def train_autokeras(self): #Load images train_data, train_labels = load_image_dataset(csv_file_path=self.TRAIN_CSV_DIR, images_path=self.RESIZE_TRAIN_IMG_DIR) test_data, test_labels = load_image_dataset(csv_file_path=self.TEST_CSV_DIR, images_path=self.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, path=self.TEMP_DIR, resume=False) clf.fit(train_data, train_labels, time_limit=self.TIME) clf.final_fit(train_data, train_labels, test_data, test_labels, retrain=True) evaluate_value = clf.evaluate(test_data, test_labels) print("Evaluate:", evaluate_value) # clf.load_searcher().load_best_model().produce_keras_model().save(MODEL_DIR) # clf.export_keras_model(MODEL_DIR) clf.export_autokeras_model(self.MODEL_DIR) #统计训练信息 dic = {} ishape = clf.cnn.searcher.input_shape dic['n_train'] = train_data.shape[0] #训练总共用了多少图 dic['n_classes'] = clf.cnn.searcher.n_classes dic['input_shape'] = str(ishape[0]) + 'x' + str(ishape[1]) + 'x' + str(ishape[2]) dic['history'] = clf.cnn.searcher.history dic['model_count'] = clf.cnn.searcher.model_count dic['best_model'] = clf.cnn.searcher.get_best_model_id() best_model = [item for item in dic['history'] if item['model_id'] == dic['best_model']] if len(best_model) > 0: dic['loss'] = best_model[0]['loss'] dic['metric_value'] = best_model[0]['metric_value'] dic['evaluate_value'] = evaluate_value self.traininfo = dic
def train_autokeras(self): time_limit = self.projectinfo['parameter_time'] #Load images train_data, train_labels = load_image_dataset(csv_file_path=self.project_train_labels_csv, images_path=self.project_resize_train_dir) test_data, test_labels = load_image_dataset(csv_file_path=self.project_test_labels_csv, images_path=self.project_resize_test_dir) train_data = train_data.astype('float32') / 255 test_data = test_data.astype('float32') / 255 self.log.info("Train data shape: %d" % train_data.shape[0]) clf = ImageClassifier(verbose=True, path=self.project_tmp_dir, resume=False) clf.fit(train_data, train_labels, time_limit=time_limit) clf.final_fit(train_data, train_labels, test_data, test_labels, retrain=True) evaluate_value = clf.evaluate(test_data, test_labels) self.log.info("Evaluate: %f" % evaluate_value) clf.export_autokeras_model(self.project_mod_path) #统计训练信息 dic = {} ishape = clf.cnn.searcher.input_shape dic['n_train'] = train_data.shape[0] #训练总共用了多少图 dic['n_classes'] = clf.cnn.searcher.n_classes dic['input_shape'] = str(ishape[0]) + 'x' + str(ishape[1]) + 'x' + str(ishape[2]) dic['history'] = clf.cnn.searcher.history dic['model_count'] = clf.cnn.searcher.model_count dic['best_model'] = clf.cnn.searcher.get_best_model_id() best_model = [item for item in dic['history'] if item['model_id'] == dic['best_model']] if len(best_model) > 0: dic['loss'] = best_model[0]['loss'] dic['metric_value'] = best_model[0]['metric_value'] dic['evaluate_value'] = evaluate_value return dic
if __name__ == '__main__': # 需要把数据放到 ~/.keras/dataset 中,不然下载会报错 (x_train, y_train), (x_test, y_test) = mnist.load_data() print(x_train.shape) # (60000, 28, 28) print('增加一个维度,以符合格式要求') x_train = x_train.reshape(x_train.shape + (1, )) print(x_train.shape) # (60000, 28, 28, 1) x_test = x_test.reshape(x_test.shape + (1, )) # 指定模型更新路径 clf = ImageClassifier(path="automodels/", verbose=True) # 限制为 4 个小时 # 搜索部分 gap = 6 clf.fit(x_train[::gap], y_train[::gap], time_limit=4 * 60 * 60) # 用表现最好的再训练一次 clf.final_fit(x_train[::gap], y_train[::gap], x_test, y_train, retrain=True) y = clf.evaluate(x_test, y_test) print(y) print("导出训练好的模型") clf.export_autokeras_model("automodels/auto_mnist_model") print("可视化模型") visualize("automodels/")
x_train.append(img) # x_train.reshape(256,256,3) y_train.append(0) x_train = np.array(x_train) y_train = np.array(y_train) for file_name in os.listdir("test/normal"): img = cv2.imread("test/normal/" + file_name) x_test.append(img) # x_train.reshape(256,256,3) y_test.append(0) for file_name in os.listdir("test/anomaly"): img = cv2.imread("test/anomaly/" + file_name) x_test.append(img) # x_train.reshape(256,256,3) y_test.append(0) x_test = np.array(x_test) y_test = np.array(y_test) print(x_train.shape) print(y_train.shape) clf = ImageClassifier(verbose=True) clf.fit(x_train, y_train, time_limit=12 * 60 * 60) clf.final_fit(x_train, y_train, x_test, y_test, retrain=True) clf.export_autokeras_model("./autokeras_model.bin") # Auto-Kerasで読み込めるモデルを保存 clf.export_keras_model("./keras_model.bin") # Kerasで読み込めるモデルを保存 acc = clf.evaluate(x_test, y_test)
from autokeras.image.image_supervised import load_image_dataset from autokeras.image.image_supervised import ImageClassifier x_train, y_train = load_image_dataset(csv_file_path="../data-mnist/train_label.csv", images_path="../data-mnist/train") print(len(x_train)) # print(x_train.shape) # print(y_train.shape) x_test, y_test = load_image_dataset(csv_file_path="../data-mnist/test_label.csv", images_path="../data-mnist/test") # print(x_test.shape) # print(y_test.shape) clf = ImageClassifier(verbose=True) clf.fit(x_train, y_train, time_limit= 14 * 60 * 60) # 14 hours clf.final_fit(x_train, y_train, x_test, y_test, retrain=True) y = clf.evaluate(x_test, y_test) print(y) model_file_name = "mnist_1_hour" clf.export_autokeras_model(model_file_name)
# x_test = x_test.reshape(x_test.shape + (1,)) # clf = ImageClassifier(path='output/', verbose=True, searcher_args={ # 'trainer_args': {'max_iter_num': 1, # 'max_no_improvement_num': 1}}) # clf.fit(x_train, y_train, time_limit=1 * 60 * 30) # clf.final_fit(x_train, y_train, x_test, y_test, retrain=True) # y = clf.evaluate(x_test, y_test) # print(y) (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.reshape(x_train.shape + (1,)) x_test = x_test.reshape(x_test.shape + (1,)) clf = ImageClassifier(verbose=True, searcher_args={'trainer_args':{'max_iter_num':7}}) clf.fit(x_train, y_train, time_limit=12 * 60 * 60) 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('output/auto_mnist_model') # alternative best_model = clf.cnn.best_model.produce_model() pickle_to_file(best_model, 'output/auto_mnist_best_model') print(best_model) # Step 2 : After the model training is complete, run examples/visualize.py, whilst passing the same path as parameter # if __name__ == '__main__': # visualize('~/automodels/')
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')