我正在尝试通过 Keras 包(和 tensorflow )实现多层感知器来运行快速 MLP。我想使用贝叶斯优化来训练算法的超参数。不过，我收到一条错误消息，显示“ValueError:速率既不是标量也不是标量张量”，然后它打印来自 keras 的 dropout 参数的随机值。然后，我还从插入符号中收到错误“重新采样的性能度量中缺少值”。我可以让该过程适用于非插入符/keras 算法。
这是我应用于 iris 数据集的代码，它应该重现错误:
library(caret)
library(rBayesianOptimization) # to create cv folds and for bayesian optimisation
library(mlrMBO) # for bayesian optimisation
library(tensorflow)
library(keras)
iris$Speciesset=as.factor(iris$Species=="setosa")
levels(iris$Speciesset) = c("nonset","set")
rounds=5
#tunning via bays search
ctrl = trainControl(method = "cv", number = rounds,
summaryFunction = twoClassSummary,
classProbs = TRUE, search= "grid",
verboseIter=FALSE,savePredictions = "all")
# objective function: we want to maximise the log likelihood by tuning MLP parameters
obj.fun <- smoof::makeSingleObjectiveFunction(
name = "mlp_cv_bayes",
fn = function(x){
train_model = caret::train(Speciesset~.,
data=iris, trControl=ctrl,
metric="ROC", method="mlpKerasDropout",
tuneGrid= expand.grid(
size = x["size"],
dropout = x["dropout"],
batch_size = x["batch_size"],
lr = x["lr"],
rho = x["rho"],
activation = x["activation"],
decay = x["decay"]
))
train_model$results$ROC
},
par.set = makeParamSet(
makeIntegerParam("size", lower= 10, upper = 500),
makeNumericParam("dropout", lower= 0.1, upper = .9),
makeIntegerParam("batch_size", lower= 2000, upper = 15000),
makeNumericParam("lr", lower= 0.01, upper = .9),
makeNumericParam("rho", lower= 0.01, upper = .9),
makeNumericParam("decay", lower= 0.00001, upper = .9),
makeDiscreteParam("activation", values = c("relu", "tanh", "sigmoid"))
),
minimize = FALSE
)
control = makeMBOControl()
control = setMBOControlTermination(control, iters = 20)
des = generateDesign(par.set = getParamSet(obj.funnb),
fun = lhs::randomLHS)
run = mbo(fun = obj.fun,
control = control,
show.info = TRUE, design = des)
最佳答案
这只是一个语法问题
以下内容对我有用:
library(caret)
library(rBayesianOptimization) #I don't think this is needed for the example
library(mlrMBO)
library(tensorflow)
library(keras)
iris$Speciesset=as.factor(iris$Species=="setosa")
levels(iris$Speciesset) = c("nonset","set")
ctrl <- trainControl(method = "cv",
number = 5,
summaryFunction = twoClassSummary,
classProbs = TRUE,
search = "grid",
verboseIter = FALSE,
savePredictions = "all")
obj.fun <- smoof::makeSingleObjectiveFunction(
name = "mlp_cv_bayes",
has.simple.signature = FALSE,
fn = function(x){
train_model = caret::train(Speciesset~.,
data = iris,
trControl = ctrl,
metric =" ROC",
method = "mlpKerasDropout",
tuneGrid = expand.grid(
size = x$size,
batch_size = x$batch_size,
dropout = x$dropout,
lr = x$lr,
rho = x$rho,
activation = x$activation,
decay = x$decay
),
preProcess = c("center", "scale"),
epochs = 10)
train_model$results$ROC
},
par.set = makeParamSet(
makeIntegerParam("size", lower = 8, upper = 32),
makeNumericParam("dropout", lower = 0.5, upper = .9),
makeIntegerParam("batch_size", lower= 32, upper = 64),
makeNumericParam("lr", lower = 0.01, upper = .9),
makeNumericParam("rho", lower = 0.01, upper = .9),
makeNumericParam("decay", lower = 0.00001, upper = .9),
makeDiscreteParam("activation", values = c("relu", "tanh", "sigmoid"))
),
minimize = FALSE
)
control <- makeMBOControl()
control <- setMBOControlTermination(control, iters = 20)
des <- generateDesign(par.set = getParamSet(obj.fun),
fun = lhs::randomLHS)
run <- mbo(fun = obj.fun,
control = control,
show.info = TRUE,
design = des)
即使在 GPU 上也需要一些时间
run
#output
Recommended parameters:
size=14; dropout=0.675; batch_size=58; lr=0.295; rho=0.776; decay=0.613; activation=tanh
