transform

commit 194510b0a3c0718ad8137de758e2646d5a4a93e7
parent f3629a2ce1f606baa6447e1d7fb4ee25a4cc58ff
Author: Étienne Simon <esimon@esimon.eu>
Date:   Fri, 11 Apr 2014 19:30:02 +0200

Add class for Translations

Diffstat:
A
relations/translation.py
 | 
53
+++++++++++++++++++++++++++++++++++++++++++++++++++++

1 file changed, 53 insertions(+), 0 deletions(-)
diff --git a/relations/translation.py b/relations/translation.py
@@ -0,0 +1,53 @@
+#!/usr/bin/env python2
+
+import numpy
+import theano
+import theano.tensor as T
+import theano.sparse as S
+
+class Translations(object):
+    """ Translations class.
+
+    This class has one parameter:
+    R -- the translations
+    """
+    def __init__(self, rng, number, dimension, tag):
+        """ Initialise the parameter.
+
+        Keyword arguments:
+        rng -- module for random number generation
+        number -- number of relation
+        dimension -- dimension of the embeddings
+        tag -- name of the relations for parameter declaration
+        """
+
+        self.number = number
+        self.dimension = dimension
+
+        R_bound = numpy.sqrt(6. / dimension)
+        R_values = rng.uniform(low=-R_bound, high=R_bound, size=(number, dimension))
+        R_values = R_values / numpy.sqrt(numpy.sum(R_values **2, axis=1))
+        self.R = theano.shared(name=tag, value=numpy.asarray(R_values, dtype=theano.config.floatX))
+
+        self.params = [R]
+
+    def L1_norm(self):
+        """ Compute the L1-norm of the relations parameter. """
+        return T.sum(T.abs(self.R))
+
+    def sqrL2_norm(self):
+        """ Compute the squared L2-norm of the relations parameter. """
+        return T.sum(T.sqr(self.R))
+
+    def apply(self, input, relations):
+        """ Apply the given relations to a given input. """
+        return S.dot(relations, self.R)+inputs
+
+    def sgd_updates(self, cost, learning_rate):
+        """ Compute the updates to perform a SGD step w.r.t. a given cost.
+
+        Keyword arguments:
+        cost -- The cost to optimise.
+        learning_rate -- The learning rate used for gradient descent.
+        """
+        return [(self.R, self.R - learning_rate * T.grad(cost=cost, wrt=self.R))]