chainer.links.Inception

class chainer.links.Inception(in_channels, out1, proj3, out3, proj5, out5, proj_pool, conv_init=None, bias_init=None)

Inception module of GoogLeNet.

It applies four different functions to the input array and concatenates their outputs along the channel dimension. Three of them are 2D convolutions of sizes 1x1, 3x3 and 5x5. Convolution paths of 3x3 and 5x5 sizes have 1x1 convolutions (called projections) ahead of them. The other path consists of 1x1 convolution (projection) and 3x3 max pooling.

The output array has the same spatial size as the input. In order to satisfy this, Inception module uses appropriate padding for each convolution and pooling.

See: Going Deeper with Convolutions.

Parameters:

• in_channels (int or None) – Number of channels of input arrays.
• out1 (int) – Output size of 1x1 convolution path.
• proj3 (int) – Projection size of 3x3 convolution path.
• out3 (int) – Output size of 3x3 convolution path.
• proj5 (int) – Projection size of 5x5 convolution path.
• out5 (int) – Output size of 5x5 convolution path.
• proj_pool (int) – Projection size of max pooling path.
• conv_init – A callable that takes numpy.ndarray or cupy.ndarray and edits its value. It is used for initialization of the convolution matrix weights. Maybe be None to use default initialization.
• bias_init – A callable that takes numpy.ndarray or cupy.ndarray and edits its value. It is used for initialization of the convolution bias weights. Maybe be None to use default initialization.

Methods

__call__(x)

Computes the output of the Inception module.

Parameters:x (Variable) – Input variable. Returns:Output variable. Its array has the same spatial size and the same minibatch size as the input array. The channel dimension has size out1 + out3 + out5 + proj_pool. Return type:Variable

__getitem__(name)

Equivalent to getattr.

add_link(name, link)

Registers a child link to this chain.

Deprecated since version v2.0.0: Assign the child link directly to an attribute within an initialization scope, instead. For example, the following code

chain.add_link('l1', L.Linear(3, 5))

can be replaced by the following line.

with self.init_scope():
    chain.l1 = L.Linear(3, 5)

The latter one is easier for IDEs to keep track of the attribute’s type.

Parameters:

• name (str) – Name of the child link. This name is also used as the attribute name.
• link (Link) – The link object to be registered.

add_param(name, shape=None, dtype=<type 'numpy.float32'>, initializer=None)

Registers a parameter to the link.

Deprecated since version v2.0.0: Assign a Parameter object directly to an attribute within an initialization scope instead. For example, the following code

link.add_param('W', shape=(5, 3))

can be replaced by the following assignment.

with self.init_scope():
    link.W = chainer.Parameter(None, (5, 3))

The latter one 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.

add_persistent(name, value)

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.

addgrads(link)

children()

cleargrads()

Clears all gradient arrays.

This method should be called before the backward computation at every iteration of the optimization.

copy()

copyparams(link)

disable_update()

Disables update rules of all parameters under the link hierarchy.

This method sets the :attr:~chainer.UpdateRule.enabled` flag of the update rule of each parameter variable to False.

enable_update()

Enables update rules of all parameters under the link hierarchy.

This method sets the enabled flag of the update rule of each parameter variable to True.

init_scope(*args, **kwds)

Creates an initialization scope.

This method returns a context manager object that enables registration of parameters (and links for Chain) by an assignment. A Parameter 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 a Parameter 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,))

links(skipself=False)

namedlinks(skipself=False)

namedparams(include_uninit=True)

params(include_uninit=True)

register_persistent(name)

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.

serialize(serializer)

to_cpu()

to_gpu(device=None)

zerograds()

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.

xp

Array module for this link.

Depending on which of CPU/GPU this link is on, this property returns numpy or cupy.