def load_images(): x_train, y_train = load_image_dataset(csv_file_path="train/label.csv", images_path="train") print(x_train.shape) print(y_train.shape) x_test, y_test = load_image_dataset(csv_file_path="test/label.csv", images_path="test") print(x_test.shape) print(y_test.shape) return x_train, y_train, x_test, y_test
def train_model(): clf = ak.ImageClassifier() train_data, train_labels = load_image_dataset( csv_file_path=train_data_dir + "/label.csv", images_path=train_data_dir) validation_data, validation_labels = load_image_dataset( csv_file_path=validation_data_dir + "/label.csv", images_path=validation_data_dir) clf.fit(train_data, train_labels) clf.final_fit(train_data, train_labels, validation_data, validation_labels, retrain=True) y = clf.evaluate(validation_data, validation_labels) print("auto CNN classifier accuracy: %f" % y) clf.load_searcher().load_best_model().produce_keras_model().save( 'shallowCNN_model.h5')
# (Version 0.2.13 as of 9/8/18) from autokeras.image_supervised import ImageClassifier, load_image_dataset train_path = 'datasets/dogsCats/dogCat128rgb_train' train_labels = 'datasets/dogsCats/dogsCats_train.csv' validation_path = 'datasets/dogsCats/dogCat128rgb_val' validation_labels = 'datasets/dogsCats/dogsCats_val.csv' from os import listdir from os.path import isfile, join files = [f for f in listdir(train_path) if isfile(join(train_path, f))] print(files) x_train, y_train = load_image_dataset(csv_file_path=train_labels, images_path=train_path) print(x_train.shape) print(y_train.shape) x_val, y_val = load_image_dataset(csv_file_path=validation_labels, images_path=validation_path) print(x_val.shape) print(y_val.shape) # Searching for the Best Model clf = ImageClassifier(verbose=True, searcher_args={'trainer_args': { 'max_iter_num': 25 }}) clf.fit(x_train, y_train,
elif action_value_list[2].replace('\t', '') == 'false': rule_value_list.append(0.0) else: sys.exit("Invalid rule!!") rule_list.append(rule_value_list) line = fp.readline() my_file = Path(output_keras_mode) if agent_skip_autokeras is True and my_file.is_file() is True: print('Skip running AutoKeras ...\n') else: if test_csv_file_path is not None and test_images_path is not None: x_train, y_train = load_image_dataset( csv_file_path=train_csv_file_path, images_path=train_images_path) else: sys.exit("No train data input!!") if test_csv_file_path is not None and test_images_path is not None: x_test, y_test = load_image_dataset( csv_file_path=test_csv_file_path, images_path=test_images_path) do_evaluate = True else: do_evaluate = False if agent_percept == 'image' and agent_func_interpert_input == 'autokeras': clf = ImageClassifier(verbose=True) clf.fit(x_train, y_train, time_limit=autokeras_timeout) clf.final_fit(x_train, y_train, x_test, y_test, retrain=True)
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Sep 27 16:46:35 2018 @author: Mohamed Laradji """ from autokeras.image_supervised import ImageClassifier, load_image_dataset x_train, y_train = load_image_dataset(csv_file_path="data/train/labels.csv", images_path="data/train") x_val, y_val = load_image_dataset(csv_file_path="data/val/labels.csv", images_path="data/val") if __name__ == '__main__': clf = ImageClassifier(verbose=True) clf.fit(x_train, y_train, time_limit=12 * 60 * 60) # Currently results in errors. clf.final_fit(x_train, y_train, x_val, y_val, retrain=True) y = clf.evaluate(x_val, y_val)
# (x_train, y_train), (x_test, y_test) = mnist.load_data() # print(x_train.shape, x_train[0].shape, x_train.reshape(x_train.shape + (1,)).shape) NB_EPOCH = 200 BATCH_SIZE = 128 VERBOSE = 1 NB_CLASSES = 3 OPTIMIZER = RMSprop() # optimizer, explainedin this chapter N_HIDDEN = 128 VALIDATION_SPLIT = 0.2 # how much TRAIN is reserved for VALIDATION DROPOUT = 0.3 RESHAPED = 7575 x_train, y_train = load_image_dataset(csv_file_path="train.csv", images_path="images") # print('x_train', x_train[0].shape, type(x_train)) # x_train = x_train.reshape(x_train.shape[0], 396, 532, 4) print('x_train', x_train.shape) # print('y_train', y_train.shape) x_test, y_test = load_image_dataset(csv_file_path="test.csv", images_path="images") # print(x_test.shape) # print(y_test.shape) x_train = x_train.reshape(3077, RESHAPED) x_test = x_test.reshape(1025, RESHAPED)