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:
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