blocks.bricks.BatchNormalizedMLP._nested_brick_property_setter() - Code Metrics - Inspection of "Merge pull request #1012 from dwf/mean_only_batch_..." - mila-udem/blocks - Measure and Improve Code Quality continuously with Scrutinizer

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_nested_brick_property_setter() A

↳ Parent: blocks.bricks.BatchNormalizedMLP

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rs	9.4285

import collections
from functools import partial

import numpy
from picklable_itertools.extras import equizip
import theano
from theano import tensor
from theano.tensor.nnet import bn

from ..graph import add_annotation
from ..initialization import Constant
from ..roles import (BATCH_NORM_POPULATION_MEAN,
                     BATCH_NORM_POPULATION_STDEV, BATCH_NORM_OFFSET,
                     BATCH_NORM_DIVISOR, BATCH_NORM_MINIBATCH_ESTIMATE,
                     BATCH_NORM_SHIFT_PARAMETER, BATCH_NORM_SCALE_PARAMETER,
                     add_role)
from ..utils import (shared_floatx_zeros, shared_floatx,
                     shared_floatx_nans)
from .base import lazy, application
from .sequences import Sequence, Feedforward, MLP
from .interfaces import RNGMixin


def _add_batch_axis(var):
    """Prepend a singleton axis to a TensorVariable and name it."""
    new_var = new_var = tensor.shape_padleft(var)
    new_var.name = 'shape_padleft({})'.format(var.name)
    return new_var


def _add_role_and_annotate(var, role, annotations=()):
    """Add a role and zero or more annotations to a variable."""
    add_role(var, role)
    for annotation in annotations:
        add_annotation(var, annotation)


class BatchNormalization(RNGMixin, Feedforward):
    r"""Normalizes activations, parameterizes a scale and shift.

    Parameters
    ----------
    input_dim : int or tuple
        Shape of a single input example. It is assumed that a batch axis
        will be prepended to this.
    broadcastable : tuple, optional
        Tuple the same length as `input_dim` which specifies which of the
        per-example axes should be averaged over to compute means and
        standard deviations. For example, in order to normalize over all
        spatial locations in a `(batch_index, channels, height, width)`
        image, pass `(False, True, True)`.
    conserve_memory : bool, optional
        Use an implementation that stores less intermediate state and
        therefore uses less memory, at the expense of 5-10% speed. Default
        is `True`.
    epsilon : float, optional
       The stabilizing constant for the minibatch standard deviation
       computation (when the brick is run in training mode).
       Added to the variance inside the square root, as in the
       batch normalization paper.
    scale_init : object, optional
        Initialization object to use for the learned scaling parameter
        ($\\gamma$ in [BN]_). By default, uses constant initialization
        of 1.
    shift_init : object, optional
        Initialization object to use for the learned shift parameter
        ($\\beta$ in [BN]_). By default, uses constant initialization of 0.
    mean_only : bool, optional
        Perform "mean-only" batch normalization as described in [SK2016]_.

    Notes
    -----
    In order for trained models to behave sensibly immediately upon
    upon deserialization, by default, this brick runs in *inference* mode,
    using a population mean and population standard deviation (initialized
    to zeros and ones respectively) to normalize activations. It is
    expected that the user will adapt these during training in some
    fashion, independently of the training objective, e.g. by taking a
    moving average of minibatch-wise statistics.

    In order to *train* with batch normalization, one must obtain a
    training graph by transforming the original inference graph. See
    :func:`~blocks.graph.apply_batch_normalization` for a routine to
    transform graphs, and :func:`~blocks.graph.batch_normalization`
    for a context manager that may enable shorter compile times
    (every instance of :class:`BatchNormalization` is itself a context
    manager, entry into which causes applications to be in minibatch
    "training" mode, however it is usually more convenient to use
    :func:`~blocks.graph.batch_normalization` to enable this behaviour
    for all of your graph's :class:`BatchNormalization` bricks at once).

