AI::MXNet::Gluon::Parameter - A Container holding parameters (weights) of AI::MXNEt::Gluon::Block(s).
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 $x->grad_req = 'null' saves memory and computation when you don't need gradient w.r.t x.
Initializes parameter and gradient arrays. Only used for `NDArray` API. Parameters ---------- :$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 $weight->data(mx->gpu(1)) [[-0.00873779 -0.02834515] [ 0.05484822 -0.06206018]] <NDArray 2x2 @gpu(1)>
Re-assign Parameter to other contexts. :$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.
Sets this parameter's value on all contexts to 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
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
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
Returns copies of this parameter on all contexts, in the same order as creation. For sparse parameters, use list_row_sparse_data instead.
Returns a gradient buffer for this parameter on one context. Parameters ---------- ctx : Context Desired context.
Returns gradient buffers on all contexts, in the same order as 'values'.
Returns a list of contexts this parameter is initialized on.
Sets gradient buffer on all contexts to 0. No action is taken if parameter is uninitialized or doesn't require gradient.
Returns a symbol representing this parameter.
Cast data and gradient of this Parameter to a new data type. Parameters ---------- $dtype : Dtype The new data type.
AI::MXNet::Gluon::Constant - A constant parameter for holding immutable tensors.
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 package Block; use AI::MXNet::Gluon::Mouse; extends 'AI::MXNet::Gluon::Block'; 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.
AI::MXNet::Gluon::ParameterDict - A dictionary managing a set of parameters.
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`.
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`.
Copies all Parameters in $other to self.
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.
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.
Sets all Parameters' gradient buffer to 0.
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.
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.
Save parameters to file. $filename : str Path to parameter file. $strip_prefix : str, default '' Strip prefix from parameter names before saving.
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.
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.