Package mdp :: Package parallel :: Class ParallelCloneLayer

Class ParallelCloneLayer

Parallel version of CloneLayer class.

Instance Methods

[hide private]

_fork(self)
Fork the internal node in the clone layer.

_join(self, forked_node)
Join the internal node in the clone layer.

use_execute_fork(self)
Return True if node requires a fork / join even during execution.

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

Inherited from hinet.CloneLayer

__init__(self, node, n_nodes=1, dtype=None)
Setup the layer with the given list of nodes.

_execute(self, x, *args, **kwargs)
Process the data through the internal nodes.

_inverse(self, x, *args, **kwargs)
Combine the inverse of all the internal nodes.

_stop_training(self, *args, **kwargs)
Stop training of the internal node.

execute(self, x, *args, **kwargs)
Process the data through the internal nodes.

inverse(self, x, *args, **kwargs)
Combine the inverse of all the internal nodes.

stop_training(self, *args, **kwargs)
Stop training of the internal node.

Inherited from hinet.Layer

__contains__(self, item)

__getitem__(self, key)

__iter__(self)

__len__(self)

_check_props(self, dtype)
Check the compatibility of the properties of the internal nodes.

_get_output_dim_from_nodes(self)
Calculate the output_dim from the nodes and return it.

_get_supported_dtypes(self)
Return the list of dtypes supported by this node.

_get_train_seq(self)
Return the train sequence.

_pre_execution_checks(self, x)
Make sure that output_dim is set and then perform normal checks.

_set_dtype(self, t)

_train(self, x, *args, **kwargs)
Perform single training step by training the internal nodes.

is_invertible(self)
Return True if the node can be inverted, False otherwise.

is_trainable(self)
Return True if the node can be trained, False otherwise.

train(self, x, *args, **kwargs)
Perform single training step by training the internal nodes.

Inherited from ParallelExtensionNode

_default_fork(self)
Default implementation of _fork.

fork(self)
Return a new instance of this node class for remote training.

join(self, forked_node)
Absorb the trained node from a fork into this parent node.

Inherited from Node

__add__(self, other)

__call__(self, x, *args, **kwargs)
Calling an instance of Node is equivalent to calling its execute method.

__repr__(self)
repr(x)

__str__(self)
str(x)

_check_input(self, x)

_check_output(self, y)

_check_train_args(self, x, *args, **kwargs)

_if_training_stop_training(self)

_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.

_refcast(self, x)
Helper function to cast arrays to the internal dtype.

_set_input_dim(self, n)

_set_output_dim(self, n)

copy(self, protocol=None)
Return a deep copy of the node.

get_current_train_phase(self)
Return the index of the current training phase.

get_dtype(self)
Return dtype.

get_input_dim(self)
Return input dimensions.

get_output_dim(self)
Return output dimensions.

get_remaining_train_phase(self)
Return the number of training phases still to accomplish.

get_supported_dtypes(self)
Return dtypes supported by the node as a list of numpy.dtype objects.

has_multiple_training_phases(self)
Return True if the node has multiple training phases.

is_training(self)
Return True if the node is in the training phase, False otherwise.

save(self, filename, protocol=-1)
Save a pickled serialization of the node to filename. If filename is None, return a string.

set_dtype(self, t)
Set internal structures' dtype.

set_input_dim(self, n)
Set input dimensions.

set_output_dim(self, n)
Set output dimensions.

Static Methods

[hide private]

Inherited from ParallelExtensionNode

_join_covariance(cov, forked_cov)
Helper method to join two CovarianceMatrix instances.

Class Variables

[hide private]

Inherited from ParallelExtensionNode

extension_name = 'parallel'
hash(x)

Properties

[hide private]

Inherited from object: __class__

Inherited from Node

_train_seq
List of tuples:

dtype
dtype

input_dim
Input dimensions

output_dim
Output dimensions

supported_dtypes
Supported dtypes

Method Details

[hide private]

_fork(self)

Fork the internal node in the clone layer.

Overrides: ParallelExtensionNode._fork

_join(self, forked_node)

Join the internal node in the clone layer.

Overrides: ParallelExtensionNode._join

use_execute_fork(self)

Return True if node requires a fork / join even during execution.

The default output is False, overwrite this method if required.

Note that the same fork and join methods are used as during training,
so the distinction must be implemented in the custom _fork and _join
methods.

Overrides: ParallelExtensionNode.use_execute_fork: (inherited documentation)