AI::MXNet::Executor - The actual executing object of MXNet.
my $executor = $sym->bind( ctx => mx->Context('cpu'), args => [$lhs_arr, $rhs_arr], args_grad => [$lhs_grad, $rhs_grad] ); $executor->forward(1); print $executor->outputs->[0]->aspdl;
Constructor, used by AI::MXNet::Symbol->bind and by AI::MXNet::Symbol->simple_bind. Parameters ---------- handle: ExecutorHandle ExecutorHandle is generated by calling bind. See Also -------- AI::MXNet::Symbol->bind : how to create the AI::MXNet::Executor.
The output ndarrays bound to this executor. Returns ------- An array ref with AI::MXNet::NDArray objects bound to the heads of the executor.
Calculate the outputs specified by the bound symbol. Parameters ---------- $is_train=0: Bool, optional whether this forward is for evaluation purpose. If True, a backward call is expected to follow. Otherwise following backward is invalid. %kwargs Additional specification of input arguments. Examples -------- >>> # doing forward by specifying data >>> $texec->forward(1, data => $mydata); >>> # doing forward by not specifying things, but copy to the executor before hand >>> $mydata->copyto($texec->arg_dict->{'data'}); >>> $texec->forward(1); >>> # doing forward by specifying data and get outputs >>> my $outputs = $texec->forward(1, data => $mydata); >>> print $outputs->[0]->aspdl;
Do a backward pass to get the gradient of the arguments. Parameters ---------- $out_grads : NDArray or an array ref of NDArrays or hash ref of NDArrays, optional. The gradient on the outputs to be propagated back. This parameter is only needed when bind is called on outputs that are not a loss function. $is_train : Bool, default 1 Whether this backward is for training or inference. Note that in rare cases you want to call backward with is_train=0 to get gradient during inference.
Install callback. Parameters ---------- $callback : CodeRef Takes a string and an NDArrayHandle.
Get a hash ref representation of the argument arrays. Returns ------- $arg_dict : HashRef[AI::MXNet::NDArray] The map that maps a name of the arguments to the NDArrays.
Get a hash ref representation of the gradient arrays. Returns ------- $grad_dict : HashRef[AI::MXNet::NDArray] The map that maps a name of the arguments to the gradient NDArrays.
Get a hash ref representation of the auxiliary states arrays. Returns ------- $aux_dict : HashRef[AI::MXNet::NDArray] The map that maps a name of the auxiliary states to the NDArrays.
Get a hash ref representation of the output arrays. Returns ------- $output_dict : HashRef[AI::MXNet::NDArray] The map that maps a name of the outputs to the NDArrays.
Copy parameters from arg_params, aux_params into the executor's internal array. Parameters ---------- $arg_params : HashRef[AI::MXNet::NDArray] Parameters, hash ref of name to NDArray of arguments $aux_params= : Maybe[HashRef[AI::MXNet::NDArray]], optional Parameters, hash ref of name to NDArray of auxiliary states. $allow_extra_params= : Bool, optional Whether to allow extra parameters that are not needed by symbol If this is True, no error will be thrown when arg_params or aux_params contain extra parameters that is not needed by the executor.
Returns new executor with the same symbol and shared memory, but different input/output shapes. For runtime reshaping, variable length sequences, etc. The returned executor shares state with the current one, and cannot be used in parallel with it. Parameters ---------- $kwargs : HashRef[Shape] new shape for arguments. :$partial_shaping : Bool Whether to allow changing the shape of unspecified arguments. :$allow_up_sizing : Bool Whether to allow allocating new ndarrays that's larger than the original. Returns ------- $exec : AI::MXNet::Executor A new executor that shares memory with self.
A debug string about the internal execution plan. Returns ------- $debug_str : Str Debug string of the executor.
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.