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()