Building a Simple Neural Network in R

Building a Simple Neural Network in R

Building a neural network in R is made significantly easier using specialized packages. One of the popular choices is keras, a high-level neural network API that interfaces nicely with R.

In this tutorial, we'll use the keras package in R to build a simple feedforward neural network.

1. Setup

First, you need to install the necessary packages:

install.packages("keras")

Next, you'll need to install TensorFlow, the backend for keras. You can do this in R using:

library(keras) install_keras()

2. Load Dataset

For this example, we'll use the classic MNIST dataset, which consists of 28x28 pixel handwritten digit images:

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

3. Preprocess the Data

# Rescale the images by dividing every pixel value by 255. train_images <- train_images / 255 test_images <- test_images / 255 # One-hot encode the labels train_labels <- to_categorical(train_labels) test_labels <- to_categorical(test_labels)

4. Build the Neural Network Model

Let's create a simple feedforward network with one hidden layer:

model <- keras_model_sequential() %>% layer_flatten(input_shape = c(28, 28)) %>% layer_dense(units = 128, activation = 'relu') %>% layer_dropout(rate = 0.5) %>% layer_dense(units = 10, activation = 'softmax') # Print the summary of the model summary(model)

5. Compile the Model

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

6. Train the Model

history <- model %>% fit( train_images, train_labels, epochs = 10, batch_size = 128, validation_split = 0.2 )

7. Evaluate the Model

Evaluate the model's performance on the test set:

scores <- model %>% evaluate(test_images, test_labels, batch_size = 128) cat('Test loss:', scores[[1]], '\n') cat('Test accuracy:', scores[[2]], '\n')

8. Predict with the Model

predictions <- model %>% predict(test_images)

That's it! You've just built, trained, and evaluated a simple neural network in R using keras. You can expand on this by experimenting with adding more layers, different activation functions, and other hyperparameters.

Examples

  1. Building a Basic Neural Network in R Example:

    # Using neuralnet package library(neuralnet) # Generate sample data set.seed(123) data <- data.frame( input1 = rnorm(100), input2 = rnorm(100), output = rnorm(100) ) # Create a basic neural network model neural_model <- neuralnet(output ~ input1 + input2, data = data, hidden = c(5, 3), linear.output = TRUE) # Print the neural network model print(neural_model)

  2. Creating Input and Output Layers in R Neural Network:

    # Creating input and output layers neural_model <- neuralnet(output ~ input1 + input2, data = data, hidden = c(5, 3), linear.output = TRUE)

  3. Adding Hidden Layers to a Neural Network in R:

    # Adding hidden layers neural_model <- neuralnet(output ~ input1 + input2, data = data, hidden = c(5, 3), linear.output = TRUE)

  4. Training a Simple Neural Network in R:

    # Training a neural network trained_model <- train(neural_model, data)

  5. Visualizing a Simple Neural Network in R:

    • Visualization can be done using the plot function in the neuralnet package.
    # Visualizing a neural network plot(neural_model)

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