Objective: y = 1.000
Optimization path
10 + 20 entries in total, displaying last 10 (or less):
size dropout batch_size lr rho decay activation y dob eol error.message exec.time cb error.model train.time prop.type propose.time se mean
21 17 0.8112161 55 0.2455501 0.1980725 0.84595374 relu 1 11 NA <NA> 22.84 -1 <NA> 0.02 infill_cb 0.89 0 1
22 29 0.8613471 55 0.8087169 0.1619325 0.58929373 sigmoid 1 12 NA <NA> 22.60 -1 <NA> 0.00 infill_cb 0.72 0 1
23 10 0.6228074 44 0.1214947 0.7515075 0.34196674 relu 1 13 NA <NA> 22.73 -1 <NA> 0.00 infill_cb 0.92 0 1
24 23 0.5021470 51 0.8890780 0.3033280 0.75097924 sigmoid 1 14 NA <NA> 22.63 -1 <NA> 0.00 infill_cb 0.73 0 1
25 26 0.5572763 52 0.2083211 0.6842752 0.12736857 sigmoid 1 15 NA <NA> 22.54 -1 <NA> 0.01 infill_cb 0.92 0 1
26 20 0.6904176 46 0.4408440 0.8439430 0.53462843 tanh 1 16 NA <NA> 22.70 -1 <NA> 0.01 infill_cb 0.75 0 1
27 32 0.8357865 62 0.8108571 0.4191330 0.02935206 tanh 1 17 NA <NA> 22.45 -1 <NA> 0.02 infill_cb 0.95 0 1
28 23 0.8332311 45 0.3894060 0.7166899 0.24697168 relu 1 18 NA <NA> 22.77 -1 <NA> 0.00 infill_cb 0.81 0 1
29 30 0.6880777 58 0.3077176 0.8634141 0.41809902 sigmoid 1 19 NA <NA> 22.78 -1 <NA> 0.01 infill_cb 1.00 0 1
30 27 0.6603150 46 0.3338476 0.1976979 0.17276289 tanh 1 20 NA <NA> 22.88 -1 <NA> 0.00 infill_cb 0.86 0 1
lambda
21 2
22 2
23 2
24 2
25 2
26 2
27 2
28 2
29 2
30 2
这是我的信息
sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
Random number generation:
RNG: Mersenne-Twister
Normal: Inversion
Sample: Rounding
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] keras_2.3.0.0.9000 tensorflow_2.2.0 mlrMBO_1.1.4 smoof_1.6.0.2 checkmate_2.0.0
[6] mlr_2.17.1 ParamHelpers_1.14 rBayesianOptimization_1.1.0 caret_6.0-86 ggplot2_3.3.1
[11] lattice_0.20-41
loaded via a namespace (and not attached):
[1] nlme_3.1-148 biomartr_0.9.2 lubridate_1.7.8 bit64_0.9-7 RColorBrewer_1.1-2 progress_1.2.2 httr_1.4.1
[8] tools_4.0.0 backports_1.1.7 R6_2.4.1 rpart_4.1-15 lazyeval_0.2.2 DBI_1.1.0 BiocGenerics_0.34.0
[15] colorspace_1.4-1 nnet_7.3-13 withr_2.2.0 tidyselect_1.1.0 prettyunits_1.1.1 mco_1.0-15.1 bit_1.1-15.2
[22] curl_4.3 compiler_4.0.0 parallelMap_1.5.0 Biobase_2.48.0 plotly_4.9.2.1 scales_1.1.1 plot3D_1.3
[29] askpass_1.1 tfruns_1.4 rappdirs_0.3.1 stringr_1.4.0 digest_0.6.25 XVector_0.28.0 base64enc_0.1-3
[36] htmltools_0.4.0 pkgconfig_2.0.3 lhs_1.0.2 dbplyr_1.4.4 htmlwidgets_1.5.1 rlang_0.4.6 readxl_1.3.1
[43] rstudioapi_0.11 RSQLite_2.2.0 BBmisc_1.11 generics_0.0.2 jsonlite_1.6.1 dplyr_1.0.0.9000 ModelMetrics_1.2.2.2
[50] zip_2.0.4 magrittr_1.5 Matrix_1.2-18 Rcpp_1.0.4.6 munsell_0.5.0 S4Vectors_0.26.1 reticulate_1.16
[57] lifecycle_0.2.0 whisker_0.4 stringi_1.4.6 pROC_1.16.2 yaml_2.2.1 RJSONIO_1.3-1.4 MASS_7.3-51.6
[64] zlibbioc_1.34.0 plyr_1.8.6 recipes_0.1.12 BiocFileCache_1.12.0 misc3d_0.8-4 grid_4.0.0 blob_1.2.1
[71] parallel_4.0.0 crayon_1.3.4 Biostrings_2.56.0 splines_4.0.0 hms_0.5.3 zeallot_0.1.0 pillar_1.4.4
[78] reshape2_1.4.4 codetools_0.2-16 biomaRt_2.44.0 stats4_4.0.0 fastmatch_1.1-0 XML_3.99-0.3 glue_1.4.1
[85] data.table_1.12.8 vctrs_0.3.0 foreach_1.5.0 cellranger_1.1.0 tidyr_1.1.0 gtable_0.3.0 openssl_1.4.1
[92] purrr_0.3.4 assertthat_0.2.1 xfun_0.14 gower_0.2.1 openxlsx_4.1.5 prodlim_2019.11.13 viridisLite_0.3.0
[99] class_7.3-17 survival_3.1-12 timeDate_3043.102 tibble_3.0.1 iterators_1.0.12 tinytex_0.23 AnnotationDbi_1.50.0
[106] memoise_1.1.0 IRanges_2.22.2 lava_1.6.7 ellipsis_0.3.1 ipred_0.9-9
关于r - mlrMBO rBayesian通过插入符号优化 R keras 模型的错误，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/62240928/