---
title: "Stat. 654 Quiz brulee using torch"
author: "Your Name Here"
date: ""
format: 
  html:
    embed-resources: true
---

## Feed-Forward Neural Network for the Google Tensorflow Playground XOR Data

### Clone the [TFPlayground](https://github.com/hyounesy/TFPlaygroundPSA) Github repository into your R Project folder.

To clone the repository you can use RStudio

File > New Project > Version Control > Git

and paste the URL of the repository into the Git Repository URL box.  Then select a folder to clone the repository into.  

Click the Green button and copy the ulr: *https://github.com/hyounesy/TFPlaygroundPSA.git*

Then paste the URL into the Git Repository URL box.  Select a folder to clone the repository into.  Click the Create Project button.  

Use the data in *../data/tiny/xor_25/input.txt* to create a feed-forward neural network to classify the data.  Use the *keras* package to create the model.  

### Load the required libraries

```{r}
library(tidyverse)
library(tidymodels)
library(readr)
library(janitor)
library(brulee)
library(keras)
```

### Load the data

```{r}
input <- read_delim("data/tiny/xor_25/input.txt", 
     delim = "\t", escape_double = FALSE, 
     trim_ws = TRUE)

input <- input |> 
  clean_names() |> 
  select(-pid) |> 
  mutate(label = as.factor(label)) |>
  tibble()

head(input)
```

### Split the data into training and testing sets


```{r}
n <- nrow(input)

input_parts <- input |>
  initial_split(prop = 0.8)

train <- input_parts |>
  training()

test <- input_parts |>
  testing()

list(train, test) |>
  map_int(nrow)
```

### Visualize the data

```{r}
train |> 
  ggplot(aes(x = x1, y = x2, color = factor(label))) +
  geom_point()
```

```{r}
test |> 
  ggplot(aes(x = x1, y = x2, color = factor(label))) +
  geom_point()
```

### brulee uses torch

```{r}
mod_nn <- mlp(mode = "classification", hidden_units = 8) |>
  set_engine("brulee") |>
  fit(label ~ x1 + x2, data = train)
mod_nn


#  train
pred <- train |> bind_cols(
    predict(mod_nn, new_data = train, type = "class")
  ) |>
  rename(mod_nn = .pred_class)

accuracy(pred, label, mod_nn)

# test
pred <- test |> bind_cols(
    predict(mod_nn, new_data = test, type = "class")
  ) |>
  rename(mod_nn = .pred_class)

accuracy(pred, label, mod_nn)

```

### brulee two layer

```{r}
mod_nn <- mlp(mode = "classification", hidden_units = 8) |>
  set_engine("brulee_two_layer") |>
  fit(label ~ x1 + x2, data = train)
mod_nn

#  train
pred <- train |> bind_cols(
    predict(mod_nn, new_data = train, type = "class")
  ) |>
  rename(mod_nn = .pred_class)

accuracy(pred, label, mod_nn)

# test
pred <- test |> bind_cols(
    predict(mod_nn, new_data = test, type = "class")
  ) |>
  rename(mod_nn = .pred_class)

accuracy(pred, label, mod_nn)

```