chainer.links.Maxout¶
-
class
chainer.links.
Maxout
(in_size, out_size, pool_size, initialW=None, initial_bias=0)[source]¶ Fully-connected maxout layer.
Let
M
,P
andN
be an input dimension, a pool size, and an output dimension, respectively. For an input vector \(x\) of sizeM
, it computes\[Y_{i} = \mathrm{max}_{j} (W_{ij\cdot}x + b_{ij}).\]Here \(W\) is a weight tensor of shape
(M, P, N)
, \(b\) an optional bias vector of shape(M, P)
and \(W_{ij\cdot}\) is a sub-vector extracted from \(W\) by fixing first and second dimensions to \(i\) and \(j\), respectively. Minibatch dimension is omitted in the above equation.As for the actual implementation, this chain has a Linear link with a
(M * P, N)
weight matrix and an optionalM * P
dimensional bias vector.Parameters: - in_size (int) – Dimension of input vectors.
- out_size (int) – Dimension of output vectors.
- pool_size (int) – Number of channels.
- initialW (3-D array or None) – Initial weight value.
If
None
, the default initializer is used to initialize the weight matrix. - initial_bias (2-D array, float or None) – Initial bias value.
If it is float, initial bias is filled with this value.
If
None
, bias is omitted.
Variables: linear (Link) – The Linear link that performs affine transformation.
See also
See also
Goodfellow, I., Warde-farley, D., Mirza, M., Courville, A., & Bengio, Y. (2013). Maxout Networks. In Proceedings of the 30th International Conference on Machine Learning (ICML-13) (pp. 1319-1327). URL
Methods
-
__call__
(x)[source]¶ Applies the maxout layer.
Parameters: x (Variable) – Batch of input vectors. Returns: Output of the maxout layer. Return type: Variable
-
add_link
(name, link)[source]¶ Registers a child link to this chain.
Deprecated since version v2.0.0: Assign the child link directly to an attribute within
init_scope()
instead. For example, the following codechain.add_link('l1', L.Linear(3, 5))
can be replaced by the following line.
with chain.init_scope(): chain.l1 = L.Linear(3, 5)
The latter is easier for IDEs to keep track of the attribute’s type.
Parameters:
-
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.
-
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.
-
cleargrads
()[source]¶ Clears all gradient arrays.
This method should be called before the backward computation at every iteration of the optimization.
-
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,))
-
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.
-
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.