R语言keras深度学习输入信息格式不对
我从github上下载别人的代码，图片上的网址是代码跳到的供用户使用的h5界面，说明书说让我输入txt格式/csv格式（含有一列信息），点击处理按钮就能得到结果，但是我输入以后显示如下错误：
求求大神帮帮忙
源代码：
#' @title Retention index predictions
#'
#' @description
#' \code{DeepReI} (Deep Learning-based Retention Index predictor) is gas chromatographic retention index predictor based on convolutional neural network.
#'
#' @details
#' It is designed to predict retention index of molecules (represented as SMILES strings) on SEMI-STANDARD NON-POLAR stationary
#' phases (e.g. 5% phenyl(dimethyl)polysiloxane). Data input, prediction and export of results is performed through GUI of shiny based app.
#'
#' @param ... function has no parameters (data input is performed by GUI)
#'
#' @return shiny.appobj
#'
#' @export
#'
#' @author Tomas Vrzal <tomas.vrzal@@beerresearch.cz>
#'
#' @references reference will be added
DeepReI <- function(){
library(shiny)
library(tidyverse)
library(shinybusy)
library(shinyjs)
library(ChemmineR)
library(stringr)
library(keras)
library(writexl)
shinyApp(
ui = fluidPage(
useShinyjs(),
navbarPage(p("DeepReI - Deep Learning-based Retention Index Predictor",style = "font-family: 'times'; font-si16pt"),
# SMILES input card ----
tabPanel("SMILES input", icon = icon("file-import"), sidebarPanel(
fileInput("smiles", "Select SMILES (txt file)",buttonLabel = icon("folder-open"),
multiple = TRUE,
accept = c("text/csv",
"text/comma-separated-values,text/plain",
".csv")),
div("Input has to have only one column."),
# Horizontal line
tags$hr(),
# Input: Checkbox if file has header
checkboxInput("header", "Header", FALSE),
# Horizontal line
tags$hr(),
# Input: Select number of rows to display
radioButtons("disp", "Display",
choices = c(Head = "head",
All = "all"),
selected = "head"),
tags$hr(),
actionButton("clear_smile", label = "Clear", icon = icon("trash"))
),
mainPanel(textOutput("text_smiles"),
tableOutput("smiles_table"))
),
# Prediction card ----
tabPanel("Prediction", icon = icon("angle-double-right"), sidebarPanel(
actionButton("predict", label = "Predict Retention Index", icon = icon("cogs")),
align = "center"),
mainPanel(textOutput("text_prediction"),
tableOutput("prediction"))
),
# Export card ----
tabPanel("Export results", icon = icon("table"), sidebarPanel(
downloadButton("export", label = "Export results", icon = icon("download")), align = "center",
tags$hr(),
actionButton("copy", label = "Copy results to clipboard", icon = icon("copy"))
)),
# Info card ----
tabPanel("Info", icon = icon("info-circle"), mainPanel(div("DeepReI is a gas chromatographic retention index predictor based on convolutional neural network
It is designed to predict retention index of a molecule on SEMI-STANDARD NON-POLAR stationary
phases (e.g. 5% phenyl(dimethyl)polysiloxane)."),
tags$hr(),
div("INSTRUCTIONS:"),
div("The required input to the DeepReI is SMILES string (Simplified Molecular-Input Line-Entry System) representation of a molecule."),
div("SMILES can be loaded in SMILES input tab (.txt or .csv file) - the file have to consist of one column. If the first row represents header of the column, tick the Header box."),
div("Retention index prediction could be performed by clicking on ?Predict Retention Index? button in the Prediction tab."),
div("When prediction is done, table with predicted value of retention index, together with reprective SMILES, will appear in the Prediction tab."),
div("Export or copy of the resulted table is avalaible in Export results tab."),
tags$hr(),
div("Developed by Tomas Vrzal and Michaela Maleckova at Research Institute of Brewing and Malting, Prague, Czech Republic (www.beerresearch.cz)."),
div("Development was suported by the Ministry of agriculture of the Czech republic within the institutional support MZE-RO1918."),
tags$hr(),
div("Please cite as follows:"),
div("Tomáš Vrzal, Michaela Malečková, Jana Olšovská,DeepReI: Deep learning-based gas chromatographic retention index predictor,Analytica Chimica Acta 1147, 2021, 64 - 71"),
tags$hr(),
div("https://doi.org/10.1016/j.aca.2020.12.043; https://github.com/TomasVrzal/DeepReI"),
tags$hr(),
div("For more info and troubleshooting please contact the author: tomas.vrzal@beerresearch.cz")
