these don't need to be lambdas
@@ -162,8 +162,8 @@ class Optimization(object):
# If smoothness parameter is too small, essentially no smoothing
# Just output the eigen vector corresponding to min
return tf.cond(self.smooth_placeholder < 1E-8,
- lambda: self.tf_min_eig_vec(),
- lambda: self.tf_smooth_eig_vec())
+ self.tf_min_eig_vec,
+ self.tf_smooth_eig_vec)
# Using autograph to automatically handle the control flow of multi_steps()
multi_steps_tf = autograph.to_graph(self.multi_steps)
