R语言Keras模型结合tf.data.Dataset处理重复列及代码报错解决方案问询

我正尝试在Keras模型中使用tf.data.Dataset来处理包含重复列的数据集（避免重复列导致的问题），但目前卡在了代码运行环节。先从测试代码的问题说起：

测试代码（无法正常运行） #

这段代码旨在让Keras模型在包含3个特征列（c1、c2、c3）和目标列target的数据集上拟合/预测：

library(keras)
library(tfaddons)
library(tfdatasets)
alldata <- data.frame(c1=rnorm(100),c2=rnorm(100),c3=rnorm(100), target=rnorm(100))
alldata_xvar <- c("c1","c2","c3")
write.csv(alldata,file = "alldata.csv", row.names=F)
alldata_spec <- csv_record_spec("alldata.csv")
xfeatures <- alldata_xvar
model <- keras_model_sequential()
model %>% layer_dense(units = length(xfeatures), activation = "relu", input_shape = length(xfeatures)) %>% layer_dense(units = 1)
dataset <- text_line_dataset("alldata.csv", record_spec = alldata_spec)
dataset %>% dataset_prepare(x = xfeatures,y="target")
predicted <- model %>%predict(dataset)

报错信息 #

Error in py_call_impl(callable, dots$args, dots$keywords) : 
  ValueError: in user code:

    File "/.local/share/r-miniconda/envs/r-reticulate/lib/python3.7/site-packages/keras/engine/training.py", line 1621, in predict_function  *
        return step_function(self, iterator)
    File "/.local/share/r-miniconda/envs/r-reticulate/lib/python3.7/site-packages/keras/engine/training.py", line 1611, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/.local/share/r-miniconda/envs/r-reticulate/lib/python3.7/site-packages/keras/engine/training.py", line 1604, in run_step  **
        outputs = model.predict_step(data)
    File "/.local/share/r-miniconda/envs/r-reticulate/lib/python3.7/site-packages/keras/engine/training.py", line 1572, in predict_step
        return self(x, training=False)
    File "/.local/share/r-miniconda/envs/r-reticulate/lib/python3.7/site-packages/keras/utils/traceback_utils.py", line 67, in error_handl

最终需求与完整代码 #

我的实际需求是处理一个包含3个特征列（c1、c2、c3）以及100个全行列值相同的额外列（RegionVar1至RegionVar100）的数据集。由于把这100列存入CSV会导致文件过大，我计划用dataset_map函数为从CSV读取的数据添加这些共用的区域信息，但这段代码也无法正常工作：

library(keras)
library(tfaddons)
library(tfdatasets)
numregion <- 50
numvarregion <- 100
alldata <- data.frame(c1=rnorm(100),c2=rnorm(100),c3=rnorm(100), target=rnorm(100))
alldata_xvar <- c("c1","c2","c3")
region <- matrix( rnorm(50*numvarregion), 50, numvarregion)
regionvar_name1 <- rep("RegionVar",numvarregion)
regionvar_name2 <- seq(1,numvarregion)
regionvar_name <- cbind(regionvar_name1,regionvar_name2)
regionvar_name <- apply(regionvar_name,1,paste,collapse="")
region <- as.data.frame(region)
names(region) <- regionvar_name
region <- array_reshape(as.matrix(region), c(nrow(region), ncol(region)))
write.csv(alldata,file = "alldata.csv", row.names=F)
alldata_spec <- csv_record_spec("alldata.csv")
xfeatures <- c(alldata_xvar,names(region))
model <- keras_model_sequential()
model %>% layer_dense(units = length(xfeatures), activation = "relu", input_shape = length(xfeatures)) %>% layer_dense(units = 1)
regionvar <- 1
dataset <- text_line_dataset("alldata.csv", record_spec = alldata_spec)
dataset <- dataset %>% dataset_map(function(record) {
  record$Region <- region[regionvar,]
  record
})
dataset %>% dataset_prepare(x = xfeatures,y="target")
predicted <- model %>%predict(dataset)

希望能得到让代码正常运行的建议，或是解决重复列问题的其他可行方案。

内容的提问来源于stack exchange，提问作者Fan