for l in liks:
x_data = l.get_free_state()
//make parameters if needed
if len(x_data):
x = tf.placeholder("float64")
l.make_tf_array(x)
//"build" the functions
After Change
F2 = GPflow.likelihoods.Likelihood.predict_density(l, self.Fmu, self.Fvar, self.Y)
//compile and run the functions:
F1 = tf.Session().run(F1, feed_dict={x: x_data})
F2 = tf.Session().run(F2, feed_dict={x: x_data})
self.failUnless(np.allclose(F1, F2, 1e-6, 1e-6))