    Note that training in inference mode should be avoided, as this
    brick introduces scales and shift parameters (tagged with the
    `PARAMETER` role) that, in the absence of batch normalization,
    usually makes things unstable. If you must do this, filter for and
    remove `BATCH_NORM_SHIFT_PARAMETER` and `BATCH_NORM_SCALE_PARAMETER`
    from the list of parameters you are training, and this brick should
    behave as a (somewhat expensive) no-op.

    This Brick accepts `scale_init` and `shift_init` arguments but is
    *not* an instance of :class:`~blocks.bricks.Initializable`, and will
    therefore not receive pushed initialization config from any parent
    brick. In almost all cases, you will probably want to stick with the
    defaults (unit scale and zero offset), but you can explicitly pass one
    or both initializers to override this.

    This has the necessary properties to be inserted into a
    :class:`blocks.bricks.conv.ConvolutionalSequence` as-is, in which case
    the `input_dim` should be omitted at construction, to be inferred from
    the layer below.


    .. [BN] Sergey Ioffe and Christian Szegedy. *Batch normalization:
       accelerating deep network training by reducing internal covariate
       shift*. ICML (2015), pp. 448-456.

    .. [SK2016] Tim Salimans and Diederik P. Kingma. *Weight
       normalization: a simple reparameterization to accelerate training
       of deep neural networks*. arXiv 1602.07868.

    """
    @lazy(allocation=['input_dim'])
    def __init__(self, input_dim, broadcastable=None,
                 conserve_memory=True, epsilon=1e-4, scale_init=None,
                 shift_init=None, mean_only=False, **kwargs):
        self.input_dim = input_dim
        self.broadcastable = broadcastable
        self.conserve_memory = conserve_memory
        self.epsilon = epsilon
        self.scale_init = (Constant(1) if scale_init is None
                           else scale_init)
        self.shift_init = (Constant(0) if shift_init is None
                           else shift_init)
        self.mean_only = mean_only
        self._training_mode = []
        super(BatchNormalization, self).__init__(**kwargs)

    @application(inputs=['input_'], outputs=['output'])
    def apply(self, input_, application_call):
        if self._training_mode:
            mean, stdev = self._compute_training_statistics(input_)
        else:
            mean, stdev = self._prepare_population_statistics()
        # Useful for filtration of calls that were already made in
        # training mode when doing graph transformations.
        # Very important to cast to bool, as self._training_mode is
        # normally a list (to support nested context managers), which would
        # otherwise get passed by reference and be remotely mutated.
        application_call.metadata['training_mode'] = bool(self._training_mode)
        # Useful for retrieving a list of updates for population
        # statistics. Ditch the broadcastable first axis, though, to
        # make it the same dimensions as the population mean and stdev
        # shared variables.
        application_call.metadata['offset'] = mean[0]
        application_call.metadata['divisor'] = stdev[0]
        # Give these quantities roles in the graph.
        _add_role_and_annotate(mean, BATCH_NORM_OFFSET,
                               [self, application_call])
        if self.mean_only:
            scale = tensor.ones_like(self.shift)
            stdev = tensor.ones_like(mean)
        else:
            scale = self.scale
            # The annotation/role information is useless if it's a constant.
            _add_role_and_annotate(stdev, BATCH_NORM_DIVISOR,
                                   [self, application_call])
        shift = _add_batch_axis(self.shift)
        scale = _add_batch_axis(scale)
        # Heavy lifting is done by the Theano utility function.
        normalized = bn.batch_normalization(input_, scale, shift, mean, stdev,
                                            mode=('low_mem'
                                                  if self.conserve_memory
                                                  else 'high_mem'))
        return normalized

    def __enter__(self):
        self._training_mode.append(True)

    def __exit__(self, *exc_info):
        self._training_mode.pop()

