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')
