRAN (R 3.5.3)
tidyr 0.8.3 2019-03-01 [1] CRAN (R 3.5.3)
tidyselect 0.2.5 2018-10-11 [1] CRAN (R 3.5.3)
tweenr 1.0.1 2018-12-14 [1] CRAN (R 3.5.3)
usethis 1.4.0 2018-08-14 [1] CRAN (R 3.5.2)
viridis 0.5.1 2018-03-29 [1] CRAN (R 3.5.0)
viridisLite 0.3.0 2018-02-01 [1] CRAN (R 3.3.3)
visNetwork 2.0.6 2019-03-26 [1] CRAN (R 3.5.3)
whisker 0.3-2 2013-04-28 [1] CRAN (R 3.5.0)
withr 2.1.2 2018-03-15 [1] CRAN (R 3.5.0)
XML 3.98-1.19 2019-03-06 [1] CRAN (R 3.5.2)
yaml 2.2.0 2018-07-25 [1] CRAN (R 3.5.2)
zeallot 0.1.0 2018-01-28 [1] CRAN (R 3.5.1)
[1] F:/repo/R-3.3.3/library
[2] C:/Program Files/R/R-3.5.2/library
Looks like the problem is elsewhere: https://github.com/rstudio/keras/issues/733