6b8a9a5f0e738d98904ff7c46c79a44bfdd56686,GPflow/sgpr.py,SGPR,build_predict,#SGPR#Any#Any#,76

Before Change

        mean = tf.matmul(tf.transpose(tmp2), c)
        if full_cov:
            var = self.kern.K(Xnew) + tf.matmul(tf.transpose(tmp2), tmp2) - tf.matmul(tf.transpose(tmp1), tmp1)
            var = tf.tile(tf.expand_dims(var, 2), tf.pack([1,1, tf.shape(self.Y)[1]]))
        else:
            var = self.kern.Kdiag(Xnew) + tf.reduce_sum(tf.square(tmp2), 0) - tf.reduce_sum(tf.square(tmp1), 0)
            var = tf.tile(tf.expand_dims(var, 1), tf.pack([1, tf.shape(self.Y)[1]]))
        return mean + self.mean_function(Xnew), var

After Change

        if full_cov:
            var = self.kern.K(Xnew) + tf.matmul(tf.transpose(tmp2), tmp2)\
                - tf.matmul(tf.transpose(tmp1), tmp1)
            shape = tf.pack([1, 1, tf.shape(self.Y)[1]])
            var = tf.tile(tf.expand_dims(var, 2), shape)
        else:
            var = self.kern.Kdiag(Xnew) + tf.reduce_sum(tf.square(tmp2), 0)\
                - tf.reduce_sum(tf.square(tmp1), 0)
            shape = tf.pack([1, tf.shape(self.Y)[1]])

Instances