plot_dlayer: plot_dlayer Visualize hidden layer activations in a deep...

In andrewsommerlot/startml: Start doing machine learning with training optimization and ensembles of popular machine learning algorithms powered by scalable implementations in h2o

Description

plot_dlayer Visualize hidden layer activations in a deep learning model with dimentionality reduction and variable labels

Usage

plot_dlayer(model, vis_data, layer, label, dimentions = 2,
  max_points = 1000, tsne_iter = 500, wd = getwd())

Arguments

model	H2O model object containing labeled data for model training. No Default.
vis_data	H2O frame object containing data to caculate layer activations. No Default.
layer	Numeric object of length 1 identifying the which hidden layer to visualize. No Defalut.
label	Character object of length 1 identifying the column name of the variable in vis_data to label visulaizaion points with. No Default.
dimentions	Numeric object set to 2 or 3, 2 returns ggplot figure, 3 returns plotly html page
max_points	Numeric object setting maximum number of observations in visualization. A number of rows equal to max_points from vis_data are sampled with out replacement for the visulaization.
tsne_iter	Numberic object sets the number of iterations in t-tailed stochastic nearest neighbors dimentionality reduction operation. Defaults to 1000.
wd	Character object defining file path where html interactive graphic will be saved if dimentions = 3. Defaults to current working directory.

Value

None

andrewsommerlot/startml documentation built on May 5, 2019, 6:58 p.m.