##### confusion matrix ##### confmat = confusion_matrix(y_true=test, y_pred=pre) print(confmat) ##### save csv ##### pre_dict = {'Recording': [], 'Result': []} test_dict = {'Recording': [], 'First_label': []} count = 0 for i in range(len(pre)): pre_dict['Recording'].append(count) pre_dict['Result'].append(pre[i]) test_dict['Recording'].append(count) test_dict['First_label'].append(test[i]) count += 1 pre = pd.DataFrame(pre_dict) test = pd.DataFrame(test_dict) test['Second_label'] = '' test['Third_label'] = '' pre.to_csv('./Result/1.csv', index=False) test.to_csv('./Result/2.csv', index=False) score_py3.score('./Result/1.csv', './Result/2.csv') ##### save process ##### log = pd.DataFrame([Accuracy, F1], index=['Accuracy', 'F1']) log.to_csv('./Result/log.csv') ##### save figure ##### if Args.show_plot: plt.ioff() plt.savefig("./Result/result.jpg") plt.show()
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data, '| train accuray: %.2f' % accuracy_train, '| test accuracy: %.2f' % accuracy_test) print('End Training') pre = pred_test.data.cpu().numpy() + 1 # pre = np.array(pred_test) + 1 test = test_y.data.cpu().numpy() + 1 # test = np.array(test_y.data) + 1 pre_dict = {'Recording': [], 'Result': []} test_dict = {'Recording': [], 'First_label': []} count = 0 for i in range(len(pre)): pre_dict['Recording'].append(count) pre_dict['Result'].append(pre[i]) test_dict['Recording'].append(count) test_dict['First_label'].append(test[i]) count += 1 pre = pd.DataFrame(pre_dict) test = pd.DataFrame(test_dict) # %% test['Second_label'] = '' test['Third_label'] = '' # %% pre.to_csv('1.csv', index=False) test.to_csv('2.csv', index=False) score('1.csv', '2.csv')
model = load_model("best_model.109-0.87.h5") pre_lists = pd.read_csv(path_test_ref) print(pre_lists.head()) pre_lists = np.array(pre_lists) pre_datas = np.array([get_feature(item, path_test) for item in pre_lists[:,0]]) #for Sequential model #pre_result = model.predict_classes(pre_datas) #for other model pre_r = model.predict(pre_datas) pre_result = np.argmax(model.predict(pre_datas), axis=1) print(pre_result.shape) result_label = [x+1 for x in pre_result] df1 = np.array([pre_lists[:,0]]).T df2 = np.array([result_label]).T df = np.hstack((df1, df2)) header = np.array(['Recording', 'Result']) answer = np.vstack((header,df)) dataframe = pd.DataFrame(answer) dataframe.to_csv(path_test_answer, header=False) print("predict finish") #Performance Evaluation score(path_test_answer, path_test_ref)