I am trying to run RBERT in tensorflow on small dataset. I have installed Tensorflow using the miniconda environment. Below is the code which throws the error:
> Sys.setenv(RETICULATE_PYTHON =
> "/Users/applemacbookpro/opt/miniconda3/envs/tensorflowa/bin/python")
>
> #Make virtual environment in anaconda
>
> reticulate::conda_list()[[1]][8] %>%
> reticulate::use_condaenv(required = TRUE)
>
>
> #Load the libraries library(keras) library(tidyverse) library(stringr) library(tidytext) library(caret) library(dplyr) library(tm)
> library(RBERT) library(tensorflow) library(reticulate)
>
> #Install RBERT
>
> devtools::install("/Users/applemacbookpro/Downloads/RBERT")
>
> #Initiate BERT BERT_PRETRAINED_DIR <- RBERT::download_BERT_checkpoint(model = "bert_base_uncased")
>
>
> #Extract tokenized words from agency trainset BERT_feats <- extract_features( examples = agency_trainset$agency, ckpt_dir =
> BERT_PRETRAINED_DIR, layer_indexes = 1:12, )
Error in py_call_impl(callable, dots$args, dots$keywords) : RuntimeError: Evaluation error: ValueError: Tried to convert 'size' to a tensor and failed. Error: Cannot convert a partially known TensorShape to a Tensor: (128, ?).
Traceback:
stop(structure(list(message = "RuntimeError: Evaluation error: ValueError: Tried to convert 'size' to a tensor and failed. Error: Cannot convert a partially known TensorShape to a Tensor: (128, ?).",
call = py_call_impl(callable, dots$args, dots$keywords),
cppstack = structure(list(file = "", line = -1L, stack = c("1 reticulate.so 0x000000010773d3de _ZN4Rcpp9exceptionC2EPKcb + 222",
"2 reticulate.so 0x0000000107746245 _ZN4Rcpp4stopERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE + 53", ...
13.
python_function at call.py#21
12.
fn at <string>#4
11.
_call_model_fn at tpu_estimator.py#1524
10.
call_without_tpu at tpu_estimator.py#1250
9.
_model_fn at tpu_estimator.py#2470
8.
_call_model_fn at estimator.py#1169
7.
_call_model_fn at tpu_estimator.py#2186
6.
predict at estimator.py#551
5.
predict at tpu_estimator.py#2431
4.
raise_errors at error_handling.py#128
3.
predict at tpu_estimator.py#2437
2.
result_iterator$`next`()
1.
extract_features(examples = agency_trainset$agency, ckpt_dir = BERT_PRETRAINED_DIR,
layer_indexes = 1:12, )