NAME
AI::MXNet::Gluon::Parameter - A Container holding parameters (weights) of AI::MXNEt::Gluon::Block(s).
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DESCRIPTION
AI::MXNet::Gluon::Parameter holds a copy of the parameter on each AI::MXNet::Context after
it is initialized with AI::MXNet::Gluon::Parameter->initialize(...)`. If grad_req is
not 'null', it will also hold a gradient array on each AI::MXNet::Context
$ctx = mx->gpu(0);
$x = mx->nd->zeros([16, 100], ctx=>$ctx);
$w = mx->gluon->Parameter('fc_weight', shape=>[64, 100], init=>mx->init->Xavier());
$b = mx->gluon->Parameter('fc_bias', shape=>[64], init=>mx->init->Zero());
$w->initialize(ctx=>$ctx);
$b->initialize(ctx=>ctx);
$out = mx->nd->FullyConnected($x, $w->data($ctx), $b->data($ctx), num_hidden=>64);
Parameters
----------
name : str
Name of this parameter.
grad_req : {'write', 'add', 'null'}, default 'write'
Specifies how to update gradient to grad arrays.
- 'write' means everytime gradient is written to grad NDArray.
- 'add' means everytime gradient is added to the grad NDArray. You need
to manually call zero_grad() to clear the gradient buffer before each
iteration when using this option.
- 'null' means gradient is not requested for this parameter. gradient arrays
will not be allocated.
shape : array ref of int or int, default undef
Shape of this parameter. By default shape is not specified. Parameter with
unknown shape can be used for `Symbol` API, but `init` will throw an error
when using `NDArray` API.
dtype : Dtype, default 'float32'
Data type of this parameter. For example, 'float64'.
lr_mult : float, default 1.0
Learning rate multiplier. Learning rate will be multiplied by lr_mult
when updating this parameter with optimizer.
wd_mult : float, default 1.0
Weight decay multiplier (L2 regularizer coefficient). Works similar to lr_mult.
init : Initializer, default None
Initializer of this parameter. Will use the global initializer by default.stype: {'default', 'row_sparse', 'csr'}, defaults to 'default'.
The storage type of the parameter.
grad_stype: {'default', 'row_sparse', 'csr'}, defaults to 'default'.
The storage type of the parameter's gradient.
Attributes
----------
grad_req : {'write', 'add', 'null'}
This can be set before or after initialization. Setting grad_req to null
with $x->grad_req = 'null' saves memory and computation when you don't
need gradient w.r.t x.
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initialize
Initializes parameter and gradient arrays. Only used for `NDArray` API.
Parameters
----------
:$init : Initializer
The initializer to use. Overrides AI::MXNet::Gluon::Parameter->init and default_init.
:$ctx : AI::MXNet::Context or array ref of AI::MXNet::Context, defaults to AI::MXNet::Context->current_ctx().
Initialize Parameter on given context. If ctx is a list of Context, a
copy will be made for each context.
Copies are independent arrays. User is responsible for keeping
their values consistent when updating. Normally gluon->Trainer does this for you.
:$default_init : Initializer
Default initializer is used when both 'init' and AI::MXNet::Gluon::Parameter->init are undefined.
:$force_reinit : bool, default False
Whether to force re-initialization if parameter is already initialized.
Examples
--------
>>> $weight = mx->gluon->Parameter('weight', shape=>[2, 2]);
>>> $weight->initialize(ctx=>mx->cpu(0));
>>> print $weight->data
[[-0.01068833 0.01729892]
[ 0.02042518 -0.01618656]]
<NDArray 2x2 @cpu(0)>
>>> print $weight->grad()
[[ 0. 0.]
[ 0. 0.]]
<NDArray 2x2 @cpu(0)>
>>> $weight->initialize(ctx=>[mx->gpu(0), mx->gpu(1)]);
>>> print $weight->data(mx->gpu(0));
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(0)>
>>> print $weight->data(mx->gpu(1))
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(1)>
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reset_ctx
Re-assign Parameter to other contexts.
:$ctx : AI::MXNet::Context or array ref of AI::MXNet::Context, default AI::MXNet::Context->current_ctx.
Assign Parameter to given context. If ctx is a list of Context, a
copy will be made for each context.
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set_data
Sets this parameter's value on all contexts to data.
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row_sparse_data
Returns a copy of the 'row_sparse' parameter on the same context as row_id's.
The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized on this context before.
Parameters
----------
$row_id: AI::MXNet::NDArray
Row ids to retain for the 'row_sparse' parameter.
Returns
-------
AI::MXNet::NDArray on row_id's context
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list_row_sparse_data
Returns copies of the 'row_sparse' parameter on all contexts, in the same order
as creation. The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized before.
Parameters
----------
$row_id: AI::MXNet::NDArray
Row ids to retain for the 'row_sparse' parameter.
Returns
-------
array ref of AI::MXNet::NDArrays
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data
Returns a copy of this parameter on one context. Must have been
initialized on this context before. For sparse parameters, use
row_sparse_data instead.
