Initializable.__setattr__() - Code Metrics - Inspection of "[WIP] Managing initilizations via a role scheme di..." - mila-udem/blocks - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#1030)

unknown

created 2016-06-01 18:17 UTC

Initializable.setattr() A

↳ Parent: Initializable

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Importance

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Metric	Value
cc	3
dl	0
loc	7
rs	9.4285
c	0
b	0
f	0

"""Bricks that are interfaces and/or mixins."""
import numpy
from six import add_metaclass
from theano.sandbox.rng_mrg import MRG_RandomStreams

from ..config import config
from .base import _Brick, Brick, lazy
from blocks.roles import WEIGHT, BIAS, FILTER, INITIAL_STATE



class ActivationDocumentation(_Brick):
    """Dynamically adds documentation to activations.

    Notes
    -----
    See http://bugs.python.org/issue12773.

    """
    def __new__(cls, name, bases, classdict):

        classdict['__doc__'] = \
            """Elementwise application of {0} function.""".format(name.lower())
        if 'apply' in classdict:
            classdict['apply'].__doc__ = \
                """Apply the {0} function element-wise.

                Parameters
                ----------
                input_ : :class:`~tensor.TensorVariable`
                    Theano variable to apply {0} to, element-wise.

                Returns
                -------
                output : :class:`~tensor.TensorVariable`
                    The input with the activation function applied.

                """.format(name.lower())
        return super(ActivationDocumentation, cls).__new__(cls, name, bases,
                                                           classdict)


@add_metaclass(ActivationDocumentation)
class Activation(Brick):
    """A base class for simple, element-wise activation functions.

    This base class ensures that activation functions are automatically
    documented using the :class:`ActivationDocumentation` metaclass.

    """
    pass


class Feedforward(Brick):
    """Declares an interface for bricks with one input and one output.

    Many bricks have just one input and just one output (activations,
    :class:`Linear`, :class:`MLP`). To make such bricks interchangable
    in most contexts they should share an interface for configuring
    their input and output dimensions. This brick declares such an
    interface.

    Attributes
    ----------
    input_dim : int
        The input dimension of the brick.
    output_dim : int
        The output dimension of the brick.

    """
    def __getattr__(self, name):
        message = ("'{}' object does not have an attribute '{}'"
                   .format(self.__class__.__name__, name))
        if name in ('input_dim', 'output_dim'):
            message += (" (which is a part of 'Feedforward' interface it"
                        " claims to support)")
        raise AttributeError(message)


class RNGMixin(object):
    """Mixin for initialization random number generators."""
    seed_rng = numpy.random.RandomState(config.default_seed)

    @property
    def seed(self):
        if getattr(self, '_seed', None) is not None:
            return self._seed
        else:
            self._seed = self.seed_rng.randint(
                numpy.iinfo(numpy.int32).max)
            return self._seed

    @seed.setter
    def seed(self, value):
        if hasattr(self, '_seed'):
            raise AttributeError("seed already set")
        self._seed = value

    @property
    def rng(self):
        if getattr(self, '_rng', None) is not None:
            return self._rng
        else:
            self._rng = numpy.random.RandomState(self.seed)
            return self._rng

    @rng.setter
    def rng(self, rng):
        self._rng = rng


class Initializable(RNGMixin, Brick):
    """Base class for bricks which push parameter initialization.

    Many bricks will initialize children which perform a linear
    transformation, often with biases. This brick allows the weights
    and biases initialization to be configured in the parent brick and
    pushed down the hierarchy.

