cnn-spec.R

library(keras)

# Hyperparameter flags --------------------------------------------------------

FLAGS <- flags(
  flag_integer("kernel_size1", 5),
  flag_integer("strides1", 1)
)

# Define Model ----------------------------------------------------------------

model <- keras_model_sequential() %>%
  layer_embedding(input_dim = max_words + 1, output_dim = 16,
                  input_length = max_length) %>%
  layer_conv_1d(filter = 32, 
                kernel_size = FLAGS$kernel_size1,
                strides = FLAGS$strides1,
                activation = "relu") %>%
  layer_global_max_pooling_1d() %>%
  layer_dense(units = 64, activation = "relu") %>%
  layer_dense(units = 1, activation = "sigmoid")

model %>% compile(
  optimizer = "adam",
  loss = "binary_crossentropy",
  metrics = c("accuracy")
)

# Training & Evaluation ----------------------------------------------------

history <- model %>% fit(
  x = kick_analysis, 
  y = state_analysis,
  batch_size = 512,
  epochs = 10,
  validation_data = list(kick_assess, state_assess)
)

plot(history)

score <- model %>% evaluate(
  kick_assess, state_assess
)

cat("Test accuracy:", score["accuracy"], "\n")