Parameters
----------
ctx : Context
Desired context.
Returns
-------
NDArray on ctx
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list_data
Returns copies of this parameter on all contexts, in the same order
instead.
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grad
Returns a gradient buffer for this parameter on one context.
Parameters
----------
ctx : Context
Desired context.
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list_grad
Returns gradient buffers on all contexts, in the same order
as 'values'.
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list_ctx
Returns a list of contexts this parameter is initialized on.
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zero_grad
Sets gradient buffer on all contexts to 0. No action is taken if
parameter is uninitialized or doesn't require gradient. |
var
Returns a symbol representing this parameter.
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cast
Cast data and gradient of this Parameter to a new data type.
Parameters
----------
$dtype : Dtype
The new data type.
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NAME
AI::MXNet::Gluon::Constant - A constant parameter for holding immutable tensors.
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DESCRIPTION
A constant parameter for holding immutable tensors.
Constants are ignored by autograd and Trainer, thus their values
will not change during training. But you can still update their values
manually with the set_data method.
Constants can be created with either
$const = mx->gluon->Constant('const', [[1,2],[3,4]]);
or
sub BUILD
{
$self->const($self->params->get_constant('const', [[1,2],[3,4]]));
}
Constructor Attributes
----------
name : str
Name of the parameter.
value : AcceptableInput (perl array, pdl, ndarray, etc)
Initial value for the constant.
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NAME
AI::MXNet::Gluon::ParameterDict - A dictionary managing a set of parameters.
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DESCRIPTION
Parameters
----------
prefix : str, default ''
The prefix to be prepended to all Parameters' names created by this dict.
shared : ParameterDict or undef
If not undef, when this dict's `get` method creates a new parameter, will
first try to retrieve it from `shared` dict. Usually used for sharing
parameters with another `Block`.
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get
Retrieves a 'AI::MXNet::Gluon::Parameter' with name '$self->prefix.$name'. If not found,
'get' will first try to retrieve it from 'shared' dict. If still not
found, 'get' will create a new 'AI::MXNet::Gluon::Parameter' with key-word arguments and
insert it to self.
Parameters
----------
name : str
Name of the desired Parameter. It will be prepended with this dictionary's
prefix.
%kwargs : hash
The rest of key-word arguments for the created `Parameter`.
Returns
-------
Parameter
The created or retrieved `Parameter`.
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update
Copies all Parameters in $other to self.
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get_constant
Retrieves AI::MXNet::Gluon::Constant with name $self->prefix.$name. If not found,
'get' will first try to retrieve it from "shared" dictionary. If still not
found, 'get' will create a new Constant with key-word
arguments and insert it to self.
Parameters
----------
name : str
Name of the desired Constant. It will be prepended with this dictionary's
prefix.
value : array-like
Initial value of constant.
Returns
-------
Constant
The created or retrieved Constant.
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initialize
Initializes all Parameters managed by this dictionary to be used for 'NDArray'
API. It has no effect when using 'Symbol' API.
Parameters
----------
:$init : Initializer
Global default Initializer to be used when AI::MXNet::Gluon::Parameter->init is undef.
Otherwise, AI::MXNet::Gluon::Parameter->init takes precedence.
:$ctx : AI::MXNet::Context or array ref of AI::MXNet::Context objects
Keeps a copy of Parameters on one or many context(s).
:$force_reinit : bool, default False
Whether to force re-initialization if parameter is already initialized.
:$verbose : bool, default False
Whether to force re-initialization if parameter is already initialized.
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zero_grad
Sets all Parameters' gradient buffer to 0.
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reset_ctx
Re-assign all Parameters to other contexts.
$ctx : AI::MXNet::Context or array ref of AI::MXNet::Context objects, defaults to AI::MXNet::Context->current_ctx().
Assign Parameter to given context. If $ctx is an array ref of AI::MXNet::Context objects, a
copy will be made for each context.
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setattr
Set an attribute to a new value for all Parameters.
For example, set grad_req to null if you don't need gradient w.r.t a
model's Parameters::
$model->collect_params()->setattr(grad_req => 'null');
or change the learning rate multiplier::
$model->collect_params()->setattr(lr_mult => 0.5);
Parameters
----------
$name : str
Name of the attribute.
$value : valid type for attribute name
The new value for the attribute.
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save
Save parameters to file.
$filename : str
Path to parameter file.
$strip_prefix : str, default ''
Strip prefix from parameter names before saving.
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Load parameters from file.
$filename : str
Path to parameter file.
:$ctx : AI::MXNet::Context or array ref of AI::MXNet::Context objects
Context(s) initialize loaded parameters on.
:$allow_missing : bool, default False
Whether to silently skip loading parameters not represents in the file.
:$ignore_extra : bool, default False
Whether to silently ignore parameters from the file that are not
present in this ParameterDict.
:$restore_prefix : str, default ''
prepend prefix to names of stored parameters before loading.
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