The variables are not modified until the end of the optimization (instead of being fed to session.run calls) and evaluating a "back channel" Tensor gets the un-modified variables. Alternatively, use the fetches argument to optimizer.minimize to piggyback on the session.run calls which have the feeds specified:
import tensorflow as tf
def print_loss(loss_evaled, vector_evaled):
print(loss_evaled, vector_evaled)
vector = tf.Variable([7., 7.], 'vector')
loss = tf.reduce_sum(tf.square(vector))
optimizer = tf.contrib.opt.ScipyOptimizerInterface(
loss, method='L-BFGS-B',
options={'maxiter': 100})
with tf.Session() as session:
tf.global_variables_initializer().run()
optimizer.minimize(session,
loss_callback=print_loss,
fetches=[loss, vector])
print(vector.eval())
This prints Tensors with the updated values:
98.0 [ 7. 7.]
79.201 [ 6.29289341 6.29289341]
7.14396e-12 [ -1.88996808e-06 -1.88996808e-06]
[ -1.88996808e-06 -1.88996808e-06]