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)
# 使用图片识别器
clf = ImageClassifier(verbose=True)
# 给其训练数据和标签，训练的最长时间可以设定，假设为1分钟，autokers会不断找寻最优的网络模型
clf.fit(train_data, train_labels, time_limit=1 * 60)
# 找到最优模型后，再最后进行一次训练和验证
clf.final_fit(train_data,
train_labels,
test_data,
test_labels,
retrain=True)
# 给出评估结果
y = clf.evaluate(test_data, test_labels)
print("evaluate:", y)
# 给一个图片试试预测是否准确
img = load_img(PREDICT_IMG_PATH)
x = img_to_array(img)
x = x.astype('float32') / 255
x = np.reshape(x, (1, IMAGE_SIZE, IMAGE_SIZE, 3))
print("x shape:", x.shape)
# 最后的结果是一个numpy数组，里面是预测值4，意味着是马，说明预测准确
y = clf.predict(x)
print("predict:", y)
# 导出我们生成的模型
clf.export_keras_model(MODEL_DIR)
# 加载模型
model = load_model(MODEL_DIR)
# 将模型导出成可视化图片
plot_model(model, to_file=MODEL_PNG)