analyze_training {cito} | R Documentation |
After training a model with cito, this function helps to analyze the training process and decide on best performing model. Creates a 'plotly' figure which allows to zoom in and out on training graph
analyze_training(object)
object |
a model created by |
a 'plotly' figure
if(torch::torch_is_installed()){
library(cito)
set.seed(222)
validation_set<- sample(c(1:nrow(datasets::iris)),25)
# Build and train Network
nn.fit<- dnn(Sepal.Length~., data = datasets::iris[-validation_set,],validation = 0.1)
# show zoomable plot of training and validation losses
analyze_training(nn.fit)
# set model which is used for predictions to model from epoch 22
nn.fit$use_model_epoch <- 22
# Use model on validation set
predictions <- predict(nn.fit, iris[validation_set,])
# Scatterplot
plot(iris[validation_set,]$Sepal.Length,predictions)
}