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")