analyze_training {cito}

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 or cnn

Details

The baseline loss is the most important reference. If the model was not able to achieve a better (lower) loss than the baseline (which is the loss for a intercept only model), the model probably did not converge. Possible reasons include an improper learning rate, too few epochs, or too much regularization. See the ?dnn help or the vignette("B-Training_neural_networks").

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)

# Use model on validation set
predictions <- predict(nn.fit, iris[validation_set,])

# Scatterplot
plot(iris[validation_set,]$Sepal.Length,predictions)
}