plot.keras.engine.training.Model: Plot a Keras model

In keras: R Interface to 'Keras'

Plot a Keras model

Description

Plot a Keras model

Usage

## S3 method for class 'keras.engine.training.Model'
plot(
  x,
  show_shapes = FALSE,
  show_dtype = FALSE,
  show_layer_names = TRUE,
  ...,
  rankdir = "TB",
  expand_nested = FALSE,
  dpi = 96,
  layer_range = NULL,
  show_layer_activations = FALSE,
  to_file = NULL
)

Arguments

x	A Keras model instance
show_shapes	whether to display shape information.
show_dtype	whether to display layer dtypes.
show_layer_names	whether to display layer names.
...	passed on to keras$utils$plot_model(). Used for forward and backward compatibility.
rankdir	a string specifying the format of the plot: 'TB' creates a vertical plot; 'LR' creates a horizontal plot. (argument passed to PyDot)
expand_nested	Whether to expand nested models into clusters.
dpi	Dots per inch. Increase this value if the image text appears excessively pixelated.
layer_range	list containing two character strings, which is the starting layer name and ending layer name (both inclusive) indicating the range of layers for which the plot will be generated. It also accepts regex patterns instead of exact name. In such case, start predicate will be the first element it matches to layer_range[1] and the end predicate will be the last element it matches to layer_range[2]. By default NULL which considers all layers of model. Note that you must pass range such that the resultant subgraph must be complete.
show_layer_activations	Display layer activations (only for layers that have an activation property).
to_file	File name of the plot image. If NULL (the default), the model is drawn on the default graphics device. Otherwise, a file is saved.

Value

Nothing, called for it's side effects.

Raises

ValueError: if plot_model is called before the model is built, unless a ⁠input_shape = ⁠ argument was supplied to keras_model_sequential().

Requirements

This function requires pydot and graphviz. pydot is by default installed by install_keras(), but if you installed tensorflow by other means, you can install pydot directly with :

reticulate::py_install("pydot", pip = TRUE)

In a conda environment, you can install graphviz with:

reticulate::conda_install(packages = "graphviz")
# Restart the R session after install.

Otherwise you can install graphviz from here: https://graphviz.gitlab.io/download/

keras documentation built on Aug. 16, 2023, 1:07 a.m.