train_val_loss_score.append([loss,np.mean(avg_loss[:, 0]),val_fscore,test_loss, video_scores,kt,sp])

from ortools.algorithms.python import knapsack_solver

osolver = knapsack_solver.KnapsackSolver(
    knapsack_solver.KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER,
    'test')

def knapsack_ortools(values, weights, items, capacity ):
    scale = 1000
    values = np.array(values)
    weights = np.array(weights)
    values = (values * scale).astype(np.int)
    weights = (weights).astype(np.int)
    capacity = capacity
    osolver.init(values.tolist(), [weights.tolist()], [capacity])
    computed_value = osolver.solve()
    packed_items = [x for x in range(0, len(weights))
                    if osolver.best_solution_contains(x)]
    return packed_items