theanets/regularizers.py
def from_kwargs(graph, **kwargs):
    '''Construct common regularizers from a set of keyword arguments.

    Keyword arguments not listed below will be passed to
    :func:`Regularizer.build` if they specify the name of a registered
    :class:`Regularizer`.

    Parameters
    ----------
    graph : :class:`theanets.graph.Network`
        A network graph to regularize.

    regularizers : dict or tuple/list of :class:`Regularizer`, optional
        If this is a list or a tuple, the contents of the list will be returned
        as the regularizers. This is to permit custom lists of regularizers to
        be passed easily.

        If this is a dict, its contents will be added to the other keyword
        arguments passed in.

    rng : int or theano RandomStreams, optional
        If an integer is provided, it will be used to seed the random number
        generators for the dropout or noise regularizers. If a theano
        RandomStreams object is provided, it will be used directly. Defaults to
        13.

    input_dropout : float, optional
        Apply dropout to input layers in the network graph, with this dropout
        rate. Defaults to 0 (no dropout).

    hidden_dropout : float, optional
        Apply dropout to hidden layers in the network graph, with this dropout
        rate. Defaults to 0 (no dropout).

    output_dropout : float, optional
        Apply dropout to the output layer in the network graph, with this
        dropout rate. Defaults to 0 (no dropout).

    input_noise : float, optional
        Apply noise to input layers in the network graph, with this standard
        deviation. Defaults to 0 (no noise).

    hidden_noise : float, optional
        Apply noise to hidden layers in the network graph, with this standard
        deviation. Defaults to 0 (no noise).

    output_noise : float, optional
        Apply noise to the output layer in the network graph, with this
        standard deviation. Defaults to 0 (no noise).

    Returns
    -------
    regs : list of :class:`Regularizer`
        A list of regularizers to apply to the given network graph.
    '''
    if 'regularizers' in kwargs:
        regs = kwargs['regularizers']
        if isinstance(regs, (tuple, list)):
            return regs
        if isinstance(regs, dict):
            kwargs.update(regs)

    regs = []

    rng = kwargs.get('rng', 13)

    def pattern(ls):
        return tuple(l.output_name for l in ls)

    inputs = pattern([l for l in graph.layers if isinstance(l, layers.Input)])
    hiddens = pattern(graph.layers[1:-1])
    outputs = pattern([graph.layers[-1]])

    # create regularizers for different types of canned dropout.
    spec = {inputs: kwargs.get('input_dropout', 0),
            hiddens: kwargs.get('hidden_dropout', 0),
            outputs: kwargs.get('output_dropout', 0)}
    spec.update(kwargs.get('dropout', {}))
    for pattern, w in spec.items():
        if w:
            regs.append(BernoulliDropout(pattern=pattern, weight=w, rng=rng))

    # create regularizers for different types of canned noise.
    spec = {inputs: kwargs.get('input_noise', 0),
            hiddens: kwargs.get('hidden_noise', 0),
            outputs: kwargs.get('output_noise', 0)}
    spec.update(kwargs.get('noise', {}))
    for pattern, w in spec.items():
        if w:
            regs.append(GaussianNoise(pattern=pattern, weight=w, rng=rng))

    # create regularizers based on other keyword arguments.
    for key, value in kwargs.items():
        if Regularizer.is_registered(key):
            if not isinstance(value, dict):
                value = dict(weight=value)
            regs.append(Regularizer.build(key, **value))

    return regs
def from_kwargs(graph, **kwargs):
    '''Construct common regularizers from a set of keyword arguments.

    Keyword arguments not listed below will be passed to
    :func:`Regularizer.build` if they specify the name of a registered
    :class:`Regularizer`.

    Parameters
    ----------
    graph : :class:`theanets.graph.Network`
        A network graph to regularize.

    regularizers : dict or tuple/list of :class:`Regularizer`, optional
        If this is a list or a tuple, the contents of the list will be returned
        as the regularizers. This is to permit custom lists of regularizers to
        be passed easily.

        If this is a dict, its contents will be added to the other keyword
        arguments passed in.

    rng : int or theano RandomStreams, optional
        If an integer is provided, it will be used to seed the random number
        generators for the dropout or noise regularizers. If a theano
        RandomStreams object is provided, it will be used directly. Defaults to
        13.

    input_dropout : float, optional
        Apply dropout to input layers in the network graph, with this dropout
        rate. Defaults to 0 (no dropout).

    hidden_dropout : float, optional
        Apply dropout to hidden layers in the network graph, with this dropout
        rate. Defaults to 0 (no dropout).

    output_dropout : float, optional
        Apply dropout to the output layer in the network graph, with this
        dropout rate. Defaults to 0 (no dropout).

    input_noise : float, optional
        Apply noise to input layers in the network graph, with this standard
        deviation. Defaults to 0 (no noise).

    hidden_noise : float, optional
        Apply noise to hidden layers in the network graph, with this standard
        deviation. Defaults to 0 (no noise).

    output_noise : float, optional
        Apply noise to the output layer in the network graph, with this
        standard deviation. Defaults to 0 (no noise).

