chainer.links.MLPConvolution2D

class chainer.links.MLPConvolution2D(self, in_channels, out_channels, ksize=None, stride=1, pad=0, activation=relu.relu, conv_init=None, bias_init=None)

Two-dimensional MLP convolution layer of Network in Network.

This is an “mlpconv” layer from the Network in Network paper. This layer is a two-dimensional convolution layer followed by 1x1 convolution layers and interleaved activation functions.

Note that it does not apply the activation function to the output of the last 1x1 convolution layer.

Parameters

• in_channels (int or None) – Number of channels of input arrays. If it is None or omitted, parameter initialization will be deferred until the first forward data pass at which time the size will be determined.

• out_channels (tuple of ints) – Tuple of number of channels. The i-th integer indicates the number of filters of the i-th convolution.

• ksize (int or pair of ints) – Size of filters (a.k.a. kernels) of the first convolution layer. ksize=k and ksize=(k, k) are equivalent.

• stride (int or pair of ints) – Stride of filter applications at the first convolution layer. stride=s and stride=(s, s) are equivalent.

• pad (int or pair of ints) – Spatial padding width for input arrays at the first convolution layer. pad=p and pad=(p, p) are equivalent.

• activation (callable) – Activation function for internal hidden units. You can specify one of activation functions from built-in activation functions or your own function. It should not be an activation functions with parameters (i.e., Link instance). The function must accept one argument (the output from each child link), and return a value. Returned value must be a Variable derived from the input Variable to perform backpropagation on the variable. Note that this function is not applied to the output of this link.

• conv_init – An initializer of weight matrices passed to the convolution layers. This option must be specified as a keyword argument.

• bias_init – An initializer of bias vectors passed to the convolution layers. This option must be specified as a keyword argument.

See: Network in Network.

Variables

activation (callable) – Activation function. See the description in the arguments for details.

Methods

__call__(*args, **kwargs)

Call self as a function.

__getitem__(index)

Returns the child at given index.

Parameters

index (int) – Index of the child in the list.

Returns

The index-th child link.

Return type

Link

__setitem__(index, value)

__len__()

Returns the number of children.

__iter__()

add_hook(hook, name=None)

Registers a link hook.

Parameters

• hook (LinkHook) – Link hook to be registered.

• name (str) – Name of the link hook. The name must be unique among link hooks registered to this link. If None, the default name of the link hook is used.

Returns

self

add_link(link)

Registers a child link and adds it to the tail of the list.

Parameters

link (Link) – The link object to be registered.

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

Registers a parameter to the link.

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)

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.

append(value)

S.append(value) – append value to the end of the sequence

children()

Returns a generator of all child links.

Returns

A generator object that generates all child links.

clear() → None -- remove all items from S

cleargrads()

Clears all gradient arrays.

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

copy(mode='share')

Returns a deep copy of the chainlist.

copyparams(link, copy_persistent=True)

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.

From v5.0.0: this method also copies the persistent values (e.g. the moving statistics of BatchNormalization). If the persistent value is an ndarray, the elements are copied. Otherwise, it is copied using copy.deepcopy(). The old behavior (not copying persistent values) can be reproduced with copy_persistent=False.

Parameters

• link (Link) – Source link object.

• copy_persistent (bool) – If True, persistent values are also copied. True by default.

count(value) → integer -- return number of occurrences of value

count_params()

Counts the total number of parameters.

This method counts the total number of scalar values included in all the Parameters held by this link and its descendants.

If the link containts uninitialized parameters, this method raises a warning.

Returns

The total size of parameters (int)

delete_hook(name)

Unregisters the link hook.

Parameters

name (str) – The name of the link hook to be unregistered.

disable_update()

Disables update rules of all parameters under the link hierarchy.

This method sets the 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.

extend(values)

S.extend(iterable) – extend sequence by appending elements from the iterable

forward(x)

Computes the output of the mlpconv layer.

Parameters

x (Variable) – Input image.

Returns

Output of the mlpconv layer.

Return type

Variable

from_chainerx()

Converts parameter variables and persistent values from ChainerX to NumPy/CuPy devices without any copy.

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

init_scope()

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,))

insert(index, link)

Insert a child link at the given index.

Parameters

• index (int) – The position of the list where the new

• is inserted. (link) –

• link (Link) – The link to be inserted.

links(skipself=False)

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)

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)

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)

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.

pop([index]) → item -- remove and return item at index (default last).

Raise IndexError if list is empty or index is out of range.

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.

remove(value)

S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

repeat(n_repeat, mode='init')

Repeats this link multiple times to make a Sequential.

This method returns a Sequential object which has the same Link multiple times repeatedly. The mode argument means how to copy this link to repeat.

Example

You can repeat the same link multiple times to create a longer Sequential block like this:

class ConvBNReLU(chainer.Chain):

    def __init__(self):
        super(ConvBNReLU, self).__init__()
        with self.init_scope():
            self.conv = L.Convolution2D(
                None, 64, 3, 1, 1, nobias=True)
            self.bn = L.BatchNormalization(64)

    def forward(self, x):
        return F.relu(self.bn(self.conv(x)))

net = ConvBNReLU().repeat(16, mode='init')

The net object contains 16 blocks, each of which is ConvBNReLU. And the mode was init, so each block is re-initialized with different parameters. If you give copy to this argument, each block has same values for its parameters but its object ID is different from others. If it is share, each block is same to others in terms of not only parameters but also the object IDs because they are shallow-copied, so that when the parameter of one block is changed, all the parameters in the others also change.

Parameters

• n_repeat (int) – Number of times to repeat.

• mode (str) – It should be either init, copy, or share. init means parameters of each repeated element in the returned Sequential will be re-initialized, so that all elements have different initial parameters. copy means that the parameters will not be re-initialized but object itself will be deep-copied, so that all elements have same initial parameters but can be changed independently. share means all the elements which consist the resulting Sequential object are same object because they are shallow-copied, so that all parameters of elements are shared with each other.

reverse()

S.reverse() – reverse IN PLACE

serialize(serializer)

Serializes the link object.

Parameters

serializer (AbstractSerializer) – Serializer object.

to_chainerx()

Converts parameter variables and persistent values to ChainerX without any copy.

This method does not handle non-registered attributes. If some of such attributes must be copied to ChainerX, the link implementation must override this method to do so.

Returns: self

to_cpu()

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 Link.to_device() to do so.

Returns: self

to_device(device)

Copies parameter variables and persistent values to the specified device.

This method does not handle non-registered attributes. If some of such attributes must be copied to the device, the link implementation must override this method to do so.

Parameters

device – Target device specifier. See get_device() for available values.

Returns: self

to_gpu(device=None)

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 Link.to_device() to do so.

Parameters

device – Target device specifier. If omitted, the current device is used.

Returns: self

to_intel64()

Copies parameter variables and persistent values to CPU.

zerograds()

Initializes all gradient arrays by zero.

Deprecated since version v1.15: Use the more efficient cleargrads() instead.

Attributes

device

local_link_hooks

Ordered dictionary of registered link hooks.

Contrary to chainer.thread_local.link_hooks, which registers its elements to all functions, link hooks in this property are specific to this link.

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.