Neural Networks

Classification

R Packages

# install.packages("torch")
# install.packages("luz")

library(torch)
library(luz) # high-level interface for torch
torch_manual_seed(13)

Data in Python

import plotnine
from plotnine.data import penguins
clean_penguins = (
    penguins
        .dropna()
        .drop(columns=["year"])
)

Classification

• Classification

• Loss Functions

• Neural Network

• In R

• Thursday

Classification

Classification in statistical learning terms indicates predicting a categorical random variable.

Common Methods

• Naive Bayes Classifier

• Tree-based Methods

• Support Vector Machines

• Logistic/Multinomial regression

• Discriminant Analysis

Neural Networks

Loss Functions

• Classification

• Loss Functions

• Neural Network

• In R

• Thursday

Loss Functions

Neural Network

• Classification

• Loss Functions

• Neural Network

• In R

• Thursday

In R

• Classification

• Loss Functions

• Neural Network

• In R

• Thursday

• Classification

• Loss Functions

• Neural Network

• In R

• Thursday

Penguin Data

• Train/Test/Evaluation
• Training
• Validate
• Testing

penguins <- penguins |> drop_na()
training <- penguins |> slice_sample(prop = .8)
pre <- penguins |> anti_join(training)
validate <- pre |> slice_sample(prop =  0.5)
testing <- pre |> anti_join(validate)

Xtraining <- training |> 
  model.matrix(body_mass ~ . - 1, data = _) |> 
  scale() |> 
  torch_tensor(dtype = torch_float())

Ytraining <- training |> 
  select(body_mass) |> 
  as.matrix() |> 
  torch_tensor(dtype = torch_float())

Xvalidate <- validate |> 
  model.matrix(body_mass ~ . - 1, data = _) |> 
  scale() |> 
  torch_tensor(dtype = torch_float())

Yvalidate <- validate |> 
  select(body_mass) |> 
  as.matrix() |> 
  torch_tensor(dtype = torch_float())

Xtesting <- testing |> 
  model.matrix(body_mass ~ . - 1, data = _) |> 
  scale() |> 
  torch_tensor(dtype = torch_float())

Ytesting <- testing |> 
  select(body_mass) |> 
  as.matrix() |> 
  torch_tensor(dtype = torch_float())

Model Description

modelnn <- nn_module(
  initialize = function(input_size) {
    self$hidden1 <- nn_linear(in_features = input_size, 
                              out_features = 20)
    self$hidden2 <- nn_linear(in_features = 20, 
                              out_features = 10)
    self$hidden3 <- nn_linear(in_features = 10, 
                              out_features = 5)

    self$output <- nn_linear(in_features = 5, 
                             out_features = 1)
        
    self$activation <- nn_relu()
  },
  forward = function(x) {
    x |>  
      self$hidden1() |>  
      self$activation() |>  
      
      self$hidden2() |>  
      self$activation() |>  

      self$hidden3() |>  
      self$activation() |>  

      self$output()
  }
)

Optimizer Set Up

modelnn <- modelnn |> 
  setup(
    loss = nn_mse_loss(), # Used for numerical counts
    optimizer = optim_rmsprop
  ) |>
  set_hparams(input_size = ncol(px))

Fit a Model

• Fit
• Plot
• Testing Model
• Thursday

fitted <- modelnn |> 
  fit(
    data = list(Xtraining, Ytraining),
    epochs = 50, # Can think as number of iterations
    valid_data = list(Xvalidate, Yvalidate)
  )

plot(fitted)

• Prediction
• MAE
• Plot

npred <- predict(fitted, Xtesting)

mean(abs(as.matrix(Ytesting) - as.matrix(npred)))

plot(as.matrix(Ytesting), as.matrix(npred),
     xlab = "Truth",
     ylab = "Predicted")

Thursday

• Classification

• Loss Functions

• Neural Network

• In R

• Thursday

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