dt_test = dataset_manager.encode_data_with_label_all_data(test)
if normalize_over == "train":
dataset_manager.calculate_divisors(dt_train)
elif normalize_over == "all":
dt_all = dataset_manager.extract_timestamp_features(data)
dt_all = dataset_manager.extract_duration_features(dt_all)
dataset_manager.calculate_divisors(dt_all)
else:
print("unknown normalization mode")
dt_test = dataset_manager.normalize_data(dt_test)
print("Done: %s" % (time.time() - start))
max_len = dataset_manager.get_max_case_length(dt_train)
activity_cols = [col for col in dt_train.columns if col.startswith("act")]
n_activities = len(activity_cols)
data_dim = dt_train.shape[1] - 3
# compile a model with same parameters that was trained, and load the weights of the trained model
print('Building model...')
start = time.time()
main_input = Input(shape=(max_len, data_dim), name='main_input')
# train a 2-layer LSTM with one shared layer
l1 = LSTM(lstmsize,
input_shape=(max_len, data_dim),
consume_less='gpu',
init='glorot_uniform',
return_sequences=True,
dropout_W=dropout)(main_input) # the shared layer