    def _compute_training_statistics(self, input_):
        axes = (0,) + tuple((i + 1) for i, b in
                            enumerate(self.population_mean.broadcastable)
                            if b)
        mean = input_.mean(axis=axes, keepdims=True)
        assert mean.broadcastable[1:] == self.population_mean.broadcastable
        add_role(mean, BATCH_NORM_MINIBATCH_ESTIMATE)
        if self.mean_only:
            stdev = tensor.ones_like(mean)
        else:
            stdev = tensor.sqrt(tensor.var(input_, axis=axes, keepdims=True) +
                                numpy.cast[theano.config.floatX](self.epsilon))
            assert (stdev.broadcastable[1:] ==
                    self.population_stdev.broadcastable)
            add_role(stdev, BATCH_NORM_MINIBATCH_ESTIMATE)
        return mean, stdev

    def _prepare_population_statistics(self):
        mean = _add_batch_axis(self.population_mean)
        if self.mean_only:
            stdev = tensor.ones_like(self.population_mean)
        else:
            stdev = self.population_stdev
        stdev = _add_batch_axis(stdev)
        return mean, stdev

    def _allocate(self):
        input_dim = ((self.input_dim,)
                     if not isinstance(self.input_dim, collections.Sequence)
                     else self.input_dim)
        broadcastable = (tuple(False for _ in input_dim)
                         if self.broadcastable is None else self.broadcastable)
        if len(input_dim) != len(broadcastable):
            raise ValueError("input_dim and broadcastable must be same length")
        var_dim = tuple(1 if broadcast else dim for dim, broadcast in
                        equizip(input_dim, broadcastable))
        broadcastable = broadcastable

        # "beta", from the Ioffe & Szegedy manuscript.
        self.shift = shared_floatx_nans(var_dim, name='batch_norm_shift',
                                        broadcastable=broadcastable)
        add_role(self.shift, BATCH_NORM_SHIFT_PARAMETER)
        self.parameters.append(self.shift)

        # These aren't technically parameters, in that they should not be
        # learned using the same cost function as other model parameters.
        self.population_mean = shared_floatx_zeros(var_dim,
                                                   name='population_mean',
                                                   broadcastable=broadcastable)
        add_role(self.population_mean, BATCH_NORM_POPULATION_MEAN)

        # Normally these would get annotated by an AnnotatingList, but they
        # aren't in self.parameters.
        add_annotation(self.population_mean, self)

        if not self.mean_only:
            # "gamma", from the Ioffe & Szegedy manuscript.
            self.scale = shared_floatx_nans(var_dim, name='batch_norm_scale',
                                            broadcastable=broadcastable)

            add_role(self.scale, BATCH_NORM_SCALE_PARAMETER)
            self.parameters.append(self.scale)

            self.population_stdev = shared_floatx(numpy.ones(var_dim),
                                                  name='population_stdev',
                                                  broadcastable=broadcastable)
            add_role(self.population_stdev, BATCH_NORM_POPULATION_STDEV)
            add_annotation(self.population_stdev, self)

    def _initialize(self):
        self.shift_init.initialize(self.shift, self.rng)
        if not self.mean_only:
            self.scale_init.initialize(self.scale, self.rng)

    # Needed for the Feedforward interface.
    @property
    def output_dim(self):
        return self.input_dim

    # The following properties allow for BatchNormalization bricks
    # to be used directly inside of a ConvolutionalSequence.
    @property
    def image_size(self):
        return self.input_dim[-2:]

    @image_size.setter
    def image_size(self, value):
        if not isinstance(self.input_dim, collections.Sequence):
            self.input_dim = (None,) + tuple(value)
        else:
            self.input_dim = (self.input_dim[0],) + tuple(value)

    @property
    def num_channels(self):
        return self.input_dim[0]

    @num_channels.setter
    def num_channels(self, value):
        if not isinstance(self.input_dim, collections.Sequence):
            self.input_dim = (value,) + (None, None)
        else:
            self.input_dim = (value,) + self.input_dim[-2:]

    def get_dim(self, name):
        if name in ('input', 'output'):
            return self.input_dim
        else:
            raise KeyError

    @property
    def num_output_channels(self):
        return self.num_channels


class SpatialBatchNormalization(BatchNormalization):
    """Convenient subclass for batch normalization across spatial inputs.

