conv_split_cub.py [363:404]:
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                            model.sample_weights: task_sample_weights[offset:offset+batch_size],
                            model.training_iters: num_iters, model.train_step: iters, model.keep_prob: 0.5, 
                            model.train_phase: True}

                if model.imp_method == 'VAN':
                    feed_dict[model.output_mask] = logit_mask
                    _, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)

                elif model.imp_method == 'EWC':
                    feed_dict[model.output_mask] = logit_mask
                    # If first iteration of the first task then set the initial value of the running fisher
                    if task == 0 and iters == 0:
                        sess.run([model.set_initial_running_fisher], feed_dict=feed_dict)
                    # Update fisher after every few iterations
                    if (iters + 1) % model.fisher_update_after == 0:
                        sess.run(model.set_running_fisher)
                        sess.run(model.reset_tmp_fisher)
                    
                    _, _, loss = sess.run([model.set_tmp_fisher, model.train, model.reg_loss], feed_dict=feed_dict)

                elif model.imp_method == 'PI':
                    feed_dict[model.output_mask] = logit_mask
                    _, _, _, loss = sess.run([model.weights_old_ops_grouped, model.train, model.update_small_omega, 
                                              model.reg_loss], feed_dict=feed_dict)

                elif model.imp_method == 'MAS':
                    feed_dict[model.output_mask] = logit_mask
                    _, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)

                elif model.imp_method == 'S-GEM':
                    if task == 0:
                        logit_mask[:] = 0
                        logit_mask[task_labels[task]] = 1.0
                        feed_dict[model.output_mask] = logit_mask
                        # Normal application of gradients
                        _, loss = sess.run([model.train_first_task, model.agem_loss], feed_dict=feed_dict)
                    else:
                        # Randomly sample a task from the previous tasks
                        prev_task = np.random.randint(0, task)
                        # Set the logit mask for the randomly sampled task
                        logit_mask[:] = 0
                        logit_mask[task_labels[prev_task]] = 1.0
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conv_split_cub_hybrid.py [380:421]:
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                            model.sample_weights: task_sample_weights[offset:offset+batch_size],
                            model.training_iters: num_iters, model.train_step: iters, model.keep_prob: 0.5,
                            model.train_phase: True}

                if model.imp_method == 'VAN':
                    feed_dict[model.output_mask] = logit_mask
                    _, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)

                elif model.imp_method == 'EWC':
                    feed_dict[model.output_mask] = logit_mask
                    # If first iteration of the first task then set the initial value of the running fisher
                    if task == 0 and iters == 0:
                        sess.run([model.set_initial_running_fisher], feed_dict=feed_dict)
                    # Update fisher after every few iterations
                    if (iters + 1) % model.fisher_update_after == 0:
                        sess.run(model.set_running_fisher)
                        sess.run(model.reset_tmp_fisher)
                    
                    _, _, loss = sess.run([model.set_tmp_fisher, model.train, model.reg_loss], feed_dict=feed_dict)

                elif model.imp_method == 'PI':
                    feed_dict[model.output_mask] = logit_mask
                    _, _, _, loss = sess.run([model.weights_old_ops_grouped, model.train, model.update_small_omega, 
                                              model.reg_loss], feed_dict=feed_dict)

                elif model.imp_method == 'MAS':
                    feed_dict[model.output_mask] = logit_mask
                    _, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)

                elif model.imp_method == 'S-GEM':
                    if task == 0:
                        logit_mask[:] = 0
                        logit_mask[task_labels[task]] = 1.0
                        feed_dict[model.output_mask] = logit_mask
                        # Normal application of gradients
                        _, loss = sess.run([model.train_first_task, model.agem_loss], feed_dict=feed_dict)
                    else:
                        # Randomly sample a task from the previous tasks
                        prev_task = np.random.randint(0, task)
                        # Set the logit mask for the randomly sampled task
                        logit_mask[:] = 0
                        logit_mask[task_labels[prev_task]] = 1.0
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