NAME
AI::MXNet::Gluon::Block - Base class for all neural network layers and models.
DESCRIPTION
Base class for all neural network layers and models. Your models should
subclass this class.
AI::MXNet::Gluon::Block can be nested recursively in a tree structure. You can create and
assign child AI::MXNet::Gluon::Block as regular attributes
use AI::MXNet::Gluon::NN qw(nn);
use AI::MXNet qw(mx);
package Model;
use AI::MXNet::Gluon::Mouse;
use AI::MXNet::Function::Parameters;
extends 'AI::MXNet::Gluon::Block';
sub BUILD
{
my $self = shift;
$self->name_scope(sub {
$self->dense0(nn->Dense(5, in_units=>5));
$self->dense1(nn->Dense(5, in_units=>5));
});
}
method forward($x)
{
return $self->dense1->($self->dense0->($x));
}
my $model = Model->new()
$model->initialize(ctx=>mx->cpu(0))
$model->(nd->zeros([10, 10], ctx=>mx->cpu(0)));
Child AI::MXNet::Gluon::Block assigned this way will be registered and ->collect_params
will collect their Parameters recursively.
Parameters
----------
Prefix acts like a name space. All children blocks created in parent block's
name_scope will have parent block's prefix in their name.
Please refer to
naming tutorial http://mxnet.incubator.apache.org/tutorials/gluon/naming.html
for more info on prefix and naming.
params : AI::MXNet::Gluon::ParameterDict or undef
AI::MXNet::Gluon::ParameterDict for sharing weights with the new AI::MXNet::Gluon::Block. For example,
if you want `dense1` to share `dense0`'s weights, you can do
$dense0 = nn->Dense(20);
$dense1 = nn->Dense(20, params=>dense0->collect_params());
params
Returns this `Block`'s parameter dictionary (does not include its
children's parameters).
collect_params
Returns a AI::MXNet::Gluon::ParameterDict containing this AI::MXNet::Gluon::Block and all of its
children's Parameters(default), also can returns the ParameterDict
with parameters that match a regular expression.
For example, collects parameters specified in ['conv1_weight', 'conv1_bias', 'fc_weight',
'fc_bias'
$model->collect_params('conv1_weight|conv1_bias|fc_weight|fc_bias')
or collects all parameters that have the name end with 'weight' or 'bias', this can be done
using regular expressions.
$model->collect_params('.*weight|.*bias')
save_parameters
Save parameters to file.
filename : str
Path to file.
load_parameters
Load parameters from file.
$filename : str
Path to parameter file.
:$ctx= : Context or list of Context
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 Block.
register_child
Registers block as a child of self. `Block`s assigned to self as
attributes will be registered automatically.
register_forward_pre_hook
Registers a forward pre-hook on the block.
The hook function is called immediately before 'forward'.
It should not modify the input or output.
Parameters
----------
$hook : CodeRef or callable object
The forward hook function of form $hook->($block, $input).
Returns
-------
AI::MXNet::Gluon::Utils::HookHandle
register_forward_hook
Registers a forward hook on the block.
The hook function is called immediately after 'forward'.
It should not modify the input or output.
Parameters
----------
$hook : CodeRef or callable object
The forward hook function of form $hook->($block, $input).
Returns
-------
AI::MXNet::Gluon::Utils::HookHandle
apply
Applies $fn recursively to every child block as well as self.
Parameters
----------
$fn : callable
Function to be applied to each submodule, of form `$fn->($block)`.
Returns
-------
this block
initialize
Initializes AI::MXNet::Gluon::Parameters of this AI::MXNet::Gluon::Block and its children.
Equivalent to $block->collect_params()->initialize(...)
Parameters
----------
$init : Initializer
Global default Initializer to be used when Parameter->init is undefined`.
Otherwise, Parameter->init takes precedence.
ctx : Context or array ref of Context
Keeps a copy of Parameters on one or many context(s).
verbose : bool, default False
Whether to verbosely print out details on initialization.
force_reinit : bool, default False
Whether to force re-initialization if parameter is already initialized.
hybridize
Activates or deactivates `HybridBlock`s recursively. Has no effect on
non-hybrid children.
Parameters
----------
$active : bool, default True
Whether to turn hybrid on or off.
:$static_alloc : bool, default False
Statically allocate memory to improve speed. Memory usage may increase.
:$static_shape : bool, default False
Optimize for invariant input shapes between iterations. Must also
set static_alloc to True. Change of input shapes is still allowed
but slower.
cast
Cast this Block to use another data type.
Parameters
----------
dtype : Dtype
The new data type.
forward
Overrides to implement forward computation using `NDArray`. Only
accepts positional arguments.
Parameters
----------
@args : array of NDArray
Input tensors.
NAME
AI::MXNet::Gluon::HybridBlock
DESCRIPTION
`HybridBlock` supports forwarding with both Symbol and NDArray.
Forward computation in `HybridBlock` must be static to work with `Symbol`s,
i.e. you cannot call `.asnumpy()`, `.shape`, `.dtype`, etc on tensors.
Also, you cannot use branching or loop logic that bases on non-constant
expressions like random numbers or intermediate results, since they change
the graph structure for each iteration.
Before activating with `hybridize()`, `HybridBlock` works just like normal
`Block`. After activation, `HybridBlock` will create a symbolic graph
representing the forward computation and cache it. On subsequent forwards,
the cached graph will be used instead of `hybrid_forward`.
Refer `Hybrid tutorial <http://mxnet.io/tutorials/gluon/hybrid.html>`_ to see
the end-to-end usage.
infer_shape
Infers shape of Parameters from inputs.
forward
Defines the forward computation. Arguments can be either
`NDArray` or `Symbol`.
hybrid_forward
Overrides to construct symbolic graph for this `Block`.
Parameters
----------
x : Symbol or NDArray
The first input tensor.
*args : list of Symbol or list of NDArray
Additional input tensors.
export
Export HybridBlock to json format that can be loaded by AI::MXNet::Module
or the C++ interface.
When there are only one input, it will have name 'data'. When there
Are more than one inputs, they will be named as `data0`, `data1`, etc.
Parameters
----------
$path : str
Path to save model. Two files `path-symbol.json` and `path-xxxx.params`
will be created, where xxxx is the 4 digits epoch number.
:$epoch=0 : Int
Epoch number of saved model.
NAME
AI::MXNet::Gluon::SymbolBlock - Construct block from symbol.
DESCRIPTION
Construct block from symbol. This is useful for using pre-trained models
as feature extractors. For example, you may want to extract get the output
from fc2 layer in AlexNet.
Parameters
----------
outputs : Symbol or list of Symbol
The desired output for SymbolBlock.
inputs : Symbol or list of Symbol
The Variables in output's argument that should be used as inputs.
params : ParameterDict
Parameter dictionary for arguments and auxililary states of outputs
that are not inputs.
Examples
--------
>>> # To extract the feature from fc1 and fc2 layers of AlexNet:
>>> alexnet = gluon.model_zoo.vision.alexnet(pretrained=True, ctx=mx.cpu(),
prefix='model_')
>>> inputs = mx.sym.var('data')
>>> out = alexnet(inputs)
>>> internals = out.get_internals()
>>> print(internals.list_outputs())
['data', ..., 'model_dense0_relu_fwd_output', ..., 'model_dense1_relu_fwd_output', ...]
>>> outputs = [internals['model_dense0_relu_fwd_output'],
internals['model_dense1_relu_fwd_output']]
>>> # Create SymbolBlock that shares parameters with alexnet
>>> feat_model = gluon.SymbolBlock(outputs, inputs, params=alexnet.collect_params())
>>> x = mx.nd.random_normal(shape=(16, 3, 224, 224))
>>> print(feat_model(x))
imports
Import model previously saved by HybridBlock->export or
Module->save_checkpoint as a SymbolBlock for use in Gluon.
Parameters
----------
$symbol_file : Str
Path to symbol file.
$input_names : Str|ArrayRef[Str]
List of input variable names
:$param_file : Str, optional
Path to parameter file.
$ctx : Context, default undef
The context to initialize SymbolBlock on.
Returns
-------
SymbolBlock
SymbolBlock loaded from symbol and parameter files.