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README.Rmd
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README.Rmd
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---
title: "Untitled"
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# deepviz
The goal of deepviz is to visualize (simple) neural network architectures.
[](https://travis-ci.org/andrie/deepviz)
## Installation
``` r
devtools::install_github("andrie/deepviz")
```
## Load the packages
```{r}
library(deepviz)
library(magrittr)
```
## plot_model() with sequential models
Create a model
```{r load-keras}
require(keras)
```
```{r model-seq-1}
model <- keras_model_sequential() %>%
layer_dense(10, input_shape = 4) %>%
layer_dense(2, activation = "sigmoid")
```
Plot the model
```{r eval=FALSE}
model %>% plot_model()
```
```{r webshot-1, echo=FALSE, fig.height=2}
htm_file <- tempfile(fileext = ".html")
model %>% plot_model() %>%
htmlwidgets::saveWidget(htm_file)
webshot::webshot(htm_file, file = tempfile(fileext = ".png"))
```
Add some more layers and plot
```{r model-seq-2}
model <- keras_model_sequential() %>%
layer_conv_2d(filters = 16, kernel_size = c(3, 3)) %>%
layer_max_pooling_2d() %>%
layer_dense(10, input_shape = 4) %>%
layer_dense(10, input_shape = 4) %>%
layer_dropout(0.25) %>%
layer_dense(2, activation = "sigmoid")
model %>% plot_model()
```
```{r eval=FALSE}
model %>% plot_model()
```
```{r webshot-2, echo=FALSE, fig.height=4}
htm_file <- tempfile(fileext = ".html")
model %>% plot_model() %>%
htmlwidgets::saveWidget(htm_file)
webshot::webshot(htm_file, file = tempfile(fileext = ".png"))
```
## plot_model() with network models
Construct a network model using the `keras` function API, using the example from https://keras.rstudio.com/articles/functional_api.html
```{r network-1}
model <- local({
main_input <- layer_input(shape = c(100), dtype = 'int32', name = 'main_input')
lstm_out <- main_input %>%
layer_embedding(input_dim = 10000, output_dim = 512, input_length = 100) %>%
layer_lstm(units = 32)
auxiliary_output <- lstm_out %>%
layer_dense(units = 1, activation = 'sigmoid', name = 'aux_output')
auxiliary_input <- layer_input(shape = c(5), name = 'aux_input')
main_output <- layer_concatenate(c(lstm_out, auxiliary_input)) %>%
layer_dense(units = 64, activation = 'relu') %>%
layer_dense(units = 64, activation = 'relu') %>%
layer_dense(units = 64, activation = 'relu') %>%
layer_dense(units = 1, activation = 'sigmoid', name = 'main_output')
keras_model(
inputs = c(main_input, auxiliary_input),
outputs = c(main_output, auxiliary_output)
)
})
model
```
Plot the model
```{r, eval=FALSE}
model %>% plot_model()
```
```{r network-webshot-1, echo=FALSE, fig.height=4}
htm_file <- tempfile(fileext = ".html")
model %>% plot_model() %>%
htmlwidgets::saveWidget(htm_file)
webshot::webshot(htm_file, file = tempfile(fileext = ".png"))
```
## plot_deepviz()
### Logistic regression:
```{r, fig.height=1.5}
c(4, 1) %>%
plot_deepviz()
```
### One hidden layer:
```{r, fig.height=2.5}
c(4, 10, 1) %>%
plot_deepviz()
```
### A multi-layer perceptron (two hidden layers):
```{r, fig.height=3}
c(4, 10, 10, 1) %>%
plot_deepviz()
```
### Multi-class classification
```{r, fig.height=3}
c(4, 10, 10, 3) %>%
plot_deepviz()
```