I would like to copy CNN model output as (RDS or RDA) file in R and I would like to load resultant output in some other application.
I have tried saverds option but it is creating Empty file in work space.
model1<- saveRDS("model", file = "model.rda")
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I have the following queries
Dataset format (is how to split train, test and valid data )
Where to place the dataset
How to change the path for dataset reader
How to save the model in my own directory
And How to use the trained model
Edit
my_config['dataset_reader']['data_path'] = '/home/ec2-user/SageMaker/squad/data/'
my_config['metadata']['variables']['MODELS_PATH'] = '/home/ec2-user/SageMaker/squad/model/'
I used this command to change my dataset path and model path in configuration file. My model is saved in this location but It is not using my dataset during training instead of this it is downloading its own dataset in that folder and using it.
The example of dataset is https://github.com/deepmipt/DeepPavlov/blob/f5117cd9ad1e64f6c2d970ecaa42fc09ccb23144/deeppavlov/dataset_readers/squad_dataset_reader.py#L46
Your dataset should have the same format.
2-3. The dataset should be placed in the folder https://github.com/deepmipt/DeepPavlov/blob/f5117cd9ad1e64f6c2d970ecaa42fc09ccb23144/deeppavlov/configs/squad/squad_torch_bert.json#L4
(you can change the folder name)
Model is saved in the directory https://github.com/deepmipt/DeepPavlov/blob/f5117cd9ad1e64f6c2d970ecaa42fc09ccb23144/deeppavlov/configs/squad/squad_torch_bert.json#L166
(here you can write your own directory)
Trained model can be used with the command: python3 -m deeppavlov interact <your_config_name> More detailed tutorial how to launch models is here https://github.com/deepmipt/DeepPavlov
I am quite new to R and I have a problem which requires the use of a text file as data and to create the Normal Probability plot of the data in the text file. I am confused on how to reference to the text file in R and use it as a parameter in the function qqnorm()
If the text file (example.txt) is a table and you want to open it as a data frame, you can open the file with the command df<-read.table("example.txt") and thereafter continue to work in R. Your data is now in df.
As part of my data analysis workflow, I want to create a function that takes data as input and returns an Rmarkdown document with the output of a bunch of analyses. I don't understand how to do this very directly, though.
I guess I could create a second r script or .rmd scipt with the code to prepare the html output file and then have the r function write the data to disk with a specific filename to be used in the analysis and then have the function knitr::spin or knitr::knit the 2nd script into the output file. But is there a more direct way of doing this from within the same script?
I'd like to use the pretrained word vectors by FastText in R. This should be possible with the 'fastrtext' library, but I'm having trouble loading the files.
How do I read the .vec file with R?
How do I load the .bin file into R?
For .bin files I've tried:
library(fastrtext)
model <- load_model(choose.files()) # load e.g. cc.frr.300.bin
Model file has wrong file format!
Error in model$load(path) : Failure in fastrtext. Exit code: 1
Clearly, that's not the way to go. Any ideas?
I'm new with R in QGIS, I could write a simple script, and I want to obtain the table resulting, the table which R uses to create the plot graphics.
How can I do that?
This is the script:
##Point pattern analysis=group
##Layer=vector
##Titulo=string
##showplots
library("maptools")
library("spatstat")
K <- Kest(as.ppp(Layer))
plot(K, main=Titulo)
Can anyone help me?
The QGIS processing module runs each R script in a separate R session. If you want to save anything created then you need to save it to a file in your script, for example:
save(K,file="K.RData")
Then in another R session you can do:
load("K.RData")
library(spatstat)
and now K is restored.
You might want to pass the save file name as another parameter to your processing script, or you may want to not do this further work in QGIS...
If you want to save this as a DBF file then there's a problem caused by the fact that K is a special kind of data frame - use write.dbf(as.data.frame(K),"/path/to/K.dbf") to convert it to a plain data frame for writing. This will lose some of the information such as labels and names of the various components but you can't store irregular data in a DBF.