copy_test_images(test_images) predictions = load_and_predict(model) # clean temp files if os.path.exists("./train"): shutil.rmtree('./train') if os.path.exists("./test"): shutil.rmtree('./test') df = pd.DataFrame(data=predictions, columns=['image_id', 'label']) df = df.set_index(['image_id']) if os.path.exists(SUBMISSION_FILE): os.remove(SUBMISSION_FILE) print(df.head()) print('Writing submission') df.to_csv(SUBMISSION_FILE) ld.distribute_images(0.95, CSV_LOCATION, TRAINING_IMAGES_INPUT) history = train_model_naive_split() lp.plot_result(history) store_prediction()
pathlib.Path(f'./test/1/').mkdir(parents=True, exist_ok=True) test_images = os.listdir(TEST_IMAGES_INPUT) ld.copy_test_images(test_images, TEST_IMAGES_INPUT) predictions = load_and_predict(model) # clean temp files if os.path.exists("./train"): shutil.rmtree('./train') if os.path.exists("./test"): shutil.rmtree('./test') df = pd.DataFrame(data=predictions, columns=['image_id', 'label']) df = df.set_index(['image_id']) if os.path.exists(SUBMISSION_FILE): os.remove(SUBMISSION_FILE) print(df.head()) print('Writing submission') df.to_csv(SUBMISSION_FILE) history = train_model_naive_split() all_history = [] all_history.append(history) lp.plot_result('./output/graphs', all_history) store_prediction()
pathlib.Path(f'./test/1/').mkdir(parents=True, exist_ok=True) test_images = os.listdir(TEST_IMAGES_INPUT) ld.copy_test_images(test_images, TEST_IMAGES_INPUT) predictions = load_and_predict(model) # clean temp files if os.path.exists("./train"): shutil.rmtree('./train') if os.path.exists("./test"): shutil.rmtree('./test') df = pd.DataFrame(data=predictions, columns=['image_id', 'label']) df = df.set_index(['image_id']) if os.path.exists(SUBMISSION_FILE): os.remove(SUBMISSION_FILE) print(df.head()) print('Writing submission') df.to_csv(SUBMISSION_FILE) history = train_model_naive_split() all_history = [] all_history.append(history) lp.plot_result('./', all_history) store_prediction()