def setUpTestData(cls):
        batch_num = 0
        section_num = 0
        voter_num = 0
        party_num = 0
        position_num = 0
        candidate_num = 0
        num_elections = 2
        voters = list()
        positions = dict()
        for i in range(num_elections):
            election = Election.objects.create(name='Election {}'.format(i))
            positions[str(election.name)] = list()

            num_batches = 2
            for j in range(num_batches):
                batch = Batch.objects.create(year=batch_num, election=election)
                batch_num += 1

                num_sections = 2 if j == 0 else 1
                for k in range(num_sections):
                    section = Section.objects.create(
                        section_name=str(section_num)
                    )
                    section_num += 1

                    num_students = 2
                    for l in range(num_students):
                        voter = User.objects.create(
                            username='user{}'.format(voter_num),
                            first_name=str(voter_num),
                            last_name=str(voter_num),
                            type=UserType.VOTER
                        )
                        voter.set_password('voter')
                        voter.save()
                        voter_num += 1

                        VoterProfile.objects.create(
                            user=voter,
                            batch=batch,
                            section=section
                        )

                        voters.append(voter)

            num_positions = 3
            for i in range(num_positions):
                position = CandidatePosition.objects.create(
                    position_name='Position {}'.format(position_num),
                    election=election
                )

                positions[str(election.name)].append(position)

                position_num += 1

            num_parties = 3
            for j in range(num_parties):
                party = CandidateParty.objects.create(
                    party_name='Party {}'.format(party_num),
                    election=election
                )
                party_num += 1

                if j != 2:  # Let every third party have no candidates.
                    num_positions = 3
                    for k in range(num_positions):
                        position = positions[str(election.name)][k]

                        candidate = Candidate.objects.create(
                            user=voters[candidate_num],
                            party=party,
                            position=position,
                            election=election
                        )

                        Vote.objects.create(
                            user=voters[candidate_num],
                            candidate=candidate,
                            election=election
                        )

                        candidate_num += 1

        # Let's give one candidate an additional vote to really make sure that
        # we all got the correct number of votes.
        Vote.objects.create(
            user=voters[0],
            # NOTE: The voter in voter[1] is a Position 1 candidate of
            #       Party 1, where the voter in voter[0] is a member.
            candidate=Candidate.objects.get(user=voters[1]),
            election=Election.objects.get(name='Election 0')
        )

        _admin = User.objects.create(username='admin', type=UserType.ADMIN)
        _admin.set_password('root')
        _admin.save()
def testWithoutMomentum(self, dtype):
    with self.session(use_gpu=True):
      var0 = variables.Variable([1.0, 2.0], dtype=dtype)
      var1 = variables.Variable([3.0, 4.0], dtype=dtype)
      grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
      grads1 = constant_op.constant([0.01, 0.01], dtype=dtype)
      opt = rmsprop.RMSPropOptimizer(
          learning_rate=2.0, decay=0.9, momentum=0.0, epsilon=1.0)
      update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
      variables.global_variables_initializer().run()

      rms0 = opt.get_slot(var0, "rms")
      self.assertIsNotNone(rms0)
      rms1 = opt.get_slot(var1, "rms")
      self.assertIsNotNone(rms1)
      mom0 = opt.get_slot(var0, "momentum")
      self.assertIsNotNone(mom0)
      mom1 = opt.get_slot(var1, "momentum")
      self.assertIsNotNone(mom1)

      # Fetch params to validate initial values
      self.assertAllClose([1.0, 2.0], var0.eval())
      self.assertAllClose([3.0, 4.0], var1.eval())
      # Step 1: the rms accumulators where 1. So we should see a normal
      # update: v -= grad * learning_rate
      update.run()
      # Check the root mean square accumulators.
      self.assertAllCloseAccordingToType(
          np.array([0.901, 0.901]), rms0.eval())
      self.assertAllCloseAccordingToType(
          np.array([0.90001, 0.90001]), rms1.eval())
      # Check the parameters.
      self.assertAllCloseAccordingToType(
          np.array([
              1.0 - (0.1 * 2.0 / math.sqrt(0.901)),
              2.0 - (0.1 * 2.0 / math.sqrt(0.901))
          ]), var0.eval())
      self.assertAllCloseAccordingToType(
          np.array([
              3.0 - (0.01 * 2.0 / math.sqrt(0.90001)),
              4.0 - (0.01 * 2.0 / math.sqrt(0.90001))
          ]), var1.eval())
      # Step 2: the root mean square accumulators contain the previous update.
      update.run()
      # Check the rms accumulators.
      self.assertAllCloseAccordingToType(
          np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), rms0.eval())
      self.assertAllCloseAccordingToType(
          np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), rms1.eval())
      # Check the parameters.
      self.assertAllCloseAccordingToType(
          np.array([
              1.0 - (0.1 * 2.0 / math.sqrt(0.901)) -
              (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001)),
              2.0 - (0.1 * 2.0 / math.sqrt(0.901)) -
              (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001))
          ]), var0.eval())
      self.assertAllCloseAccordingToType(
          np.array([
              3.0 - (0.01 * 2.0 / math.sqrt(0.90001)) -
              (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5)),
              4.0 - (0.01 * 2.0 / math.sqrt(0.90001)) -
              (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5))
          ]), var1.eval())