The “layer_dense” Function in R

• Package: keras

• Purpose: Add a densely-connected neural network layer.

• General class: Layer

• Required argument(s):

• units: Positive integer, dimensionality of the output space.

• Notable optional arguments:

• activation: Activation function to use (e.g., ‘relu’, ‘sigmoid’).

• use_bias: Boolean, whether the layer uses a bias vector.

• kernel_initializer: Initializer for the kernel weights matrix.

• bias_initializer: Initializer for the bias vector.

• input_shape: Shape of the input (required only for the first layer).

• Example:

• # Load the required library
  library(keras)
  
  # Define a simple neural network model with one dense layer
  model <- keras_model_sequential() %>%
  layer_dense(units = 32, activation = 'relu', input_shape = c(784)) %>%
  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_dense function from the keras package is used to add two densely-connected layers to a neural network model. The first layer has 32 units with a ReLU activation function and expects an input shape of 784 (typical for MNIST dataset). The second layer has 10 units with a softmax activation function, suitable for classification into 10 classes. The model is then compiled with categorical cross-entropy loss, RMSprop optimizer, and accuracy metric.