    Parameters
    ----------
    input_dim : int or tuple
        The input size of a single example. Must be length at least 2.
        It's assumed that the first axis of this tuple is a "channels"
        axis, which should not be summed over, and all remaining
        dimensions are spatial dimensions.

    Notes
    -----
    See :class:`BatchNormalization` for more details (and additional
    keyword arguments).

    """
    def _allocate(self):
        if not isinstance(self.input_dim,
                          collections.Sequence) or len(self.input_dim) < 2:
            raise ValueError('expected input_dim to be length >= 2 '
                             'e.g. (channels, height, width)')
        self.broadcastable = (False,) + ((True,) * (len(self.input_dim) - 1))
        super(SpatialBatchNormalization, self)._allocate()


class BatchNormalizedMLP(MLP):
    """Convenient subclass for building an MLP with batch normalization.

    Parameters
    ----------
    conserve_memory : bool, optional
        See :class:`BatchNormalization`.
    mean_only : bool, optional
        See :class:`BatchNormalization`.

    Notes
    -----
    All other parameters are the same as :class:`~blocks.bricks.MLP`. Each
    activation brick is wrapped in a :class:`~blocks.bricks.Sequence`
    containing an appropriate :class:`BatchNormalization` brick and
    the activation that follows it.

    By default, the contained :class:`~blocks.bricks.Linear` bricks will
    not contain any biases, as they could be canceled out by the biases
    in the :class:`BatchNormalization` bricks being added. Pass
    `use_bias` with a value of `True` if you really want this for some
    reason.

    """
    @lazy(allocation=['dims'])
    def __init__(self, activations, dims, *args, **kwargs):
        self._conserve_memory = kwargs.pop('conserve_memory', True)
        self._mean_only = kwargs.pop('mean_only', False)
        activations = [
            Sequence([
                BatchNormalization(conserve_memory=self._conserve_memory,
                                   mean_only=self._mean_only).apply,
                act.apply
            ], name='batch_norm_activation_{}'.format(i))
            for i, act in enumerate(activations)
        ]
        # Batch normalization bricks incorporate a bias, so there's no
        # need for our Linear bricks to have them.
        kwargs.setdefault('use_bias', False)
        super(BatchNormalizedMLP, self).__init__(activations, dims, *args,
                                                 **kwargs)

    def _nested_brick_property_getter(self, property_name):
        return getattr(self, '_' + property_name)

    def _nested_brick_property_setter(self, value, property_name):
        setattr(self, '_' + property_name, value)
        for act in self.activations:
            assert isinstance(act.children[0], BatchNormalization)
            setattr(act.children[0], property_name, value)

    conserve_memory = property(partial(_nested_brick_property_getter,
                                       property_name='conserve_memory'),
                               partial(_nested_brick_property_setter,
                                       property_name='conserve_memory'))

    mean_only = property(partial(_nested_brick_property_getter,
                                 property_name='mean_only'),
                         partial(_nested_brick_property_setter,
                                 property_name='mean_only'))

    def _push_allocation_config(self):
        super(BatchNormalizedMLP, self)._push_allocation_config()
        # Do the extra allocation pushing for the BatchNormalization
        # bricks. They need as their input dimension the output dimension
        # of each linear transformation.  Exclude the first dimension,
        # which is the input dimension.
        for act, dim in equizip(self.activations, self.dims[1:]):
            assert isinstance(act.children[0], BatchNormalization)
            act.children[0].input_dim = dim


