AI::MXNet::Module - FeedForward interface of MXNet. See AI::MXNet::Module::Base for the details.
Create a model from previously saved checkpoint. Parameters ---------- $prefix : Str path prefix of saved model files. You should have "prefix-symbol.json", "prefix-xxxx.params", and optionally "prefix-xxxx.states", where xxxx is the epoch number. $epoch : Int epoch to load. $load_optimizer_states=0 : Bool whether to load optimizer states. Checkpoint needs to have been made with save_optimizer_states=True. :$data_names : array ref of str Default is ['data'] for a typical model used in image classification. :$label_names : array ref of str Default is ['softmax_label'] for a typical model used in image classification. :$logger : Logger Default is AI::MXNet::Logging. :$context : Context or list of Context Default is cpu(0). :$work_load_list : array ref of number Default is undef, indicating an uniform workload. :$fixed_param_names: array ref of str Default is undef, indicating no network parameters are fixed.
Save current progress to a checkpoint. Use mx->callback->module_checkpoint as epoch_end_callback to save during training. Parameters ---------- $prefix : Str The file prefix to checkpoint to $epoch : Int The current epoch number $save_optimizer_states=0 : Bool Whether to save optimizer states for later training
Checkpoint the model data into file. Parameters ---------- $prefix : Str Prefix of model name. $epoch : Int The epoch number of the model. $symbol : AI::MXNet::Symbol The input symbol $arg_params : HashRef[AI::MXNet::NDArray] Model's parameters, hash ref of name to AI::MXNet::NDArray of net's weights. $aux_params : HashRef[AI::MXNet::NDArray] Model's parameters, hash ref of name to AI::MXNet::NDArray of net's auxiliary states. Notes ----- - prefix-symbol.json will be saved for symbol. - prefix-epoch.params will be saved for parameters.
Bind the symbols to construct executors. This is necessary before one can perform computation with the module. Parameters ---------- :$data_shapes : ArrayRef[AI::MXNet::DataDesc|NameShape] Typically is $data_iter->provide_data. :$label_shapes : Maybe[ArrayRef[AI::MXNet::DataDesc|NameShape]] Typically is $data_iter->provide_label. :$for_training : bool Default is 1. Whether the executors should be bind for training. :$inputs_need_grad : bool Default is 0. Whether the gradients to the input data need to be computed. Typically this is not needed. But this might be needed when implementing composition of modules. :$force_rebind : bool Default is 0. This function does nothing if the executors are already binded. But with this 1, the executors will be forced to rebind. :$shared_module : Module Default is undef. This is used in bucketing. When not undef, the shared module essentially corresponds to a different bucket -- a module with different symbol but with the same sets of parameters (e.g. unrolled RNNs with different lengths).
Reshape the module for new input shapes. Parameters ---------- :$data_shapes : ArrayRef[AI::MXNet::DataDesc] Typically is $data_iter->provide_data. :$label_shapes= : Maybe[ArrayRef[AI::MXNet::DataDesc]] Typically is $data_iter->provide_label.
Borrow optimizer from a shared module. Used in bucketing, where exactly the same optimizer (esp. kvstore) is used. Parameters ---------- shared_module : AI::MXNet::Module
Synchronize parameters from devices to CPU. This function should be called after calling 'update' that updates the parameters on the devices, before one can read the latest parameters from $self->_arg_params and $self->_aux_params.
To install AI::MXNet, copy and paste the appropriate command in to your terminal.
cpanm
cpanm AI::MXNet
CPAN shell
perl -MCPAN -e shell install AI::MXNet
For more information on module installation, please visit the detailed CPAN module installation guide.