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sense.py
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sense.py
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from keras import backend as K
from keras.models import Sequential
from keras.engine.topology import Layer
from keras.engine import InputSpec
from keras import initializations, activations
import theano
import theano.tensor as T
class MatrixFactorization(Layer):
# Embeddings for matrix factorization
#
def __init__(self, , vector_dim, input_dim, output_dim = 1, init = 'uniform', activation = 'sigmoid', **kwargs):
self.input_dim = input_dim
self.vector_dim = vector_dim
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.output_dim = output_dim
kwargs['input_dtype'] = 'int32'
if self.input_dim:
kwargs['input_shape'] = (self.input_dim, )
super(SenseEmbedding, self).__init__(**kwargs)
def build(self, input_shape):
E = self.num_entities
R = self.num_relations
self.tupleEmbed = self.init((E, E, self.vector_dim))
self.relationEmbed = self.init((R, self.vector_dim))
self.trainable_weights = [self.W_g, self.W_s]
def call(self, x, mask = None):
tupleEmbed = self.tupleEmbed
relationEmbed = self.relationEmbed
nb = x.shape[0]
entity_embeddings = tupleEmbed[x[:, 0], x[:, 2]]
relation_embeddings = relationEmbed[x[:, 1]]
dot_prod = K.batch_dot(entity_embeddings, relation_embeddings, axes = 1)
return self.activation(dot_prod)
def get_output_shape_for(self, input_shape):
assert input_shape and len(input_shape) == 2
return (input_shape[0], 1)
def get_config(self):
return {"name":self.__class__.__name__,
"input_dim":self.input_dim,
"vector_dim":self.vector_dim,
"vocab_dim" :self.vocab_dim,
"init":self.init.__name__,
"activation":self.activation.__name__}