1			import collections
2			from functools import partial
3
4			import numpy
5			from picklable_itertools.extras import equizip
6			import theano
7			from theano import tensor
8			from theano.tensor.nnet import bn
9
10			from ..graph import add_annotation
11			from ..initialization import Constant
12			from ..roles import (BATCH_NORM_POPULATION_MEAN,
13			BATCH_NORM_POPULATION_STDEV, BATCH_NORM_OFFSET,
14			BATCH_NORM_DIVISOR, BATCH_NORM_MINIBATCH_ESTIMATE,
15			BATCH_NORM_SHIFT_PARAMETER, BATCH_NORM_SCALE_PARAMETER,
16			add_role)
17			from ..utils import (shared_floatx_zeros, shared_floatx,
18			shared_floatx_nans)
19			from .base import lazy, application
20			from .sequences import Sequence, Feedforward, MLP
21			from .interfaces import RNGMixin
22
23
24			def _add_batch_axis(var):
25			"""Prepend a singleton axis to a TensorVariable and name it."""
26			new_var = new_var = tensor.shape_padleft(var)
27			new_var.name = 'shape_padleft({})'.format(var.name)
28			return new_var
29
30
31			def _add_role_and_annotate(var, role, annotations=()):
32			"""Add a role and zero or more annotations to a variable."""
33			add_role(var, role)
34			for annotation in annotations:
35			add_annotation(var, annotation)
36
37
38			class BatchNormalization(RNGMixin, Feedforward):
39			r"""Normalizes activations, parameterizes a scale and shift.
40
41			Parameters
42			----------
43			input_dim : int or tuple
44			Shape of a single input example. It is assumed that a batch axis
45			will be prepended to this.
46			broadcastable : tuple, optional
47			Tuple the same length as `input_dim` which specifies which of the
48			per-example axes should be averaged over to compute means and
49			standard deviations. For example, in order to normalize over all
50			spatial locations in a `(batch_index, channels, height, width)`
51			image, pass `(False, True, True)`.
52			conserve_memory : bool, optional
53			Use an implementation that stores less intermediate state and
54			therefore uses less memory, at the expense of 5-10% speed. Default
55			is `True`.
56			epsilon : float, optional
57			The stabilizing constant for the minibatch standard deviation
58			computation (when the brick is run in training mode).
59			Added to the variance inside the square root, as in the
60			batch normalization paper.
61			scale_init : object, optional
62			Initialization object to use for the learned scaling parameter
63			($\\gamma$ in [BN]_). By default, uses constant initialization
64			of 1.
65			shift_init : object, optional
66			Initialization object to use for the learned shift parameter
67			($\\beta$ in [BN]_). By default, uses constant initialization of 0.
68			mean_only : bool, optional
69			Perform "mean-only" batch normalization as described in [SK2016]_.
70
71			Notes
72			-----
73			In order for trained models to behave sensibly immediately upon
74			upon deserialization, by default, this brick runs in inference mode,
75			using a population mean and population standard deviation (initialized
76			to zeros and ones respectively) to normalize activations. It is
77			expected that the user will adapt these during training in some
78			fashion, independently of the training objective, e.g. by taking a
79			moving average of minibatch-wise statistics.
80
81			In order to train with batch normalization, one must obtain a
82			training graph by transforming the original inference graph. See
83			:func:`~blocks.graph.apply_batch_normalization` for a routine to
84			transform graphs, and :func:`~blocks.graph.batch_normalization`
85			for a context manager that may enable shorter compile times
86			(every instance of :class:`BatchNormalization` is itself a context
87			manager, entry into which causes applications to be in minibatch
88			"training" mode, however it is usually more convenient to use
89			:func:`~blocks.graph.batch_normalization` to enable this behaviour
90			for all of your graph's :class:`BatchNormalization` bricks at once).
91
92			Note that training in inference mode should be avoided, as this