    Parameters
    ----------
    weights_init : object
        A `NdarrayInitialization` instance which will be used by to
        initialize the weight matrix. Required by
        :meth:`~.Brick.initialize`.
    biases_init : :obj:`object`, optional
        A `NdarrayInitialization` instance that will be used to initialize
        the biases. Required by :meth:`~.Brick.initialize` when `use_bias`
        is `True`. Only supported by bricks for which :attr:`has_biases` is
        ``True``.
    use_bias : :obj:`bool`, optional
        Whether to use a bias. Defaults to `True`. Required by
        :meth:`~.Brick.initialize`.
    rng : :class:`numpy.random.RandomState`

    """

    @lazy()
    def __init__(self, initialization_schemes=None, parameter_roles=None,
                 use_bias=True, seed=None, **kwargs):
        self.use_bias = use_bias
        self.seed = seed
        self.initialization_schemes = initialization_schemes
        if self.initialization_schemes is None:
            self.initialization_schemes = {}

        if parameter_roles:
            self.parameter_roles = parameter_roles
        else:
            self.parameter_roles = set([WEIGHT])
            if use_bias:
                self.parameter_roles.update(set([BIAS]))

        initialization_to_role = {"weights_init": WEIGHT, 'biases_init': BIAS,
                                  'initial_state_init': INITIAL_STATE}
        for key in list(kwargs.keys()):
            if key[-5:] == "_init":
                if key not in initialization_to_role:
                    raise ValueError("The initlization scheme: {}".format(key),
                                     "is not defined by default, pass it"
                                     "via initialization_schemes")
                if initialization_to_role[key] in \
                        self.initialization_schemes.keys():
                    raise ValueError("All initializations are accepted either"
                                     "through initialization schemes or "
                                     "corresponding attribute but not both")
                else:
                    self.initialization_schemes[initialization_to_role[
                                                key]] = kwargs[key]
                kwargs.pop(key)

        super(Initializable, self).__init__(**kwargs)
        self._collect_roles()

    def _validate_roles(self):
        high_level_roles = []
        for role in self.parameter_roles:
            if role not in self.initialization_schemes.keys():
                for key in list(self.initialization_schemes.keys()):
                    if isinstance(role, type(key)):
                        self.initialization_schemes[role] = \
                                            self.initialization_schemes[key]
                        high_level_roles.append(key)

        for key in high_level_roles:
            if key not in self.parameter_roles:
                self.initialization_schemes.pop(key)

        for key in self.initialization_schemes:
            if key not in self.parameter_roles:
                raise ValueError("{} is not member of ".format(key) +
                                 "parameter_roles")

    def _push_initialization_config(self):
        self._validate_roles()
        for child in self.children:
            if (isinstance(child, Initializable) and
                    hasattr(child, 'initialization_schemes')):
                child.rng = self.rng
                for role, scheme in self.initialization_schemes.items():
                    if role in child.parameter_roles:
                        child.initialization_schemes[role] = scheme

    def _collect_roles(self):
        for child in self.children:
            if isinstance(child, Initializable):
                self.parameter_roles.update(child.parameter_roles)

    def _initialize(self):
        for param in self.parameters:
            for role in param.tag.roles:
                if role in self.parameter_roles:
                    self.initialization_schemes[role].initialize(param,
                                                                 self.rng)

    def __getattr__(self, name):
        if name == "weights_init":
            if WEIGHT in self.initialization_schemes:
                return self.initialization_schemes[WEIGHT]
        elif name == "biases_init":
            if BIAS in self.initialization_schemes:
                return self.initialization_schemes[BIAS]
        super(Initializable, self).__getattr__(name)

    def __setattr__(self, name, value):
        if name == 'weights_init':
            self.initialization_schemes[WEIGHT] = value
        elif name == 'biases_init':
            self.initialization_schemes[BIAS] = value
        else:
            super(Initializable, self).__setattr__(name, value)


class LinearLike(Initializable):
    """Initializable subclass with logic for :class:`Linear`-like classes.

    Notes
    -----
    Provides `W` and `b` properties that can be overridden in subclasses
    to implement pre-application transformations on the weights and
    biases.  Application methods should refer to ``self.W`` and ``self.b``
    rather than accessing the parameters list directly.

    This assumes a layout of the parameters list with the weights coming
    first and biases (if ``use_bias`` is True) coming second.

    """

    def __init__(self, **kwargs):
        if 'parameter_roles' in kwargs:
            kwargs['parameter_roles'].update(set([WEIGHT, BIAS]))
        else:
            kwargs['parameter_roles'] = set([WEIGHT, BIAS])
        super(LinearLike, self).__init__(**kwargs)

    @property
    def W(self):
        return self.parameters[0]

    @property
    def b(self):
        if getattr(self, 'use_bias', True):
            return self.parameters[1]
        else:
            raise AttributeError('use_bias is False')


class Random(Brick):
    """A mixin class for Bricks which need Theano RNGs.

