def run():
# set initial time
start_time = time.time()
# dump hyper parameters settings
print(time.strftime('%Y-%m-%d %H:%M:%S') + ' Hyper-parameters setting')
# get configuration
args = config.get()
# check for save_sample_image model
save_best_model = ModelCheckpoint(
filepath=args.model_file_train, verbose=1,
save_best_only=True) # , save_best_only=True
if os.path.isfile(args.model_file_train):
print(time.strftime('%Y-%m-%d %H:%M:%S') + ' Load model from file...')
skip_gram_model = load_model(args.model_file_train)
else:
# get embedding model
print(time.strftime('%Y-%m-%d %H:%M:%S') + ' Build model...')
skip_gram_model = model.get(args)
# dashboard
watch_board = Dashboard(folder=config.FOLDER,
dump_file="dashboard.dump",
statistic_file="statistic.txt",
model=skip_gram_model,
show_board=True)
# begin training
print(time.strftime('%Y-%m-%d %H:%M:%S') + " Begin training..")
# Train the model each generation and show predictions against the
# validation dataset
for iteration in range(1, args.iterations):
print(
time.strftime('%Y-%m-%d %H:%M:%S') + ' Iteration %d ' % iteration)
skip_gram_model.fit_generator(
zhwiki_corpus.skip_gram_generator(batch_size=args.batch_size,
context_window_size=5,
negative_samples=10),
steps_per_epoch=args.steps_per_epoch,
epochs=args.epochs,
callbacks=[save_best_model, watch_board],
# callbacks=[save_best_model],
validation_data=zhwiki_corpus.skip_gram_generator(
batch_size=args.batch_size,
context_window_size=5,
negative_samples=10),
validation_steps=100,
verbose=0)
# close_board windows
watch_board.close_board()
print("task took %.3fs" % (float(time.time()) - start_time))