93			brick introduces scales and shift parameters (tagged with the
94			`PARAMETER` role) that, in the absence of batch normalization,
95			usually makes things unstable. If you must do this, filter for and
96			remove `BATCH_NORM_SHIFT_PARAMETER` and `BATCH_NORM_SCALE_PARAMETER`
97			from the list of parameters you are training, and this brick should
98			behave as a (somewhat expensive) no-op.
99
100			This Brick accepts `scale_init` and `shift_init` arguments but is
101			not an instance of :class:`~blocks.bricks.Initializable`, and will
102			therefore not receive pushed initialization config from any parent
103			brick. In almost all cases, you will probably want to stick with the
104			defaults (unit scale and zero offset), but you can explicitly pass one
105			or both initializers to override this.
106
107			This has the necessary properties to be inserted into a
108			:class:`blocks.bricks.conv.ConvolutionalSequence` as-is, in which case
109			the `input_dim` should be omitted at construction, to be inferred from
110			the layer below.
111
112
113			.. [BN] Sergey Ioffe and Christian Szegedy. *Batch normalization:
114			accelerating deep network training by reducing internal covariate
115			shift*. ICML (2015), pp. 448-456.
116
117			.. [SK2016] Tim Salimans and Diederik P. Kingma. *Weight
118			normalization: a simple reparameterization to accelerate training
119			of deep neural networks*. arXiv 1602.07868.
120
121			"""
122			@lazy(allocation=['input_dim'])
123			def __init__(self, input_dim, broadcastable=None,
124			conserve_memory=True, epsilon=1e-4, scale_init=None,
125			shift_init=None, mean_only=False, **kwargs):
126			self.input_dim = input_dim
127			self.broadcastable = broadcastable
128			self.conserve_memory = conserve_memory
129			self.epsilon = epsilon
130			self.scale_init = (Constant(1) if scale_init is None
131			else scale_init)
132			self.shift_init = (Constant(0) if shift_init is None
133			else shift_init)
134			self.mean_only = mean_only
135			self._training_mode = []
136			super(BatchNormalization, self).__init__(**kwargs)
137
138			@application(inputs=['input_'], outputs=['output'])
139			def apply(self, input_, application_call):
140			if self._training_mode:
141			mean, stdev = self._compute_training_statistics(input_)
142			else:
143			mean, stdev = self._prepare_population_statistics()
144			# Useful for filtration of calls that were already made in
145			# training mode when doing graph transformations.
146			# Very important to cast to bool, as self._training_mode is
147			# normally a list (to support nested context managers), which would
148			# otherwise get passed by reference and be remotely mutated.
149			application_call.metadata['training_mode'] = bool(self._training_mode)
150			# Useful for retrieving a list of updates for population
151			# statistics. Ditch the broadcastable first axis, though, to
152			# make it the same dimensions as the population mean and stdev
153			# shared variables.
154			application_call.metadata['offset'] = mean[0]
155			application_call.metadata['divisor'] = stdev[0]
156			# Give these quantities roles in the graph.
157			_add_role_and_annotate(mean, BATCH_NORM_OFFSET,
158			[self, application_call])
159			if self.mean_only:
160			scale = tensor.ones_like(self.shift)
161			stdev = tensor.ones_like(mean)
162			else:
163			scale = self.scale
164			# The annotation/role information is useless if it's a constant.
165			_add_role_and_annotate(stdev, BATCH_NORM_DIVISOR,
166			[self, application_call])
167			shift = _add_batch_axis(self.shift)
168			scale = _add_batch_axis(scale)
169			# Heavy lifting is done by the Theano utility function.
170			normalized = bn.batch_normalization(input_, scale, shift, mean, stdev,
171			mode=('low_mem'
172			if self.conserve_memory
173			else 'high_mem'))
174			return normalized
175
176			def __enter__(self):
177			self._training_mode.append(True)
178
179			def __exit__(self, *exc_info):
180			self._training_mode.pop()
181