    Parameters
    ----------
    theano_seed : int or list, optional
        Seed to use for a
        :class:`~theano.sandbox.rng_mrg.MRG_RandomStreams` object.

    """
    seed_rng = numpy.random.RandomState(config.default_seed)

    def __init__(self, theano_seed=None, **kwargs):
        super(Random, self).__init__(**kwargs)
        self.theano_seed = theano_seed

    @property
    def theano_seed(self):
        if getattr(self, '_theano_seed', None) is not None:
            return self._theano_seed
        else:
            self._theano_seed = self.seed_rng.randint(
                numpy.iinfo(numpy.int32).max)
            return self._theano_seed

    @theano_seed.setter
    def theano_seed(self, value):
        if hasattr(self, '_theano_seed'):
            raise AttributeError("seed already set")
        self._theano_seed = value

    @property
    def theano_rng(self):
        """Returns Brick's Theano RNG, or a default one.

        The default seed can be set through ``blocks.config``.

        """
        if not hasattr(self, '_theano_rng'):
            self._theano_rng = MRG_RandomStreams(self.theano_seed)
        return self._theano_rng

    @theano_rng.setter
    def theano_rng(self, theano_rng):
        self._theano_rng = theano_rng


1			"""Bricks that are interfaces and/or mixins."""
2			import numpy
3			from six import add_metaclass
4			from theano.sandbox.rng_mrg import MRG_RandomStreams
5
6			from ..config import config
7			from .base import _Brick, Brick, lazy
8			from blocks.roles import WEIGHT, BIAS, FILTER, INITIAL_STATE
			0 ignored issues – show Unused Code introduced 2016-04-12 19:16 UTC by Report Bug Copy Issue Report Unused FILTER imported from blocks.roles Loading history...
9
10
11			class ActivationDocumentation(_Brick):
12			"""Dynamically adds documentation to activations.
13
14			Notes
15			-----
16			See http://bugs.python.org/issue12773.
17
18			"""
19			def __new__(cls, name, bases, classdict):
			0 ignored issues – show Coding Style Best Practice introduced 2016-01-16 01:07 UTC by Report Bug Copy Issue Report The first argument of the `__new__` class method of metaclasses should be named `mcs` by convention. Loading history...
20			classdict['__doc__'] = \
21			"""Elementwise application of {0} function.""".format(name.lower())
22			if 'apply' in classdict:
23			classdict['apply'].__doc__ = \
24			"""Apply the {0} function element-wise.
25
26			Parameters
27			----------
28			input_ : :class:`~tensor.TensorVariable`
29			Theano variable to apply {0} to, element-wise.
30
31			Returns
32			-------
33			output : :class:`~tensor.TensorVariable`
34			The input with the activation function applied.
35
36			""".format(name.lower())
37			return super(ActivationDocumentation, cls).__new__(cls, name, bases,
38			classdict)
39
40
41			@add_metaclass(ActivationDocumentation)
42			class Activation(Brick):
43			"""A base class for simple, element-wise activation functions.
44
45			This base class ensures that activation functions are automatically
46			documented using the :class:`ActivationDocumentation` metaclass.
47
48			"""
49			pass
50
51
52			class Feedforward(Brick):
53			"""Declares an interface for bricks with one input and one output.
54
55			Many bricks have just one input and just one output (activations,
56			:class:`Linear`, :class:`MLP`). To make such bricks interchangable
57			in most contexts they should share an interface for configuring
58			their input and output dimensions. This brick declares such an
59			interface.
60
61			Attributes
62			----------
63			input_dim : int
64			The input dimension of the brick.
65			output_dim : int
66			The output dimension of the brick.
67
68			"""
69			def __getattr__(self, name):
70			message = ("'{}' object does not have an attribute '{}'"
71			.format(self.__class__.__name__, name))
72			if name in ('input_dim', 'output_dim'):
73			message += (" (which is a part of 'Feedforward' interface it"
74			" claims to support)")
