| # Module API |
| |
| ## Overview |
| |
| The module API, defined in the `module` (or simply `mod`) package (`AI::MXNet::Module` under the hood), provides an |
| intermediate and high-level interface for performing computation with a |
| `AI::MXNet::Symbol` or just `mx->sym`. One can roughly think a module is a machine which can execute a |
| program defined by a `Symbol`. |
| |
| The class `AI::MXNet::Module` is a commonly used module, which accepts a `AI::MXNet::Symbol` as |
| the input: |
| |
| ```perl |
| pdl> $data = mx->symbol->Variable('data') |
| pdl> $fc1 = mx->symbol->FullyConnected($data, name=>'fc1', num_hidden=>128) |
| pdl> $act1 = mx->symbol->Activation($fc1, name=>'relu1', act_type=>"relu") |
| pdl> $fc2 = mx->symbol->FullyConnected($act1, name=>'fc2', num_hidden=>10) |
| pdl> $out = mx->symbol->SoftmaxOutput($fc2, name => 'softmax') |
| pdl> $mod = mx->mod->Module($out) # create a module by given a Symbol |
| ``` |
| |
| Assume there is a valid MXNet data iterator `data`. We can initialize the |
| module: |
| |
| ```perl |
| pdl> $mod->bind(data_shapes=>$data->provide_data, |
| label_shapes=>$data->provide_label) # create memory by given input shapes |
| pdl> $mod->init_params() # initial parameters with the default random initializer |
| ``` |
| |
| Now the module is able to compute. We can call high-level API to train and |
| predict: |
| |
| ```perl |
| pdl> $mod->fit($data, num_epoch=>10, ...) # train |
| pdl> $mod->predict($new_data) # predict on new data |
| ``` |
| |
| or use intermediate APIs to perform step-by-step computations |
| |
| ```perl |
| pdl> $mod->forward($data_batch, is_train => 1) # forward on the provided data batch |
| pdl> $mod->backward() # backward to calculate the gradients |
| pdl> $mod->update() # update parameters using the default optimizer |
| ``` |
