The “layer_dropout” Function in R

• Package: keras

• Purpose: Add a dropout layer to prevent overfitting.

• General class: Layer

• Required argument(s):

• rate: Proportion of the input units to drop.

• Notable optional arguments:

• noise_shape: 1D integer tensor representing the shape of the binary dropout mask that will be multiplied with the input.

• seed: Integer, used to create random seeds.

• Example:

• # Load the required library
  library(keras)
  
  # Define a simple neural network model with a dropout layer
  model <- keras_model_sequential() %>%
  layer_dense(units = 64, activation = 'relu', input_shape = c(784)) %>%
  layer_dropout(rate = 0.5) %>%
  layer_dense(units = 10, activation = 'softmax')
  
  # Compile the model
  model %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = optimizer_rmsprop(),
  metrics = c('accuracy')
  )
  
  # Summary of the model
  summary(model)

• In this example, the layer_dropout function from the keras package is used to add a dropout layer to a neural network model. The model starts with a dense layer of 64 units with a ReLU activation function. The dropout layer follows, with a dropout rate of 0.5, meaning that 50% of the input units will be randomly dropped during training. This helps to prevent overfitting by introducing noise during training. Finally, the model has a dense layer with 10 units and a softmax activation function for classification into 10 classes. The model is compiled with categorical cross-entropy loss, RMSprop optimizer, and accuracy metric.