discriminator.R

discriminator <-
  function(name = NULL) {
    keras_model_custom(name = name, function(self) {

      self$conv1 <- layer_conv_2d(
        filters = 64,
        kernel_size = c(5, 5),
        strides = c(2, 2),
        padding = "same"
      )
      self$leaky_relu1 <- layer_activation_leaky_relu()
      self$dropout <- layer_dropout(rate = 0.3)
      self$conv2 <-
        layer_conv_2d(
          filters = 128,
          kernel_size = c(5, 5),
          strides = c(2, 2),
          padding = "same"
        )
      self$leaky_relu2 <- layer_activation_leaky_relu()
      self$flatten <- layer_flatten()
      self$fc1 <- layer_dense(units = 1)

      function(inputs, mask = NULL, training = TRUE) {
        inputs %>% self$conv1() %>%
          self$leaky_relu1() %>%
          self$dropout(training = training) %>%
          self$conv2() %>%
          self$leaky_relu2() %>%
          self$flatten() %>%
          self$fc1()
      }
    })
  }