我正在尝试制作一个基本的图像分类项目，该项目可以区分任意两个或多个类别。我已经编写了以下代码，并遇到了标题中提到的错误。我该如何解决这个错误？以下是我的代码

from keras.preprocessing import imagefrom keras.preprocessing.image import ImageDataGeneratorfrom keras.models import Sequentialfrom keras.layers import Conv2D,MaxPooling2Dfrom keras.layers import Activation,Dropout,Flatten,Densefrom keras import backend as kimport numpy as npimg_width,img_height = 64,64train_data_dir = 'dataset/training_set'validation_data_dir = 'dataset/test_set'nb_train_samples = 1000nb_validation_samples = 100epochs = 50batch_size = 20if k.image_data_format() == 'channel-first':   input_shape = (3,img_width,img_height)else:   input_shape = (img_width,img_height,3)train_datagen = ImageDataGenerator(rescale = 1./255,                                   shear_range = 0.2,                                   zoom_range = 0.2,                                   horizontal_flip = True)test_datagen = ImageDataGenerator(rescale = 1./255)train_generator = train_datagen.flow_from_directory(                                      train_data_dir,                                      target_size=(img_width,img_height),                                      batch_size = batch_size,                                      class_mode = 'binary')validation_generator=test_datagen.flow_from_directory(                                    validation_data_dir,                                    target_size = (img_width,img_height),                                    batch_size = batch_size,                                    class_mode = 'binary')model = Sequential()model.add(Conv2D(32,(3,3),input_shape = input_shape))model.add(Activation('relu'))model.add(MaxPooling2D(pool_size = (2,2)))model.summary()model.add(Conv2D(32,(3,3)))model.add(Activation('relu'))model.add(MaxPooling2D(pool_size = (2,2)))#model.add(Conv2D(64,(3,3)))#model.add(Activation('relu'))#model.add(MaxPooling2D(pool_size = (2,2)))model.summary()model.add(Flatten())model.add(Dense(units = 128, activation = "relu"))model.add(Dense(units = 1, activation = "sigmoid"))model.add(Dropout(0.5))optimizer = keras.optimizers.sgd(lr = 0.1)model.compile(loss='binary_crossentropy',              optimizer = optimizer,              metrices = ['accuracy'])model.fit_generator(train_generator,                steps_per_epoch = nb_train_samples // batch_size,                epochs = epochs,                validation_data = validation_generator,                validation_steps = nb_validation_samples // batch_size)

除了最后一段代码外，每行都运行正常。在运行最后一段代码时，我遇到了以下错误

(‘Some keys in session_kwargs are not supported at this time: %s’, dict_keys([‘metrices’]))

回答：

您是否应该使用 metrics 而不是 metrices？