Hi,application_mobilenet to create a personal model that I have retrained. After that, I saved the model with save_model_hdf5. But when I try to use the model again with load_model_hdf5, I've got this problem reading the model: Error in py_call_impl(callable, dots$args, dots$keywords) : ValueError: Unknown activation function:relu6
I found in this Keras Github Issue that when you want to reuse a personal mobilenet model you need to specify the Custom Objects to be loaded: how can I specify this custom objetcs in R?
Andrea
October 1, 2018, 3:18pm
2
Hi!
Can you please create a reprex , so that we can reproduce your error? Thanks!
Hi,
library(keras)
input_tensor <- layer_input(shape = list(224, 224, 3), name = "input_tensor")
conv_base <- application_mobilenet(
weights = "imagenet",
include_top = FALSE,
input_shape = c(224, 224, 3)
)
output_tensor <- input_tensor %>%
conv_base() %>%
layer_global_average_pooling_2d() %>%
layer_dense(units = 1024, name = "fc1") %>%
layer_activation(activation = "relu") %>%
layer_batch_normalization() %>%
layer_dense(units = 2, activation = "softmax", name = "fc4")
model <- keras_model(input_tensor, output_tensor)
save_model_hdf5(object = model, filepath = "model.h5")
modelo <- load_model_hdf5(filepath = "model.h5")
My environment is:
> devtools::session_info()
Session info ----------------------------------------------------------------------------------------------------------------------
setting value
version R version 3.4.4 (2018-03-15)
system x86_64, mingw32
ui RStudio (1.2.933)
language (EN)
collate Spanish_Spain.1252
tz Europe/Berlin
date 2018-10-02
Packages --------------------------------------------------------------------------------------------------------------------------
package * version date source
assertthat 0.2.0 2017-04-11 CRAN (R 3.4.4)
backports 1.1.2 2017-12-13 CRAN (R 3.4.3)
base * 3.4.4 2018-03-15 local
base64enc 0.1-3 2015-07-28 CRAN (R 3.4.1)
bindr 0.1.1 2018-03-13 CRAN (R 3.4.4)
bindrcpp 0.2.2 2018-03-29 CRAN (R 3.4.4)
compiler 3.4.4 2018-03-15 local
datasets * 3.4.4 2018-03-15 local
devtools 1.13.6 2018-06-27 CRAN (R 3.4.4)
digest 0.6.15 2018-01-28 CRAN (R 3.4.3)
dplyr 0.7.4 2017-09-28 CRAN (R 3.4.3)
glue 1.3.0 2018-07-17 CRAN (R 3.4.4)
graphics * 3.4.4 2018-03-15 local
grDevices * 3.4.4 2018-03-15 local
grid 3.4.4 2018-03-15 local
here 0.1 2017-05-28 CRAN (R 3.4.4)
jsonlite 1.5 2017-06-01 CRAN (R 3.4.4)
keras * 2.2.0 2018-08-24 CRAN (R 3.4.4)
knitr 1.20 2018-02-20 CRAN (R 3.4.4)
lattice 0.20-35 2017-03-25 CRAN (R 3.4.4)
magrittr 1.5 2014-11-22 CRAN (R 3.4.4)
Matrix 1.2-12 2017-11-30 CRAN (R 3.4.4)
memoise 1.1.0 2017-04-21 CRAN (R 3.4.4)
methods * 3.4.4 2018-03-15 local
packrat 0.4.9-2 2018-04-20 CRAN (R 3.4.4)
pillar 1.2.1 2018-02-27 CRAN (R 3.4.4)
pkgconfig 2.0.1 2017-03-21 CRAN (R 3.4.4)
R6 2.2.2 2017-06-17 CRAN (R 3.4.4)
Rcpp 0.12.18 2018-07-23 CRAN (R 3.4.4)
reticulate 1.10 2018-08-05 CRAN (R 3.4.4)
rlang 0.2.2 2018-08-16 CRAN (R 3.4.4)
rprojroot 1.3-2 2018-01-03 CRAN (R 3.4.4)
stats * 3.4.4 2018-03-15 local
tensorflow 1.9 2018-08-07 CRAN (R 3.4.4)
tfruns 1.3 2018-05-24 Github (rstudio/tfruns@03fb652)
tibble 1.4.2 2018-01-22 CRAN (R 3.4.4)
tools 3.4.4 2018-03-15 local
utils * 3.4.4 2018-03-15 local
whisker 0.3-2 2013-04-28 CRAN (R 3.4.4)
withr 2.1.2 2018-03-15 CRAN (R 3.4.4)
zeallot 0.1.0 2018-01-28 CRAN (R 3.4.4)
1 Like
Andrea
October 2, 2018, 10:13am
4
Thanks! It would be better to use the reprex package, so that
you can be sure all and only the packages which are strictly necessary for the MCVE to run are included
we can also see the output and actual error.
