Multi Layered Neural Networks in R

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.

Examples

  1. 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)

  2. 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)

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