182			def _compute_training_statistics(self, input_):
183			axes = (0,) + tuple((i + 1) for i, b in
184			enumerate(self.population_mean.broadcastable)
185			if b)
186			mean = input_.mean(axis=axes, keepdims=True)
187			assert mean.broadcastable[1:] == self.population_mean.broadcastable
188			add_role(mean, BATCH_NORM_MINIBATCH_ESTIMATE)
189			if self.mean_only:
190			stdev = tensor.ones_like(mean)
191			else:
192			stdev = tensor.sqrt(tensor.var(input_, axis=axes, keepdims=True) +
193			numpy.cast[theano.config.floatX](self.epsilon))
194			assert (stdev.broadcastable[1:] ==
195			self.population_stdev.broadcastable)
196			add_role(stdev, BATCH_NORM_MINIBATCH_ESTIMATE)
197			return mean, stdev
198
199			def _prepare_population_statistics(self):
200			mean = _add_batch_axis(self.population_mean)
201			if self.mean_only:
202			stdev = tensor.ones_like(self.population_mean)
203			else:
204			stdev = self.population_stdev
205			stdev = _add_batch_axis(stdev)
206			return mean, stdev
207
208			def _allocate(self):
209			input_dim = ((self.input_dim,)
210			if not isinstance(self.input_dim, collections.Sequence)
211			else self.input_dim)
212			broadcastable = (tuple(False for _ in input_dim)
213			if self.broadcastable is None else self.broadcastable)
214			if len(input_dim) != len(broadcastable):
215			raise ValueError("input_dim and broadcastable must be same length")
216			var_dim = tuple(1 if broadcast else dim for dim, broadcast in
217			equizip(input_dim, broadcastable))
218			broadcastable = broadcastable
219
220			# "beta", from the Ioffe & Szegedy manuscript.
221			self.shift = shared_floatx_nans(var_dim, name='batch_norm_shift',
222			broadcastable=broadcastable)
223			add_role(self.shift, BATCH_NORM_SHIFT_PARAMETER)
224			self.parameters.append(self.shift)
225
226			# These aren't technically parameters, in that they should not be
227			# learned using the same cost function as other model parameters.
228			self.population_mean = shared_floatx_zeros(var_dim,
229			name='population_mean',
230			broadcastable=broadcastable)
231			add_role(self.population_mean, BATCH_NORM_POPULATION_MEAN)
232
233			# Normally these would get annotated by an AnnotatingList, but they
234			# aren't in self.parameters.
235			add_annotation(self.population_mean, self)
236
237			if not self.mean_only:
238			# "gamma", from the Ioffe & Szegedy manuscript.
239			self.scale = shared_floatx_nans(var_dim, name='batch_norm_scale',
240			broadcastable=broadcastable)
241
242			add_role(self.scale, BATCH_NORM_SCALE_PARAMETER)
243			self.parameters.append(self.scale)
244
245			self.population_stdev = shared_floatx(numpy.ones(var_dim),
246			name='population_stdev',
247			broadcastable=broadcastable)
248			add_role(self.population_stdev, BATCH_NORM_POPULATION_STDEV)
249			add_annotation(self.population_stdev, self)
250
251			def _initialize(self):
252			self.shift_init.initialize(self.shift, self.rng)
253			if not self.mean_only:
254			self.scale_init.initialize(self.scale, self.rng)
255
256			# Needed for the Feedforward interface.
257			@property
258			def output_dim(self):
259			return self.input_dim
260
261			# The following properties allow for BatchNormalization bricks
262			# to be used directly inside of a ConvolutionalSequence.
263			@property
264			def image_size(self):
265			return self.input_dim[-2:]
266
267			@image_size.setter
268			def image_size(self, value):
269			if not isinstance(self.input_dim, collections.Sequence):
270			self.input_dim = (None,) + tuple(value)
271			else:
272			self.input_dim = (self.input_dim[0],) + tuple(value)
273
274			@property
275			def num_channels(self):
276			return self.input_dim[0]
277
278			@num_channels.setter
279			def num_channels(self, value):
280			if not isinstance(self.input_dim, collections.Sequence):
281			self.input_dim = (value,) + (None, None)
282			else:
283			self.input_dim = (value,) + self.input_dim[-2:]
284
285			def get_dim(self, name):
286			if name in ('input', 'output'):
287			return self.input_dim
288			else:
289			raise KeyError
