R Multiclasses Classification Neural Network 报错：“Matrix type cannot be converted to python”

Question

我正在尝试建立一个神经网络，以便对人的图像进行分类，并预测他们的年龄。

代码如下：

library(EBImage)
library(keras)

# Read images
setwd('C:\\Users\\smbsv\\Desktop\\UTKFace')

# Get file list with full path and file names
files <- list.files('C:\\Users\\smbsv\\Desktop\\UTKFace', full.names = TRUE, pattern = "jpg$|JPG$")

files

# Select the desired % or number of file by simple random sampling 
randomize <- sample(seq(files))

files2analyse <- files[randomize]


files2analyse <- files2analyse[(1:5000)]

files2analyse

mypic <- list()

for (i in 1:length(files2analyse)) {mypic[[i]] <- readImage(files2analyse[i])}

mypic

display(mypic[[1]])
display(mypic[[100]])
display(mypic[[2400]])
display(mypic[[4720]])

# Resize
for (i in 1:length(mypic)) {mypic[[i]] <- resize(mypic[[i]], 128, 128)}

display(mypic[[2400]])
display(mypic[[4720]])

# Reshape
for (i in 1:length(mypic)) {mypic[[i]] <- array_reshape(mypic[[i]], c(128, 128, 3))}

#make this example reproducible
set.seed(1493)

#use 70% of dataset as training set and 30% as test set
sample <- sample(c(TRUE, FALSE), length(mypic), replace=TRUE, prob=c(0.7,0.3))

train  <- mypic[sample]
test   <- mypic[!sample]

filenamesTRAIN <- files2analyse[sample]
filenamesTEST <- files2analyse[!sample]

filenamesTRAIN[10]
display(train[[10]])

filenamesTRAIN[362]
display(train[[362]])

filenamesTEST[25]
display(test[[25]])

filenamesTEST[189]
display(test[[189]])

length(train)
length(test)

trainx <- NULL

trainx <- rbind(trainx, train)
trainx

str(trainx)

testx <- NULL
testx <- rbind(testx, test)

str(testx)

#change files names to only images names (TRAIN)

for (i in 1:length(filenamesTRAIN)) {filenamesTRAIN[i] <- substring(filenamesTRAIN[i], 32)}

#change files names to only images names (TRAIN)

for (i in 1:length(filenamesTEST)) {filenamesTEST[i] <- substring(filenamesTEST[i], 32)}

filenamesTRAIN[27]
filenamesTRAIN[147]

filenamesTEST[189]
filenamesTEST[4]

#extract classes from filename (pattern: [age]_[gender(0=male,1=female)]_[race(from 0 to 4, denoting White, Black, Asian, Indian, and Others)]_[date&time].jpg)
library(stringr)

trainy <- NULL

for (i in 1:length(filenamesTRAIN)) {trainy[i] <- str_split(filenamesTRAIN[i], "_")[[1]][1]}

filenamesTRAIN[20]
trainy[20]
filenamesTRAIN[100]
trainy[100]

testy <- NULL

for (i in 1:length(filenamesTEST)) {testy[i] <- (str_split(filenamesTEST[i], "_")[[1]][1])} 

filenamesTEST[16]
testy[16]
filenamesTEST[185]
testy[185]

# One Hot Encoding
class(trainy[4])
for (i in 1:length(trainy)) {trainy[i] <- as.numeric(trainy[i]) - 1} # -1 avoid 1 more column when use "to_categorical"
for (i in 1:length(testy)) {testy[i] <- as.numeric(testy[i]) - 1}
  
trainLabels <- to_categorical(trainy)
testLabels <- to_categorical(testy)

filenamesTEST[7]
testLabels    # rows: people ; columns : labels (age)

# Model
model <- keras_model_sequential()
model %>%
  layer_dense(units = 256, activation = 'relu', input_shape = c(3498)) %>%
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dense(units = 116, activation = 'softmax')
summary(model)

# Compile
model %>%
  compile(loss = 'crossentropy',
          optimizer = optimizer_rmsprop(),
          metrics = c('accuracy'))

# Fit Model
history <- model %>%
  fit(trainx,
      trainLabels,
      epochs = 30,
      batch_size = 32,
      validation_split = 0.2)

一切正常，直到我尝试拟合模型，它给了我这个错误：

Error: Matrix type cannot be converted to python (only integer, numeric, complex, logical, and character matrixes can be converted

我认为问题是“trainx”和“trainLabels”的数据类型。 如果有用的话，这是这些变量的返回类型：

火车x：

>str(trainx)

List of 3498
$ : num [1:128, 1:128, 1:3] 0.113 0.123 0.121 0.117 0.118 ...
$ : num [1:128, 1:128, 1:3] 0.108 0.127 0.163 0.222 0.29 ...

...
- attr(*, "dim")= int [1:2] 1 3498
 - attr(*, "dimnames")=List of 2
  ..$ : chr "train"
  ..$ : NULL

火车标签：

> str(trainLabels)
num [1:3498, 1:116] 0 0 0 0 0 0 0 0 0 0 ...

我该如何解决这个错误？