我已经用图像数据集训练了一个CNN模型，并将模型保存为classifier.h5。现在，我需要加载这个模型来进行预测。我按照以下方式实现了代码，但遇到了一个错误 _maybe_load_initial_epoch_from_ckpt() takes 2 positional arguments but 3 were given。是什么导致了这个错误，如何解决它？
这是我的CNN模型。我将其保存为classifier.h5
EPOCHS = 60batch_size = 32iter_per_epoch = len(x_train)//batch_sizeval_per_epoch = len(x_test)//batch_sizeprint(len(x_train))print(len(x_test))classifier = Sequential()classifier.add(Conv2D(32, 3, activation='relu', padding='same', input_shape=(img_w, img_h, 3)))classifier.add(BatchNormalization())classifier.add(MaxPooling2D())classifier.add(Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_uniform'))classifier.add(BatchNormalization())classifier.add(MaxPooling2D())classifier.add(Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_uniform'))classifier.add(BatchNormalization())classifier.add(MaxPooling2D())classifier.add(Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_uniform'))classifier.add(BatchNormalization())classifier.add(MaxPooling2D())classifier.add(Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_uniform'))classifier.add(BatchNormalization())classifier.add(MaxPooling2D())classifier.add(Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_uniform'))classifier.add(BatchNormalization())classifier.add(MaxPooling2D())classifier.add(Flatten())classifier.add(Dropout(0.5))classifier.add(Dense(128, activation='relu'))classifier.add(Dense(4, activation='softmax'))classifier.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])classifier.summary()train_datagen = ImageDataGenerator( rotation_range=25, zoom_range=0.1, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.2, horizontal_flip=True)val_datagen = ImageDataGenerator()train_gen = train_datagen.flow(x_train, y_train, batch_size=batch_size)val_gen = val_datagen.flow(x_test, y_test, batch_size=batch_size)m = classifier.fit_generator( train_gen, steps_per_epoch=iter_per_epoch, epochs=EPOCHS, validation_data=val_gen, validation_steps=val_per_epoch, verbose=1)现在在另一个脚本中，我试图加载这个模型，但遇到了上述错误
from keras.models import load_modelfrom tensorflow import Graphimport tensorflow as tfimg_w, img_h = 256, 256gpuoptions = tf.compat.v1.GPUOptions(allow_growth=True)graph = Graph()with graph.as_default(): tf_session = tf.compat.v1.Session( config=tf.compat.v1.ConfigProto(gpu_options=gpuoptions)) with tf_session.as_default(): model = load_model('classifier.h5')path = "./images/test/Leaf Blast/blast__0_2632.jpg"img = cv2.imread(path)test_image = cv2.resize(img, (int(img_w*1.5), int(img_h*1.5)))test_image = preprocess(test_image)test_image = edge_and_cut(test_image)test_image = np.array(test_image)test_image = np.expand_dims(test_image, axis=0)img_class = model.predict(test_image)print(img_class)以下是我的完整错误追踪：
Traceback (most recent call last): File "error.py", line 171, in <module> img_class = model.predict(test_image) File "C:\Users\namba\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\training_v1.py", line 983, in predict return func.predict( File "C:\Users\namba\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 708, in predict return predict_loop( File "C:\Users\namba\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 259, in model_iteration initial_epoch = model._maybe_load_initial_epoch_from_ckpt(initial_epoch, mode)TypeError: _maybe_load_initial_epoch_from_ckpt() takes 2 positional arguments but 3 were given回答：
我不知道是什么导致了这个错误。但是，以下更改解决了问题。我删除了Graph部分并加载了我的模型。现在它显示结果了。
class_dict = {'Bacterial leaf blight': 0, 'Brown spot': 1, 'Leaf Blast': 2, 'Leaf smut': 3 }class_names = list(class_dict.keys())img_w, img_h = 256, 256model = load_model('classifier.h5')path = "./images/test/Leaf Blast/blast__0_2632.jpg"img = cv2.imread(path)test_image = cv2.resize(img, (int(img_w*1.5), int(img_h*1.5)))test_image = preprocess(test_image)test_image = edge_and_cut(test_image)test_image = np.array(test_image)test_image = np.expand_dims(test_image, axis=0)img_class = model.predict(test_image)img_class = img_class.flatten()m = max(img_class)for index, item in enumerate(img_class): if item == m: pred_class = class_names[index]print(pred_class)