| Matrix Factorization w/ Sparse Embedding |
| =========== |
| The example demonstrates the basic usage of the SparseEmbedding 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) |
| ``` |