The “layer_flatten” Function in R

• Package: keras

• Purpose: Flatten the input while maintaining the batch size.

• General class: Layer

• Required argument(s): None

• Notable optional arguments: None

• Example:

• # Load the required library
  library(keras)
  
  # Define a simple neural network model with a flatten layer
  model <- keras_model_sequential() %>%
  layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = 'relu', input_shape = c(28, 28, 1)) %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_flatten() %>%
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dense(units = 10, activation = 'softmax')
  
  # Compile the model
  model %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = optimizer_adam(),
  metrics = c('accuracy')
  )
  
  # Summary of the model
  summary(model)

• In this example, the layer_flatten function from the keras package is used to flatten the input while preserving the batch size. The model starts with a 2D convolutional layer followed by a max pooling layer. The layer_flatten function is then used to flatten the output of the max pooling layer, transforming it into a 1D vector, which can be fed into the fully connected dense layers that follow. This is commonly used in convolutional neural networks to transition from convolutional layers to dense layers.