Module API


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:

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:

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:

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

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