def train_autokeras(RESIZE_TRAIN_IMG_DIR, TRAIN_CSV_DIR, RESIZE_TEST_IMG_DIR, TEST_CSV_DIR, TIME): #Load images train_data, train_labels = load_image_dataset( csv_file_path=TRAIN_CSV_DIR, images_path=RESIZE_TRAIN_IMG_DIR) test_data, test_labels = load_image_dataset( csv_file_path=TEST_CSV_DIR, images_path=RESIZE_TEST_IMG_DIR) train_data = train_data.astype('float32') / 255 test_data = test_data.astype('float32') / 255 print("Train data shape:", train_data.shape) clf = ImageClassifier(verbose=True) clf.fit(train_data, train_labels, time_limit=TIME) clf.final_fit(train_data, train_labels, test_data, test_labels, retrain=True) y = clf.evaluate(test_data, test_labels) print("Evaluate:", y) #Predict the category of the test image img = load_img(PREDICT_IMG_PATH) x = img_to_array(img) x = x.astype('float32') / 255 x = np.reshape(x, (1, RESIZE, RESIZE, 3)) print("x shape:", x.shape) y = clf.predict(x) print("predict:", y) clf.load_searcher().load_best_model().produce_keras_model().save(MODEL_DIR) #Save model architecture diagram model = load_model(MODEL_DIR) plot_model(model, to_file=MODEL_PNG)
y_test = [] base_path = "../data-deep-fashion-women/img/" #Load the data from local file into a dataframe df = pd.read_csv('../data-deep-fashion-women/img/WOMEN/labels_test.csv') print(len(df)) for index, row in df.iterrows(): #print(row[0], row[1]) ss = base_path + row[0] #print(ss) img = image.load_img(ss, target_size=(224, 224)) img_data = image.img_to_array(img) image_data_np = np.array(img_data) x_test.append(image_data_np) y_test.append(row[1]) from autokeras.image.image_supervised import load_image_dataset from autokeras.image.image_supervised import ImageClassifier clf = ImageClassifier(verbose=True) clf.fit(x_train, y_train, time_limit=10 * 60 * 60) # 10 hours clf.final_fit(x_train, y_train, x_test, y_test, retrain=True) y = clf.evaluate(x_test, y_test) print(y) clf.export_autokeras_model('./_models/nas_1.h5') clf.export_keras_model('./_models/nas_2.h5') clf.load_searcher().load_best_model().produce_keras_model().save( './_models/nas_3.h5')