我使用 CNN 根據 Keras 中的圖像對汽車損壞進行了預測，無論它們是否嚴重。每次我為同一數據集運行代碼并且沒有更改其他參數時，預測的類別和準確性都會發生變化。我嘗試重新啟動內核并為模型設置種子，希望獲得一致的結果。我是 python 的新手，所以請幫助我每次都獲得相同的結果。import randomrandom.seed(801)# Importing the Keras libraries and packagesfrom keras.models import Sequentialfrom keras.layers import Conv2Dfrom keras.layers import MaxPooling2Dfrom keras.layers import Flattenfrom keras.layers import Densefrom keras.layers import Dropout# Initialising the CNNclassifier = Sequential()# Step 1 - Convolutionclassifier.add(Conv2D(64, (2, 2), input_shape = (64, 64, 3), activation = 'relu'))# Step 2 - Poolingclassifier.add(MaxPooling2D(pool_size = (2, 2)))# Adding a second convolutional layerclassifier.add(Conv2D(64, (2, 2), activation = 'relu'))classifier.add(MaxPooling2D(pool_size = (2, 2)))# Step 3 - Flatteningclassifier.add(Flatten())# Adding dropoutclassifier.add(Dropout(0.2))# Step 4 - Full connectionclassifier.add(Dense(units = 128, activation = 'relu'))# Adding dropoutclassifier.add(Dropout(0.2))classifier.add(Dense(units = 1, activation = 'sigmoid'))# Compiling the CNNclassifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])# Part 2 - Fitting the CNN to the imagesfrom keras.preprocessing.image import ImageDataGeneratortrain_datagen = ImageDataGenerator(rescale = 1./255,                                  # shear_range = 0.2,                                  # zoom_range = 0.2,                                   horizontal_flip = True)test_datagen = ImageDataGenerator(rescale = 1./255)#train_labels = keras.utils.to_categorical(train_labels,num_classes)#test_labels = keras.utils.to_categorical(test_labels,num_classes)training_set = train_datagen.flow_from_directory('C:/Users/Allianz/Desktop/Image Processing/car-damage-detective-neokt/app/2 category/training',                                                 target_size = (64, 64),                                                 batch_size = 16,                                                 class_mode = 'binary')