After installing the keras and tensorflow packages in centos 7 Linux, R has crashed while loading keras dataset, and I fixed the problem as described below.
> library(keras)
> install_keras()
> dataset=dataset_boston_housing()
/root/.virtualenvs/r-tensorflow/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
*** caught illegal operation ***
address 0x7f016fe93fd0, cause 'illegal operand'
Traceback:
1: .Call(`_reticulate_py_module_import`, module, convert)
2: py_module_import(module, convert = convert)
3: import(module)
4: doTryCatch(return(expr), name, parentenv, handler)
5: tryCatchOne(expr, names, parentenv, handlers[[1L]])
6: tryCatchList(expr, classes, parentenv, handlers)
7: tryCatch(import(module), error = clear_error_handler())
8: py_resolve_module_proxy(x)
9: `$.python.builtin.module`(keras, datasets)
10: keras$datasets
11: dataset_boston_housing()
Possible actions:
1: abort (with core dump, if enabled)
2: normal R exit
3: exit R without saving workspace
4: exit R saving workspace
Selection:
After searching the problem, I found below command to fix it.
> install_keras(tensorflow = "1.5")
Using existing virtualenv at ~/.virtualenvs/r-tensorflow
Upgrading pip ...
.....
After the installation has finished, I can load the dataset.
> library(keras)> dataset=dataset_boston_housing()
/root/.virtualenvs/r-tensorflow/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.> head(dataset)
$train
$train$x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
[1,] 1.23247 0.0 8.14 0 0.5380 6.142 91.7 3.9769 4 307 21.0
[2,] 0.02177 82.5 2.03 0 0.4150 7.610 15.7 6.2700 2 348 14.7
[3,] 4.89822 0.0 18.10 0 0.6310 4.970 100.0 1.3325 24 666 20.2
[4,] 0.03961 0.0 5.19 0 0.5150 6.037 34.5 5.9853 5 224 20.2
[5,] 3.69311 0.0 18.10 0 0.7130 6.376 88.4 2.5671 24 666 20.2
.....
That is it! Thanks for reading!
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