NAME
AI::MXNet::AutoGrad - Autograd for NDArray.
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DESCRIPTION
Auto gradients differentiation for dynamic graphs, primarily used with Gluon.
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SYNOPSIS
my $x = mx->nd->ones([1]);
$x->attach_grad;
my $z;
mx->autograd->record(sub {
$z = mx->nd->elemwise_add($x->exp, $x);
});
my $dx = mx->autograd->grad($z, $x, create_graph=>1);
ok(abs($dx->asscalar - 3.71828175) < 1e-7);
$dx->backward;
ok(abs($x->grad->asscalar - 2.71828175) < 1e-7);
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set_is_training
Set status to training/not training. When training, graph will be constructed
for gradient computation. Operators will also run with $is_train=1. For example,
Dropout will drop inputs randomly when is_train=True while simply passing through
if $is_train=0.
Parameters
----------
$is_train: Bool
Returns
-------
previous state before this set.
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set_is_recording
Set status to recording/not recording. When recording, graph will be constructed
for gradient computation.
Parameters
----------
$is_recoding: Bool
Returns
-------
previous state before this set.
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is_recording
Get status on recording/not recording.
Returns
-------
Current state of recording.
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is_training
Get status on training/predicting.
Returns
-------
Current state of training/predicting.
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mark_variables
Mark AI::MXNet::NDArrays as variables to compute gradient for autograd.
Parameters
----------
ArrayRef[AI::MXNet::NDArray] $variables
ArrayRef[AI::MXNet::NDArray] $gradients
GradReq|ArrayRef[GradReq] :$grad_reqs='write'
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backward
Compute the gradients of heads w.r.t previously marked variables.
Parameters
----------
$heads: ArrayRef[AI::MXNet::NDArray]
Output NDArray(s)
:$head_grads=: Maybe[AI::MXNet::NDArray|ArrayRef[AI::MXNet::NDArray|Undef]]
Gradients with respect to heads.
:$retain_graph=0: Bool, optional
Whether to retain graph.
:$train_mode=1: Bool, optional
Whether to do backward for training or predicting.
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compute_gradient
Compute the gradients of outputs w.r.t variables.
Parameters
----------
outputs: ArrayRef[AI::MXNet::NDArray]
Returns
-------
gradients: ArrayRef[AI::MXNet::NDArray]
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grad_and_loss
Return function that computes both gradient of arguments and loss value.
Parameters
----------
$func: CodeRef
The forward (loss) function.
$argnum: Maybe[Int|ArrayRef[Int]]
The index of argument to calculate gradient for.
Returns
-------
grad_and_loss_func: CodeRef
A function that would compute both the gradient of arguments and loss value.
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grad
Compute the gradients of heads w.r.t variables. Gradients will be
returned as new NDArrays instead of stored into `variable.grad`.
Supports recording gradient graph for computing higher order gradients.
Note: Currently only a very limited set of operators support higher order
gradients.
Parameters
----------
$heads: AI::MXNet::NDArray|ArrayRef[AI::MXNet::NDArray]
Output NDArray(s)
$variables: AI::MXNet::NDArray|ArrayRef[AI::MXNet::NDArray]
Input variables to compute gradients for.
:$head_grads=: Maybe[AI::MXNet::NDArray|ArrayRef[AI::MXNet::NDArray|Undef]]
Gradients with respect to heads.
:$retain_graph=: Bool
Whether to keep computation graph to differentiate again, instead
of clearing history and release memory. Defaults to the same value
as create_graph.
:$create_graph=0: Bool
Whether to record gradient graph for computing of higher order gradients.
$train_mode=1: Bool, optional
Whether to do backward for training or prediction.
Returns
-------
AI::MXNet::NDArray|ArrayRef[AI::MXNet::NDArray]:
Gradients with respect to variables.
Examples
--------
>>> $x = mx->nd->ones([1]);
>>> $x->attach_grad();
>>> mx->autograd->record(sub {
$z = mx->nd->elemwise_add(mx->nd->exp($x), $x);
});
>>> $dx = mx->autograd->grad($z, [$x], create_graph=>1)
>>> $dx->backward();
>>> print($dx->grad->aspdl)
[3.71828175]
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train_mode
Executes $sub within an autograd training scope context.
Parameters
----------
$sub: CodeRef
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predict_mode
Executes $sub within an autograd predicting scope context.
Parameters
----------
$sub: CodeRef
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record
Executes $sub within an autograd recording scope context
and captures code that needs gradients to be calculated.
Parameters
----------
$sub: CodeRef
:$train_mode=1 : Maybe[Bool]
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pause
Executes $sub within an autograd recording scope context
and captures code that needs gradients to be calculated.
Parameters
----------
$sub: CodeRef
:$train_mode=0 : Maybe[Bool]
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get_symbol
Retrieve recorded computation history as `Symbol`.
Parameters
----------
$x : AI::MXNet::NDArray
AI::MXNet::NDArray representing the head of computation graph.
Returns
-------
AI::MXNet::Symbol
The retrieved Symbol.
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