75			raise AttributeError(message)
76
77
78			class RNGMixin(object):
79			"""Mixin for initialization random number generators."""
80			seed_rng = numpy.random.RandomState(config.default_seed)
81
82			@property
83			def seed(self):
84			if getattr(self, '_seed', None) is not None:
85			return self._seed
86			else:
87			self._seed = self.seed_rng.randint(
88			numpy.iinfo(numpy.int32).max)
89			return self._seed
90
91			@seed.setter
92			def seed(self, value):
93			if hasattr(self, '_seed'):
94			raise AttributeError("seed already set")
95			self._seed = value
96
97			@property
98			def rng(self):
99			if getattr(self, '_rng', None) is not None:
100			return self._rng
101			else:
102			self._rng = numpy.random.RandomState(self.seed)
103			return self._rng
104
105			@rng.setter
106			def rng(self, rng):
107			self._rng = rng
108
109
110			class Initializable(RNGMixin, Brick):
111			"""Base class for bricks which push parameter initialization.
112
113			Many bricks will initialize children which perform a linear
114			transformation, often with biases. This brick allows the weights
115			and biases initialization to be configured in the parent brick and
116			pushed down the hierarchy.
117
118			Parameters
119			----------
120			weights_init : object
121			A `NdarrayInitialization` instance which will be used by to
122			initialize the weight matrix. Required by
123			:meth:`~.Brick.initialize`.
124			biases_init : :obj:`object`, optional
125			A `NdarrayInitialization` instance that will be used to initialize
126			the biases. Required by :meth:`~.Brick.initialize` when `use_bias`
127			is `True`. Only supported by bricks for which :attr:`has_biases` is
128			``True``.
129			use_bias : :obj:`bool`, optional
130			Whether to use a bias. Defaults to `True`. Required by
131			:meth:`~.Brick.initialize`.
132			rng : :class:`numpy.random.RandomState`
133
134			"""
135
136			@lazy()
137			def __init__(self, initialization_schemes=None, parameter_roles=None,
138			use_bias=True, seed=None, **kwargs):
139			self.use_bias = use_bias
140			self.seed = seed
141			self.initialization_schemes = initialization_schemes
142			if self.initialization_schemes is None:
143			self.initialization_schemes = {}
144
145			if parameter_roles:
146			self.parameter_roles = parameter_roles
147			else:
148			self.parameter_roles = set([WEIGHT])
149			if use_bias:
150			self.parameter_roles.update(set([BIAS]))
151
152			initialization_to_role = {"weights_init": WEIGHT, 'biases_init': BIAS,
153			'initial_state_init': INITIAL_STATE}
154			for key in list(kwargs.keys()):
155			if key[-5:] == "_init":
156			if key not in initialization_to_role:
157			raise ValueError("The initlization scheme: {}".format(key),
158			"is not defined by default, pass it"
159			"via initialization_schemes")
160			if initialization_to_role[key] in \
161			self.initialization_schemes.keys():
162			raise ValueError("All initializations are accepted either"
163			"through initialization schemes or "
164			"corresponding attribute but not both")
165			else:
166			self.initialization_schemes[initialization_to_role[
167			key]] = kwargs[key]
168			kwargs.pop(key)
169
170			super(Initializable, self).__init__(**kwargs)
171			self._collect_roles()
172
173			def _validate_roles(self):
174			high_level_roles = []
175			for role in self.parameter_roles:
176			if role not in self.initialization_schemes.keys():
177			for key in list(self.initialization_schemes.keys()):
178			if isinstance(role, type(key)):
179			self.initialization_schemes[role] = \
180			self.initialization_schemes[key]
181			high_level_roles.append(key)
182
183			for key in high_level_roles:
184			if key not in self.parameter_roles:
185			self.initialization_schemes.pop(key)
186
187			for key in self.initialization_schemes:
188			if key not in self.parameter_roles:
189			raise ValueError("{} is not member of ".format(key) +
190			"parameter_roles")
191
192			def _push_initialization_config(self):
