The “layer_conv_2d” Function in R

• Package: keras

• Purpose: Perform 2D convolution on input data.

• General class: Layer

• Required argument(s):

• filters: Number of output filters in the convolution.

• kernel_size: Dimensions of the convolution window.

• Notable optional arguments:

• strides: Stride length of the convolution.

• padding: Padding mode (‘valid’ or ‘same’).

• activation: Activation function to use.

• input_shape: Shape of the input data (required for the first layer in the model).

• Example:

• # Load the required library
  library(keras)
  
  # Define a simple neural network model with a 2D convolutional 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_conv_2d function from the keras package is used to add a 2D convolutional layer to the neural network model. The convolutional layer has 32 filters, each of size 3x3, and uses the ReLU activation function. The input_shape argument specifies the shape of the input data, which is required for the first layer in the model. The model continues with a max pooling layer, a flatten layer, and two dense layers to perform classification.