model.print_model_eval() cur_params, cur_margins = model.sess.run( [model.params, model.margin], feed_dict=model.all_train_feed_dict) cur_influences = model.get_influence_on_test_loss( test_indices=[test_idx], train_idx=np.arange(num_train), force_refresh=False) params[counter, :] = np.concatenate(cur_params) margins[counter, :] = cur_margins influences[counter, :] = cur_influences if temp == 0: actual_loss_diffs[counter, :], predicted_loss_diffs[ counter, :], indices_to_remove[ counter, :] = experiments.test_retraining( model, test_idx, iter_to_load=0, force_refresh=False, num_steps=2000, remove_type='maxinf', num_to_remove=num_to_remove) np.savez('output/hinge_results', temps=temps, indices_to_remove=indices_to_remove, actual_loss_diffs=actual_loss_diffs, predicted_loss_diffs=predicted_loss_diffs, influences=influences)
data_sets=data_sets, initial_learning_rate=initial_learning_rate, keep_probs=keep_probs, decay_epochs=decay_epochs, mini_batch=False, train_dir='output', log_dir='log', model_name='spam_logreg_lbfgs') tf_model.train() test_idx = 8 actual_loss_diffs, predicted_loss_diffs_cg, indices_to_remove = experiments.test_retraining( tf_model, test_idx, iter_to_load=0, force_refresh=False, num_to_remove=500, remove_type='maxinf', random_seed=0) # LiSSA np.random.seed(17) predicted_loss_diffs_lissa = tf_model.get_influence_on_test_loss( [test_idx], indices_to_remove, approx_type='cg', approx_params={ 'scale': 25, 'recursion_depth': 5000, 'damping': 0, 'batch_size': 1,
train_dir='output', log_dir='log', model_name='cifar_all_cnn_c') num_steps = 500000 model.train(num_steps=num_steps, iter_to_switch_to_batch=10000000, iter_to_switch_to_sgd=10000000) iter_to_load = num_steps - 1 test_idx = 6 actual_loss_diffs, predicted_loss_diffs, indices_to_remove = experiments.test_retraining( model, test_idx=test_idx, iter_to_load=iter_to_load, num_to_remove=100, num_steps=30000, remove_type='maxinf', force_refresh=True) np.savez('output/cifar_all_cnn_c_iter-500k_retraining-100.npz', actual_loss_diffs=actual_loss_diffs, predicted_loss_diffs=predicted_loss_diffs, indices_to_remove=indices_to_remove) # Load the trained model model.load_checkpoint(499999) # compute influence values for the set of test points test_indices = [6]