Module Expr Str Assign NameStore Str ImportFrom alias ImportFrom alias alias ImportFrom alias ImportFrom alias FunctionDef arguments args defaults body Expr Str Assign NameStore Call NameLoad Str Assign NameStore Call NameLoad Num keyword Str Assign NameStore Call NameLoad Num keyword Str Assign NameStore Call NameLoad Num Num Num Assign AttributeStore NameLoad attr Str Expr Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad Expr Call AttributeLoad NameLoad attr Return NameLoad decorator_list If CompareEq NameLoad Str body Expr Call NameLoad Call NameLoad Str
N/A N/A 

Build a mixed nested network:

    >>> n = buildMixedNestedNetwork()
    >>> inner = n['inner']

Some specific tests:
The feed-forward part should have its buffers increased in size, and
keep the correct offset.

    >>> len(inner.outputbuffer)
    1

    >>> o1 = n.activate([1])
    >>> o2 = n.activate([2])
    >>> o2 = n.activate([3])
    >>> (o1 == o2).any()
    False

    >>> n.offset
    3
    >>> inner.offset
    3
    >>> len(inner.outputbuffer)
    4

Verify everything is still fine after reset
    >>> n.reset()

    >>> n.offset
    0
    >>> inner.offset
    0



Check its gradient:

    >>> from pybrain.tests import gradientCheck
    >>> gradientCheck(n)
    Perfect gradient
    True

Try writing it to an xml file, reread it and determine if it looks the same:

    >>> from pybrain.tests import xmlInvariance
    >>> xmlInvariance(n)
    Same representation
    Same function
    Same class


 N/A __author__ Tom Schaul, tom@idsia.ch pybrain.structure RecurrentNetwork pybrain LinearLayer FullConnection pybrain.tools.shortcuts buildNetwork pybrain.tests runModuleTestSuite buildMixedNestedNetwork N/A N/A N/A N/A N/A  build a nested network with the inner one being a ffn and the outer one being recurrent.  N/A N N/A RecurrentNetwork outer N/A a N/A LinearLayer 1 name a N/A b N/A LinearLayer 2 name b N/A c N/A buildNetwork 2 3 1 N/A N/A c name inner N/A N/A N/A N addInputModule a N/A N/A N/A N addModule c N/A N/A N/A N addOutputModule b N/A N/A N/A N addConnection N/A FullConnection a b N/A N/A N/A N addConnection N/A FullConnection b c N/A N/A N/A N addRecurrentConnection N/A FullConnection c c N/A N/A N/A N sortModules N/A N N/A N/A N/A __name__ __main__ N/A N/A N/A runModuleTestSuite N/A __import__ __main__