193			self._validate_roles()
194			for child in self.children:
195			if (isinstance(child, Initializable) and
196			hasattr(child, 'initialization_schemes')):
197			child.rng = self.rng
198			for role, scheme in self.initialization_schemes.items():
199			if role in child.parameter_roles:
200			child.initialization_schemes[role] = scheme
201
202			def _collect_roles(self):
203			for child in self.children:
204			if isinstance(child, Initializable):
205			self.parameter_roles.update(child.parameter_roles)
206
207			def _initialize(self):
208			for param in self.parameters:
209			for role in param.tag.roles:
210			if role in self.parameter_roles:
211			self.initialization_schemes[role].initialize(param,
212			self.rng)
213
214			def __getattr__(self, name):
215			if name == "weights_init":
216			if WEIGHT in self.initialization_schemes:
217			return self.initialization_schemes[WEIGHT]
218			elif name == "biases_init":
219			if BIAS in self.initialization_schemes:
220			return self.initialization_schemes[BIAS]
221			super(Initializable, self).__getattr__(name)
222
223			def __setattr__(self, name, value):
224			if name == 'weights_init':
225			self.initialization_schemes[WEIGHT] = value
226			elif name == 'biases_init':
227			self.initialization_schemes[BIAS] = value
228			else:
229			super(Initializable, self).__setattr__(name, value)
230
231
232			class LinearLike(Initializable):
233			"""Initializable subclass with logic for :class:`Linear`-like classes.
234
235			Notes
236			-----
237			Provides `W` and `b` properties that can be overridden in subclasses
238			to implement pre-application transformations on the weights and
239			biases. Application methods should refer to ``self.W`` and ``self.b``
240			rather than accessing the parameters list directly.
241
242			This assumes a layout of the parameters list with the weights coming
243			first and biases (if ``use_bias`` is True) coming second.
244
245			"""
246
247			def __init__(self, **kwargs):
248			if 'parameter_roles' in kwargs:
249			kwargs['parameter_roles'].update(set([WEIGHT, BIAS]))
250			else:
251			kwargs['parameter_roles'] = set([WEIGHT, BIAS])
252			super(LinearLike, self).__init__(**kwargs)
253
254			@property
255			def W(self):
256			return self.parameters[0]
257
258			@property
259			def b(self):
260			if getattr(self, 'use_bias', True):
261			return self.parameters[1]
262			else:
263			raise AttributeError('use_bias is False')
264
265
266			class Random(Brick):
267			"""A mixin class for Bricks which need Theano RNGs.
268
269			Parameters
270			----------
271			theano_seed : int or list, optional
272			Seed to use for a
273			:class:`~theano.sandbox.rng_mrg.MRG_RandomStreams` object.
274
275			"""
276			seed_rng = numpy.random.RandomState(config.default_seed)
277
278			def __init__(self, theano_seed=None, **kwargs):
279			super(Random, self).__init__(**kwargs)
280			self.theano_seed = theano_seed
281
282			@property
283			def theano_seed(self):
284			if getattr(self, '_theano_seed', None) is not None:
285			return self._theano_seed
286			else:
287			self._theano_seed = self.seed_rng.randint(
288			numpy.iinfo(numpy.int32).max)
289			return self._theano_seed
290
291			@theano_seed.setter
292			def theano_seed(self, value):
293			if hasattr(self, '_theano_seed'):
294			raise AttributeError("seed already set")
295			self._theano_seed = value
296
297			@property
298			def theano_rng(self):
299			"""Returns Brick's Theano RNG, or a default one.
300
301			The default seed can be set through ``blocks.config``.
302
303			"""
304			if not hasattr(self, '_theano_rng'):
305			self._theano_rng = MRG_RandomStreams(self.theano_seed)
306			return self._theano_rng
307
308			@theano_rng.setter
309			def theano_rng(self, theano_rng):
310			self._theano_rng = theano_rng
311

mila-udem / blocks

Pull Request — master (#1030)

Initializable.__setattr__() A

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Initializable.setattr() A