    Returns
    -------
    regs : list of :class:`Regularizer`
        A list of regularizers to apply to the given network graph.
    '''
    if 'regularizers' in kwargs:
        regs = kwargs['regularizers']
        if isinstance(regs, (tuple, list)):
            return regs
        if isinstance(regs, dict):
            kwargs.update(regs)

    regs = []

    rng = kwargs.get('rng', 13)

    def pattern(ls):
        return tuple(l.output_name for l in ls)

    inputs = pattern([l for l in graph.layers if isinstance(l, layers.Input)])
    hiddens = pattern(graph.layers[1:-1])
    outputs = pattern([graph.layers[-1]])

    # create regularizers for different types of canned dropout.
    spec = {inputs: kwargs.get('input_dropout', 0),
            hiddens: kwargs.get('hidden_dropout', 0),
            outputs: kwargs.get('output_dropout', 0)}
    spec.update(kwargs.get('dropout', {}))
    for pattern, w in spec.items():
        if w:
            regs.append(BernoulliDropout(pattern=pattern, weight=w, rng=rng))

    # create regularizers for different types of canned noise.
    spec = {inputs: kwargs.get('input_noise', 0),
            hiddens: kwargs.get('hidden_noise', 0),
            outputs: kwargs.get('output_noise', 0)}
    spec.update(kwargs.get('noise', {}))
    for pattern, w in spec.items():
        if w:
            regs.append(GaussianNoise(pattern=pattern, weight=w, rng=rng))

    # create regularizers based on other keyword arguments.
    for key, value in kwargs.items():
        if Regularizer.is_registered(key):
            if not isinstance(value, dict):
                value = dict(weight=value)
            regs.append(Regularizer.build(key, **value))

    return regs
def from_kwargs ( graph , * * kwargs ) : if 'regularizers' in kwargs : regs = kwargs [ 'regularizers' ] if isinstance ( regs , ( tuple , list ) ) : return regs if isinstance ( regs , dict ) : kwargs . update ( regs ) regs = [ ] rng = kwargs . get ( 'rng' , 13 ) def pattern ( ls ) : return tuple ( l . output_name for l in ls ) inputs = pattern ( [ l for l in graph . layers if isinstance ( l , layers . Input ) ] ) hiddens = pattern ( graph . layers [ 1 : - 1 ] ) outputs = pattern ( [ graph . layers [ - 1 ] ] ) # create regularizers for different types of canned dropout. spec = { inputs : kwargs . get ( 'input_dropout' , 0 ) , hiddens : kwargs . get ( 'hidden_dropout' , 0 ) , outputs : kwargs . get ( 'output_dropout' , 0 ) } spec . update ( kwargs . get ( 'dropout' , { } ) ) for pattern , w in spec . items ( ) : if w : regs . append ( BernoulliDropout ( pattern = pattern , weight = w , rng = rng ) ) # create regularizers for different types of canned noise. spec = { inputs : kwargs . get ( 'input_noise' , 0 ) , hiddens : kwargs . get ( 'hidden_noise' , 0 ) , outputs : kwargs . get ( 'output_noise' , 0 ) } spec . update ( kwargs . get ( 'noise' , { } ) ) for pattern , w in spec . items ( ) : if w : regs . append ( GaussianNoise ( pattern = pattern , weight = w , rng = rng ) ) # create regularizers based on other keyword arguments. for key , value in kwargs . items ( ) : if Regularizer . is_registered ( key ) : if not isinstance ( value , dict ) : value = dict ( weight = value ) regs . append ( Regularizer . build ( key , * * value ) ) return regs
Construct common regularizers from a set of keyword arguments.

    Keyword arguments not listed below will be passed to
    :func:`Regularizer.build` if they specify the name of a registered
    :class:`Regularizer`.

    Parameters
    ----------
    graph : :class:`theanets.graph.Network`
        A network graph to regularize.

    regularizers : dict or tuple/list of :class:`Regularizer`, optional
        If this is a list or a tuple, the contents of the list will be returned
        as the regularizers. This is to permit custom lists of regularizers to
        be passed easily.

        If this is a dict, its contents will be added to the other keyword
        arguments passed in.

    rng : int or theano RandomStreams, optional
        If an integer is provided, it will be used to seed the random number
        generators for the dropout or noise regularizers. If a theano
        RandomStreams object is provided, it will be used directly. Defaults to
        13.

    input_dropout : float, optional
        Apply dropout to input layers in the network graph, with this dropout
        rate. Defaults to 0 (no dropout).

    hidden_dropout : float, optional
        Apply dropout to hidden layers in the network graph, with this dropout
        rate. Defaults to 0 (no dropout).

    output_dropout : float, optional
        Apply dropout to the output layer in the network graph, with this
        dropout rate. Defaults to 0 (no dropout).

    input_noise : float, optional
        Apply noise to input layers in the network graph, with this standard
        deviation. Defaults to 0 (no noise).

    hidden_noise : float, optional
        Apply noise to hidden layers in the network graph, with this standard
        deviation. Defaults to 0 (no noise).

    output_noise : float, optional
        Apply noise to the output layer in the network graph, with this
        standard deviation. Defaults to 0 (no noise).

    Returns
    -------
    regs : list of :class:`Regularizer`
        A list of regularizers to apply to the given network graph.
Construct common regularizers from a set of keyword arguments .