290
291			@property
292			def num_output_channels(self):
293			return self.num_channels
294
295
296			class SpatialBatchNormalization(BatchNormalization):
297			"""Convenient subclass for batch normalization across spatial inputs.
298
299			Parameters
300			----------
301			input_dim : int or tuple
302			The input size of a single example. Must be length at least 2.
303			It's assumed that the first axis of this tuple is a "channels"
304			axis, which should not be summed over, and all remaining
305			dimensions are spatial dimensions.
306
307			Notes
308			-----
309			See :class:`BatchNormalization` for more details (and additional
310			keyword arguments).
311
312			"""
313			def _allocate(self):
314			if not isinstance(self.input_dim,
315			collections.Sequence) or len(self.input_dim) < 2:
316			raise ValueError('expected input_dim to be length >= 2 '
317			'e.g. (channels, height, width)')
318			self.broadcastable = (False,) + ((True,) * (len(self.input_dim) - 1))
319			super(SpatialBatchNormalization, self)._allocate()
320
321
322			class BatchNormalizedMLP(MLP):
323			"""Convenient subclass for building an MLP with batch normalization.
324
325			Parameters
326			----------
327			conserve_memory : bool, optional
328			See :class:`BatchNormalization`.
329			mean_only : bool, optional
330			See :class:`BatchNormalization`.
331
332			Notes
333			-----
334			All other parameters are the same as :class:`~blocks.bricks.MLP`. Each
335			activation brick is wrapped in a :class:`~blocks.bricks.Sequence`
336			containing an appropriate :class:`BatchNormalization` brick and
337			the activation that follows it.
338
339			By default, the contained :class:`~blocks.bricks.Linear` bricks will
340			not contain any biases, as they could be canceled out by the biases
341			in the :class:`BatchNormalization` bricks being added. Pass
342			`use_bias` with a value of `True` if you really want this for some
343			reason.
344
345			"""
346			@lazy(allocation=['dims'])
347			def __init__(self, activations, dims, args, *kwargs):
348			self._conserve_memory = kwargs.pop('conserve_memory', True)
349			self._mean_only = kwargs.pop('mean_only', False)
350			activations = [
351			Sequence([
352			BatchNormalization(conserve_memory=self._conserve_memory,
353			mean_only=self._mean_only).apply,
354			act.apply
355			], name='batch_norm_activation_{}'.format(i))
356			for i, act in enumerate(activations)
357			]
358			# Batch normalization bricks incorporate a bias, so there's no
359			# need for our Linear bricks to have them.
360			kwargs.setdefault('use_bias', False)
361			super(BatchNormalizedMLP, self).__init__(activations, dims, *args,
362			**kwargs)
363
364			def _nested_brick_property_getter(self, property_name):
365			return getattr(self, '_' + property_name)
366
367			def _nested_brick_property_setter(self, value, property_name):
368			setattr(self, '_' + property_name, value)
369			for act in self.activations:
370			assert isinstance(act.children[0], BatchNormalization)
371			setattr(act.children[0], property_name, value)
372
373			conserve_memory = property(partial(_nested_brick_property_getter,
374			property_name='conserve_memory'),
375			partial(_nested_brick_property_setter,
376			property_name='conserve_memory'))
377
378			mean_only = property(partial(_nested_brick_property_getter,
379			property_name='mean_only'),
380			partial(_nested_brick_property_setter,
381			property_name='mean_only'))
382
383			def _push_allocation_config(self):
384			super(BatchNormalizedMLP, self)._push_allocation_config()
385			# Do the extra allocation pushing for the BatchNormalization
386			# bricks. They need as their input dimension the output dimension
387			# of each linear transformation. Exclude the first dimension,
388			# which is the input dimension.
389			for act, dim in equizip(self.activations, self.dims[1:]):
390			assert isinstance(act.children[0], BatchNormalization)
391			act.children[0].input_dim = dim
392

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