But this is already a big help. Thanks again.
Andrea
October 2, 2018, 11:03pm
5
I ran your MCVE and I got no errors or warnings:
library(keras)
input_tensor <- layer_input(shape = list(224, 224, 3), name = "input_tensor")
conv_base <- application_mobilenet(
weights = "imagenet",
include_top = FALSE,
input_shape = c(224, 224, 3)
)
output_tensor <- input_tensor %>%
conv_base() %>%
layer_global_average_pooling_2d() %>%
layer_dense(units = 1024, name = "fc1") %>%
layer_activation(activation = "relu") %>%
layer_batch_normalization() %>%
layer_dense(units = 2, activation = "softmax", name = "fc4")
model <- keras_model(input_tensor, output_tensor)
save_model_hdf5(object = model, filepath = "model.h5")
modelo <- load_model_hdf5(filepath = "model.h5")
Created on 2018-10-02 by the reprex package (v0.2.1)
Session info
devtools::session_info()
#> Warning: running command 'timedatectl' had status 1
#> Session info -------------------------------------------------------------
#> setting value
#> version R version 3.4.4 (2018-03-15)
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate C.UTF-8
#> tz Etc/UTC
#> date 2018-10-02
#> Packages -----------------------------------------------------------------
#> package * version date source
#> backports 1.1.2 2017-12-13 RSPM (R 3.4.4)
#> base * 3.4.4 2018-07-08 local
#> base64enc 0.1-3 2015-07-28 RSPM (R 3.4.4)
#> compiler 3.4.4 2018-07-08 local
#> datasets * 3.4.4 2018-07-08 local
#> devtools 1.13.6 2018-06-27 RSPM (R 3.4.4)
#> digest 0.6.17 2018-09-12 RSPM (R 3.4.4)
#> evaluate 0.11 2018-07-17 RSPM (R 3.4.4)
#> graphics * 3.4.4 2018-07-08 local
#> grDevices * 3.4.4 2018-07-08 local
#> grid 3.4.4 2018-07-08 local
#> htmltools 0.3.6 2017-04-28 RSPM (R 3.4.4)
#> jsonlite 1.5 2017-06-01 RSPM (R 3.4.4)
#> keras * 2.2.0 2018-08-24 RSPM (R 3.4.4)
#> knitr 1.20 2018-02-20 RSPM (R 3.4.4)
#> lattice 0.20-35 2017-03-25 CRAN (R 3.4.4)
#> magrittr 1.5 2014-11-22 RSPM (R 3.4.4)
#> Matrix 1.2-12 2017-11-30 CRAN (R 3.4.4)
#> memoise 1.1.0 2017-04-21 RSPM (R 3.4.4)
#> methods * 3.4.4 2018-07-08 local
#> R6 2.2.2 2017-06-17 RSPM (R 3.4.4)
#> Rcpp 0.12.19 2018-10-01 CRAN (R 3.4.4)
#> reticulate 1.10 2018-08-05 RSPM (R 3.4.4)
#> rmarkdown 1.10 2018-06-11 RSPM (R 3.4.4)
#> rprojroot 1.3-2 2018-01-03 RSPM (R 3.4.4)
#> stats * 3.4.4 2018-07-08 local
#> stringi 1.2.4 2018-07-20 RSPM (R 3.4.4)
#> stringr 1.3.1 2018-05-10 RSPM (R 3.4.4)
#> tensorflow 1.9 2018-08-07 RSPM (R 3.4.4)
#> tfruns 1.4 2018-08-25 RSPM (R 3.4.4)
#> tools 3.4.4 2018-07-08 local
#> utils * 3.4.4 2018-07-08 local
#> whisker 0.3-2 2013-04-28 RSPM (R 3.4.4)
#> withr 2.1.2 2018-03-15 RSPM (R 3.4.4)
#> yaml 2.2.0 2018-07-25 RSPM (R 3.4.4)
#> zeallot 0.1.0 2018-01-28 RSPM (R 3.4.4)
Let's see:
If you delete the model.h5 file and run your code again, do you still get the same error?
Did you use some "esoteric" setup when configuring the Tensorflow backend with the install_keras() function?
1 Like