Multi Layered Neural Networks in R

In this tutorial, we will walk through the creation and training of a multi-layered neural network (also known as a deep feedforward network) in R using the keras package, which is an R interface to the popular deep learning Python library.

1. Installation and Setup:

Before we can use keras in R, it requires both R and Python installations. It's recommended to have Python and TensorFlow already set up on your machine.

To install keras in R, you can use:

install.packages("keras")

Then, you can install TensorFlow via keras:

library(keras)
install_keras()

2. Building a Multi-layered Neural Network:

We will create a model for the classic MNIST dataset, a set of handwritten digits.

Load the Dataset:

data <- dataset_mnist()
train_images <- data$train$x
train_labels <- data$train$y
test_images <- data$test$x
test_labels <- data$test$y

Preprocess the Data:

train_images <- array_reshape(train_images, c(60000, 28 * 28))
train_images <- train_images / 255
test_images <- array_reshape(test_images, c(10000, 28 * 28))
test_images <- test_images / 255

train_labels <- to_categorical(train_labels)
test_labels <- to_categorical(test_labels)

Build the Model:

model <- keras_model_sequential() %>%
  layer_dense(units = 512, activation = 'relu', input_shape = c(28 * 28)) %>%
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dense(units = 10, activation = 'softmax')

model %>% compile(
  optimizer = 'rmsprop',
  loss = 'categorical_crossentropy',
  metrics = c('accuracy')
)

Here, we've set up a neural network with three layers:

Input layer with 512 neurons and relu activation
Hidden layer with 128 neurons and relu activation
Output layer with 10 neurons (for 10 digit classes) and softmax activation

Train the Model:

history <- model %>% fit(
  train_images, train_labels,
  epochs = 5,
  batch_size = 128,
  validation_split = 0.2
)

Evaluate the Model:

model %>% evaluate(test_images, test_labels)

3. Visualize Training:

You can visualize the training progress by plotting the metrics stored in the history object:

plot(history)

Conclusion:

This is a basic introduction to creating multi-layered neural networks in R using keras. The keras package provides a flexible and powerful way to create deep learning models in R, leveraging the capabilities of the underlying TensorFlow library. Remember, the architecture and hyperparameters chosen in this example are just a starting point; in practice, these would need tuning for optimal performance.

Building multi-layered neural networks in R:

Overview: Introduce the concept of multi-layered neural networks and the steps involved in building them in R.

Code:

# Using the neuralnet package to build a multi-layered neural network
library(neuralnet)

# Sample data
data <- data.frame(
  input1 = c(0, 1, 0, 1),
  input2 = c(0, 0, 1, 1),
  output = c(0, 1, 1, 0)
)

# Building a neural network with one hidden layer
neural_network <- neuralnet(output ~ input1 + input2, data = data, hidden = c(3), linear.output = FALSE)

# Printing the neural network
print(neural_network)

Training and testing multi-layered networks in R:

Overview: Cover the steps involved in training and testing multi-layered neural networks in R.

Code:

# Training and testing a neural network in R
set.seed(123)
train_indices <- sample(1:nrow(data), 0.7 * nrow(data))

# Splitting data into training and testing sets
train_data <- data[train_indices, ]
test_data <- data[-train_indices, ]

# Building and training the neural network
neural_network <- neuralnet(output ~ input1 + input2, data = train_data, hidden = c(3), linear.output = FALSE)

# Testing the neural network on the test set
predictions <- predict(neural_network, newdata = test_data)