我试着训练一个基于MobilenetV2的图像分类器，但是loss不能收敛，我不确定我是否正确使用tensorflow

from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
import numpy as np

image = ... # Your image data
image = preprocess_input(image)

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
    preprocessing_function=preprocess_input,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)

from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model

base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)

model = Model(inputs=base_model.input, outputs=predictions)

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

batch_size = 32
epochs = 50

history = model.fit(
    train_datagen.flow(x_train, y_train, batch_size=batch_size),
    steps_per_epoch=len(x_train) // batch_size,
    epochs=epochs,
    validation_data=(x_val, y_val)
)

base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))