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"""Bricks that compose together other bricks in linear sequences.""" |
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import copy |
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from toolz import interleave, unique |
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from picklable_itertools.extras import equizip |
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from ..utils import pack |
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from .base import Brick, application, lazy |
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from .interfaces import Feedforward, Initializable |
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from .simple import Linear |
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class Sequence(Brick): |
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"""A sequence of bricks. |
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This brick applies a sequence of bricks, assuming that their in- and |
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outputs are compatible. |
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Parameters |
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---------- |
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application_methods : list |
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List of :class:`.BoundApplication` or :class:`.Brick` to apply. |
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For :class:`.Brick`s, the ``.apply`` method is used. |
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""" |
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def __init__(self, application_methods, **kwargs): |
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pairs = ((a.apply, a) if isinstance(a, Brick) else (a, a.brick) |
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for a in application_methods) |
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self.application_methods, bricks = zip(*pairs) |
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kwargs.setdefault('children', []).extend(unique(bricks)) |
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super(Sequence, self).__init__(**kwargs) |
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@application |
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def apply(self, *args): |
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child_input = args |
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for application_method in self.application_methods: |
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output = application_method(*pack(child_input)) |
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child_input = output |
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return output |
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@apply.property('inputs') |
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def apply_inputs(self): |
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return self.application_methods[0].inputs |
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@apply.property('outputs') |
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def apply_outputs(self): |
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return self.application_methods[-1].outputs |
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class FeedforwardSequence(Sequence, Feedforward): |
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"""A sequence where the first and last bricks are feedforward. |
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Parameters |
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---------- |
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application_methods : list |
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List of :class:`.BoundApplication` to apply. The first and last |
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application method should belong to a :class:`Feedforward` brick. |
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""" |
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@property |
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def input_dim(self): |
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return self.children[0].input_dim |
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@input_dim.setter |
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def input_dim(self, value): |
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self.children[0].input_dim = value |
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@property |
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def output_dim(self): |
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return self.children[-1].output_dim |
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@output_dim.setter |
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def output_dim(self, value): |
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self.children[-1].output_dim = value |
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class MLP(FeedforwardSequence, Initializable): |
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"""A simple multi-layer perceptron. |
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Parameters |
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---------- |
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activations : list of :class:`.Brick`, :class:`.BoundApplication`, |
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or ``None`` |
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A list of activations to apply after each linear transformation. |
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Give ``None`` to not apply any activation. It is assumed that the |
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application method to use is ``apply``. Required for |
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:meth:`__init__`. |
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dims : list of ints |
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A list of input dimensions, as well as the output dimension of the |
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last layer. Required for :meth:`~.Brick.allocate`. |
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prototype : :class:`.Brick`, optional |
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The transformation prototype. A copy will be created for every |
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activation. If not provided, an instance of :class:`~simple.Linear` |
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will be used. |
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Notes |
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----- |
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See :class:`Initializable` for initialization parameters. |
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Note that the ``weights_init``, ``biases_init`` (as well as |
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``use_bias`` if set to a value other than the default of ``None``) |
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configurations will overwrite those of the layers each time the |
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:class:`MLP` is re-initialized. For more fine-grained control, push the |
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configuration to the child layers manually before initialization. |
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>>> from blocks.bricks import Tanh |
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>>> from blocks.initialization import IsotropicGaussian, Constant |
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>>> mlp = MLP(activations=[Tanh(), None], dims=[30, 20, 10], |
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... weights_init=IsotropicGaussian(), |
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... biases_init=Constant(1)) |
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>>> mlp.push_initialization_config() # Configure children |
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>>> mlp.children[0].weights_init = IsotropicGaussian(0.1) |
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>>> mlp.initialize() |
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""" |
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@lazy(allocation=['dims']) |
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def __init__(self, activations, dims, prototype=None, **kwargs): |
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self.activations = activations |
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self.prototype = Linear() if prototype is None else prototype |
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self.linear_transformations = [] |
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for i in range(len(activations)): |
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linear = copy.deepcopy(self.prototype) |
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name = self.prototype.__class__.__name__.lower() |
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linear.name = '{}_{}'.format(name, i) |
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self.linear_transformations.append(linear) |
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if not dims: |
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dims = [None] * (len(activations) + 1) |
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self.dims = dims |
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# Interleave the transformations and activations |
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applications = [a for a in interleave([self.linear_transformations, |
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activations]) if a is not None] |
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super(MLP, self).__init__(applications, **kwargs) |
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@property |
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def input_dim(self): |
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return self.dims[0] |
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@input_dim.setter |
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def input_dim(self, value): |
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self.dims[0] = value |
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@property |
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def output_dim(self): |
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return self.dims[-1] |
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@output_dim.setter |
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def output_dim(self, value): |
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self.dims[-1] = value |
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def _push_allocation_config(self): |
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if not len(self.dims) - 1 == len(self.linear_transformations): |
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raise ValueError |
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for input_dim, output_dim, layer in \ |
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equizip(self.dims[:-1], self.dims[1:], |
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self.linear_transformations): |
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layer.input_dim = input_dim |
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layer.output_dim = output_dim |
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if getattr(self, 'use_bias', None) is not None: |
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layer.use_bias = self.use_bias |
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mila-udem /
blocks
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