The example demonstrates the basic usage of the sparse.Embedding operator in MXNet, adapted based on @leopd's recommender examples. This is for demonstration purpose only.
usage: train.py [-h] [--num-epoch NUM_EPOCH] [--seed SEED] [--batch-size BATCH_SIZE] [--log-interval LOG_INTERVAL] [--factor-size FACTOR_SIZE] [--gpus GPUS] [--dense] Run matrix factorization with sparse embedding optional arguments: -h, --help show this help message and exit --num-epoch NUM_EPOCH number of epochs to train (default: 3) --seed SEED random seed (default: 1) --batch-size BATCH_SIZE number of examples per batch (default: 128) --log-interval LOG_INTERVAL logging interval (default: 100) --factor-size FACTOR_SIZE the factor size of the embedding operation (default: 128) --gpus GPUS list of gpus to run, e.g. 0 or 0,2. empty means using cpu(). (default: None) --dense whether to use dense embedding (default: False)