# trained_model, history = def_train_model.fully_connected_with_dropout_l2(
# x_train, x_val, y_train, y_val, MAX_WORDS, MAX_SEQUENCE_LEN, word_index)
serialize_model_and_history(trained_model, history)
log_plot_training_performance(history, PLOT=False) # Note: Cannot plot on Compute Canada nodes.
# evaluate_model_on_test_set(trained_model, x_test, y_test)
evaluate_model_on_test_set(trained_model, x_test_asi, y_test_asi, TEST_SET_LABEL='ASI')
evaluate_model_on_test_set(trained_model, x_test_pan18ap, y_test_pan18ap, TEST_SET_LABEL='PAN18AP')
# Destroy the current TF graph and create a new one, to ensure reproducible results.
# This is also useful to avoid clutter from old models/layers.
K.clear_session()
# Log run time
logger.info("@ %.2f seconds: Run finished", time.process_time())
'''
The following lines will be executed only if this .py file is run as a script,
and not if it is imported as a module.
• __name__ is one of the import-related module attributes, which holds the name of the module.
• A module's __name__ is set to '__main__' when it is running in
the main scope (the scope in which top-level code executes).
'''
if __name__ == '__main__':
logger, RUN_TIMESTAMP = my_utils.configure_root_logger(1)
my_utils.set_working_directory(1)
main()
# At this point, some users might have no tweets, either because an inconsistency between the list of users (when
# loading the user demographics) and the existing tweets dataset, or because all of their tweets were retweets
# or short tweets and were dropped in the previous operations.
# Let's drop those users
dataset.drop_users_with_no_tweets()
dataset.drop_users_with_few_total_words(100)
dataset.produce_stats('ASI dataset stats')
dataset.labels_to_xml()
dataset.all_tweets_to_xml()
# Log run time
logger.info("@ %.2f seconds: Run finished. Current date and time: %s",
time.process_time(), datetime.today())
'''
The following lines will be executed only if this .py file is run as a script,
and not if it is imported as a module.
• __name__ is one of the import-related module attributes, which holds the name of the module.
• A module's __name__ is set to '__main__' when it is running in
the main scope (the scope in which top-level code executes).
'''
if __name__ == '__main__':
proj_dir = 'C:/Users/Saman/GitHub/usermodeling'
logger, RUN_TIMESTAMP = my_utils.configure_root_logger(proj_dir)
my_utils.set_working_directory(proj_dir)
main()