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"""Introduces Lookup brick.""" |
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from blocks.bricks import Initializable |
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from blocks.bricks.base import application, lazy |
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from blocks.roles import WEIGHT, add_role |
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from blocks.utils import check_theano_variable, shared_floatx_nans |
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class LookupTable(Initializable): |
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"""Encapsulates representations of a range of integers. |
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Parameters |
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---------- |
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length : int |
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The size of the lookup table, or in other words, one plus the |
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maximum index for which a representation is contained. |
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dim : int |
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The dimensionality of representations. |
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Notes |
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----- |
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See :class:`.Initializable` for initialization parameters. |
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""" |
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has_bias = False |
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@lazy(allocation=['length', 'dim']) |
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def __init__(self, length, dim, **kwargs): |
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super(LookupTable, self).__init__(**kwargs) |
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self.length = length |
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self.dim = dim |
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@property |
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def W(self): |
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return self.parameters[0] |
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def _allocate(self): |
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self.parameters.append(shared_floatx_nans((self.length, self.dim), |
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name='W')) |
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add_role(self.parameters[-1], WEIGHT) |
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def _initialize(self): |
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self.weights_init.initialize(self.W, self.rng) |
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@application(inputs=['indices'], outputs=['output']) |
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def apply(self, indices): |
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"""Perform lookup. |
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Parameters |
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---------- |
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indices : :class:`~tensor.TensorVariable` |
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The indices of interest. The dtype must be integer. |
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Returns |
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------- |
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output : :class:`~tensor.TensorVariable` |
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Representations for the indices of the query. Has :math:`k+1` |
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dimensions, where :math:`k` is the number of dimensions of the |
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`indices` parameter. The last dimension stands for the |
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representation element. |
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""" |
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check_theano_variable(indices, None, ("int", "uint")) |
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output_shape = [indices.shape[i] |
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for i in range(indices.ndim)] + [self.dim] |
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return self.W[indices.flatten()].reshape(output_shape) |
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def get_dim(self, name): |
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if name == 'output': |
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return self.dim |
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if name == 'indices': |
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return 0 |
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mila-udem /
blocks
