analyze_training {cito}

R Documentation

Visualize training of Neural Network

Description

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

Usage

analyze_training(object)

Arguments

object

a model created by dnn

Value

a 'plotly' figure

Examples

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)
}

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