model.py 文件源码

def calc_log_loss(self, Pairwise, Question, Answer, Review, TermtoTermR, TermtoTermP, Question_I, Answer_I, Review_I):

        #print 'Doing for item %d'%(i)

        shape1      = tf.shape(Pairwise)
        shape2      = tf.shape(Answer)

        nq      = shape1[0]
        nr      = shape1[1]
        na      = shape2[1]

        pairwise    = tf.reshape(Pairwise, [-1, self.PairwiseDim])
        pairwise    = tf.reshape(tf.matmul(pairwise, self.theta), [nq, nr])

        termTotermR     = tf.sparse_reshape(TermtoTermR, [-1, self.V])
        termTotermR     = tf.reshape(tf.sparse_tensor_dense_matmul(termTotermR, self.RelvPar), [nq, nr])

        QProj       = tf.sparse_tensor_dense_matmul(Question_I, self.A)
        RProjR      = tf.sparse_tensor_dense_matmul(Review_I, self.B)
        BilinearR   = tf.matmul(QProj, tf.transpose(RProjR))

        Relevance   = tf.nn.softmax(pairwise + termTotermR + BilinearR)

        termTotermP     = tf.sparse_reshape(TermtoTermP, [-1, self.V])
        termTotermP     = tf.reshape(tf.sparse_tensor_dense_matmul(termTotermP, self.PredPar), [nq, na, nr])

        AProj       = tf.sparse_tensor_dense_matmul(tf.sparse_reshape(Answer_I, [-1, self.V]), self.X)
        RProjP      = tf.sparse_tensor_dense_matmul(Review_I, self.Y)
        BilinearP   = tf.reshape(tf.matmul(AProj, tf.transpose(RProjP)), [nq, na, nr])

        Prediction  = BilinearP + termTotermP
        Prediction      = tf.expand_dims(Prediction[:,0,:], 1) - Prediction
        Prediction  = Prediction[:,1:,:]
        Prediction  = tf.sigmoid(Prediction)

        MoE         = tf.reduce_sum(tf.multiply(Prediction, tf.expand_dims(Relevance, axis = 1)), axis = 2)
        accuracy_count  = tf.cast(tf.shape(tf.where(MoE > 0.5))[0], tf.float64)
        count       = nq * na 

        log_likelihood  = tf.reduce_sum(tf.log(MoE))
        R1      = tf.reduce_sum(tf.square(self.A)) + tf.reduce_sum(tf.square(self.B)) 
        R2      = tf.reduce_sum(tf.square(self.X)) + tf.reduce_sum(tf.square(self.Y))

        log_likelihood  -= self.Lambda * (R1 + R2)

        return -1*log_likelihood, MoE, Relevance