dropout["INTRA"] = 0
MAX_EPOCHS = 30
min_time = 1.0
time_threshold = torch.cuda.FloatTensor([min_time]) / 24
dims["INTRA_HIDDEN"] = dims["EMBEDDING_DIM"]
dims["INTER_INPUT_DIM"] = dims["INTRA_HIDDEN"] + dims["TIME_HIDDEN"] + dims[
"USER_HIDDEN"]
dims["INTER_HIDDEN"] = dims["INTRA_HIDDEN"]
datahandler = DataHandler(dataset_path, BATCHSIZE, MAX_SESSION_REPRESENTATIONS,
dims["INTRA_HIDDEN"], dims["TIME_RESOLUTION"],
min_time)
dims["N_ITEMS"] = datahandler.get_num_items()
N_SESSIONS = datahandler.get_num_training_sessions()
dims["N_USERS"] = datahandler.get_num_users()
# TODO: Initialize tester
tester = Tester("Log")
model = DynamicRecModel(dims, dropout, params, datahandler, tester,
time_threshold)
# setting up for training
epoch_nr = 0
start_time = time.time()
num_training_batches = datahandler.get_num_training_batches()
num_test_batches = datahandler.get_num_test_batches()
epoch_loss = 0
# start training
while epoch_nr < MAX_EPOCHS:
