chainer.Link¶
-
class
chainer.
Link
(**params)[source]¶ Building block of model definitions.
Link is a building block of neural network models that support various features like handling parameters, defining network fragments, serialization, etc.
Link is the primitive structure for the model definitions. It supports management of parameter variables and persistent values that should be incorporated to serialization.
Parameter is an instance of
Parameter
registered to a link. AParameter
object can be registered as a parameter of the link by assigning it to an attribute within an initialization scope, which is a code surrounded by ainit_scope()
context manager using thewith
statement.Persistent values are arrays, scalars, or any other serializable values registered via
register_persistent()
oradd_persistent()
.Note
Whereas arbitrary serializable objects can be registered as persistent values, it is strongly recommended to just register values that should be treated as results of learning. A typical example of persistent values is ones computed during training and required for testing, e.g. running statistics for batch normalization.
Parameters and persistent values are referred by their names. They can be accessed as attributes of the links. Link class itself manages the lists of names of parameters and persistent values to distinguish parameters and persistent values from other attributes.
Link can be composed into more complex models. This composition feature is supported by child classes like
Chain
andChainList
. One can create a chain by combining one or more links. See the documents for these classes for details.As noted above, Link supports the serialization protocol of the
Serializer
class. Note that only parameters and persistent values are saved and loaded. Other attributes are considered as a part of user program (i.e. a part of network definition). In order to construct a link from saved file, other attributes must be identically reconstructed by user codes.Example
This is a simple example of custom link definition. Chainer itself also provides many links defined under the
links
module. They might serve as examples, too.Consider we want to define a simple primitive link that implements a fully-connected layer based on the
linear()
function. Note that this function takes input units, a weight variable, and a bias variable as arguments. Then, the fully-connected layer can be defined as follows:import chainer import chainer.functions as F from chainer import initializers import numpy as np class LinearLayer(chainer.Link): def __init__(self, n_in, n_out): super(LinearLayer, self).__init__() with self.init_scope(): self.W = chainer.Parameter( initializers.Normal(), (n_out, n_in)) self.b = chainer.Parameter( initializers.Zero(), (n_out,)) def __call__(self, x): return F.linear(x, self.W, self.b)
This example shows that a user can define arbitrary parameters and use them in any methods. Links typically implement the
__call__
operator, although they can also provide other methods to implement the forward propagation.Parameters: params – (deprecated since v2.0.0) Names, shapes, and optional dtypes of initial parameters. The keywords are used as the parameter names and the corresponding values consist either of the shape or a tuple of shape and a dtype (shape, dtype)
. If only the shape is supplied, the default dtype will be used.Variables: name (str) – Name of this link, given by the parent chain (if exists). Methods
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add_param
(name, shape=None, dtype=<class 'numpy.float32'>, initializer=None)[source]¶ Registers a parameter to the link.
Deprecated since version v2.0.0: Assign a
Parameter
object directly to an attribute withininit_scope()
instead. For example, the following codelink.add_param('W', shape=(5, 3))
can be replaced by the following assignment.
with link.init_scope(): link.W = chainer.Parameter(None, (5, 3))
The latter is easier for IDEs to keep track of the attribute’s type.
Parameters: - name (str) – Name of the parameter. This name is also used as the attribute name.
- shape (int or tuple of ints) – Shape of the parameter array. If it is omitted, the parameter variable is left uninitialized.
- dtype – Data type of the parameter array.
- initializer – If it is not
None
, the data is initialized with the given initializer. If it is an array, the data is directly initialized by it. If it is callable, it is used as a weight initializer. Note that in these cases,dtype
argument is ignored.
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add_persistent
(name, value)[source]¶ Registers a persistent value to the link.
The registered value is saved and loaded on serialization and deserialization. The value is set to an attribute of the link.
Parameters: - name (str) – Name of the persistent value. This name is also used for the attribute name.
- value – Value to be registered.
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addgrads
(link)[source]¶ Accumulates gradient values from given link.
This method adds each gradient array of the given link to corresponding gradient array of this link. The accumulation is even done across host and different devices.
Parameters: link (Link) – Source link object.
-
children
()[source]¶ Returns a generator of all child links.
Returns: A generator object that generates all child links.
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cleargrads
()[source]¶ Clears all gradient arrays.
This method should be called before the backward computation at every iteration of the optimization.
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copy
()[source]¶ Copies the link hierarchy to new one.
The whole hierarchy rooted by this link is copied. The copy is basically shallow, except that the parameter variables are also shallowly copied. It means that the parameter variables of copied one are different from ones of original link, while they share the data and gradient arrays.
The name of the link is reset on the copy, since the copied instance does not belong to the original parent chain (even if exists).
Returns: Copied link object. Return type: Link
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copyparams
(link)[source]¶ Copies all parameters from given link.
This method copies data arrays of all parameters in the hierarchy. The copy is even done across the host and devices. Note that this method does not copy the gradient arrays.
Parameters: link (Link) – Source link object.
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disable_update
()[source]¶ Disables update rules of all parameters under the link hierarchy.
This method sets the
enabled
flag of the update rule of each parameter variable toFalse
.
-
enable_update
()[source]¶ Enables update rules of all parameters under the link hierarchy.
This method sets the
enabled
flag of the update rule of each parameter variable toTrue
.
-
init_scope
()[source]¶ Creates an initialization scope.
This method returns a context manager object that enables registration of parameters (and links for
Chain
) by an assignment. AParameter
object can be automatically registered by assigning it to an attribute under this context manager.Example
In most cases, the parameter registration is done in the initializer method. Using the
init_scope
method, we can simply assign aParameter
object to register it to the link.class MyLink(chainer.Link): def __init__(self): super().__init__() with self.init_scope(): self.W = chainer.Parameter(0, (10, 5)) self.b = chainer.Parameter(0, (5,))
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links
(skipself=False)[source]¶ Returns a generator of all links under the hierarchy.
Parameters: skipself (bool) – If True
, then the generator skips this link and starts with the first child link.Returns: A generator object that generates all links.
-
namedlinks
(skipself=False)[source]¶ Returns a generator of all (path, link) pairs under the hierarchy.
Parameters: skipself (bool) – If True
, then the generator skips this link and starts with the first child link.Returns: A generator object that generates all (path, link) pairs.
-
namedparams
(include_uninit=True)[source]¶ Returns a generator of all (path, param) pairs under the hierarchy.
Parameters: include_uninit (bool) – If True
, it also generates uninitialized parameters.Returns: A generator object that generates all (path, parameter) pairs. The paths are relative from this link.
-
params
(include_uninit=True)[source]¶ Returns a generator of all parameters under the link hierarchy.
Parameters: include_uninit (bool) – If True
, it also generates uninitialized parameters.Returns: A generator object that generates all parameters.
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register_persistent
(name)[source]¶ Registers an attribute of a given name as a persistent value.
This is a convenient method to register an existing attribute as a persistent value. If
name
has been already registered as a parameter, this method removes it from the list of parameter names and re-registers it as a persistent value.Parameters: name (str) – Name of the attribute to be registered.
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serialize
(serializer)[source]¶ Serializes the link object.
Parameters: serializer (AbstractSerializer) – Serializer object.
-
to_cpu
()[source]¶ Copies parameter variables and persistent values to CPU.
This method does not handle non-registered attributes. If some of such attributes must be copied to CPU, the link implementation must override this method to do so.
Returns: self
-
to_gpu
(device=None)[source]¶ Copies parameter variables and persistent values to GPU.
This method does not handle non-registered attributes. If some of such attributes must be copied to GPU, the link implementation must override this method to do so.
Parameters: device – Target device specifier. If omitted, the current device is used. Returns: self
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zerograds
()[source]¶ Initializes all gradient arrays by zero.
This method can be used for the same purpose of cleargrads, but less efficient. This method is left for backward compatibility.
Deprecated since version v1.15: Use
cleargrads()
instead.
Attributes
-
update_enabled
¶ True
if at least one parameter has an update rule enabled.
-
within_init_scope
¶ True if the current code is inside of an initialization scope.
See
init_scope()
for the details of the initialization scope.
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