)
)
)
),
server = function(input, output) {
# SMILES table output ----
output$smiles_table <- renderTable({
req(input$smiles)
sm <- read.csv(input$smiles$datapath,
header = input$header
)
if(input$disp == "head") {
return(head(sm))
}
else {
return(sm)
}
})
# Imported data text for flags ----
output$text_flags <- renderText({
req(input$flags)
return("Imported data")
})
# Imported data text fo SMILES ----
output$text_smiles <- renderText({
req(input$smiles)
return("Imported data")
})
# Flags table output ----
output$flags_table <- renderTable({
req(input$flags)
fl <- read.csv(input$flags$datapath,
header = input$header_flags,
sep = input$sep,
quote = input$quote,
dec = input$dec)
if(input$disp_flags == "head") {
return(head(fl))
}
else {
return(fl)
}
})
# RI prediction after "predict" button click ----
observeEvent(input$predict, {
req(input$smiles)
show_modal_spinner(spin = "fading-circle", text = "DeepReI in progress")
# DeepReI body
tokenizer_loaded <- load_text_tokenizer(system.file("extdata", "tokenizer_train_aug", package = "DeepReI"))
model_DeepReI <- load_model_hdf5(system.file("extdata", "model_DeepReI.h5", package = "DeepReI"), compile = FALSE)
smiles_r <- read.SMIset(input$smiles$datapath)
sdf <- ChemmineR::smiles2sdf(smiles_r)
prop <- ChemmineR::propOB(sdf)
t1_r <- str_replace_all(prop[,1], "Si", "G")
t2_r <- str_replace_all(t1_r, "c", "a")
t3_r <- str_replace_all(t2_r, "o", "b")
t4_r <- str_replace_all(t3_r, "n","d")
t5_r <- str_replace_all(t4_r, "Cl","w")
t6_r <- str_replace_all(t5_r, "Br","Q")
t7 <- texts_to_sequences(tokenizer_loaded, t6_r)
vectorize_sequences <- function(sequences, dimension = 32) {
results <- matrix(as.integer(0), nrow = 183, ncol = dimension)
for (i in 1:length(sequences))
results[i, sequences[[i]]] <- as.integer(1)
t(results)
}
t8 <- lapply(t7, vectorize_sequences)
t9 <- array_reshape(t8, c(length(smiles_r), 32, 183, 1), order = "F")
t10 <- array_reshape(t9, c(length(smiles_r), 32, 183, 1))
predicted <- model_DeepReI %>% predict(t10) %>% round(digits = 0)
sm <- read.csv(input$smiles$datapath,
header = input$header)
predicted <- cbind(sm, predicted)
colnames(predicted) <- c("SMILES", "RI")
predicted <- data.frame(predicted)
#
# Function for retention time calculation
rt <- function(flag, pred){
fl <- read.csv(flag,
header = TRUE,
sep = "\t", dec = ",", colClasses = "double")
reg <- lm(fl[,2] ~ poly(fl[,1],3, raw = TRUE))
pred_plus <- pred %>% mutate(
retention_time = coef(reg)[1] + coef(reg)[2]*pred$RI + coef(reg)[3]*pred$RI^2 + coef(reg)[4]*pred$RI^3)
return(pred_plus)
}
# Output table with predicted values
output$prediction <- renderTable({
if(is.null(isolate(input$flags$datapath)) == TRUE) {
return(predicted)
}
else {
rt_comp <- rt(flag = isolate(input$flags$datapath), pred = predicted)
return(rt_comp)
}
},
digits = 2)
remove_modal_spinner()
# Export or copy results ----
output$export <- downloadHandler(
filename = function() {
paste(input$smiles, Sys.Date(), "-", format(Sys.time(), "%R"), ".xlsx", sep="")
},
content = function(file) {
if(is.null(isolate(input$flags$datapath)) == TRUE){
write_xlsx(predicted, path = file)
}
else {
write_xlsx(rt(flag = isolate(input$flags$datapath), pred = predicted), path = file)
}
})
observeEvent(input$copy, {
req(predicted)
if(is.null(isolate(input$flags$datapath)) == TRUE){
write.table(predicted, "clipboard-16384", sep="\t", col.names=TRUE, row.names = FALSE)
}
else {
write.table(rt(flag = isolate(input$flags$datapath), pred = predicted), "clipboard-16384", sep="\t", col.names=TRUE, row.names = FALSE)
}
})
})
# Reset flags input (delete flags file from input) ----
observeEvent(input$clear_flags, {
reset("flags")
})
# Reset SMILES input (delete SMILES file from input) ----
observeEvent(input$clear_smile, {
reset("smiles")
})
},
options = list(launch.browser = TRUE)
)
}源代码里有两次使用了array_reshape，我如果对其进行更改就会报错说找不到input
从报错信息看，是输入数据的维度有问题，本来需要2维数组，但现在是一个多维数组，不过需要确定那一行代码出现了问题，才好有针对性地调试、解决。
对于多维数组，可以展开成二维数组。
您好，我是有问必答小助手，您的问题已经有小伙伴解答了，您看下是否解决，可以追评进行沟通哦~
如果有您比较满意的答案 / 帮您提供解决思路的答案，可以点击【采纳】按钮，给回答的小伙伴一些鼓励哦～～
ps:问答VIP仅需29元，即可享受5次/月 有问必答服务，了解详情>>>https://vip.csdn.net/askvip?utm_source=1146287632