Python TypeError:flow（）缺少1个必需的位置参数：'；x'；

我试着运行这段代码，但还是卡住了

在这段代码中，我使用了一个经过预训练的神经resnet50，我试图提取一个深层特征并预测我的类

如果有人有这个错误，请告诉我如何修复它

谢谢

NUM_CLASSES = 2
CHANNELS = 3
IMAGE_RESIZE = 224
RESNET50_POOLING_AVERAGE = 'avg'
DENSE_LAYER_ACTIVATION = 'softmax'
OBJECTIVE_FUNCTION = 'binary_crossentropy'
LOSS_METRICS = ['accuracy']
NUM_EPOCHS = 10
EARLY_STOP_PATIENCE = 3
STEPS_PER_EPOCH_TRAINING = 10
STEPS_PER_EPOCH_VALIDATION = 10
batch_size = 32
from keras.models import load_model
BATCH_SIZE_TRAINING = 100
BATCH_SIZE_VALIDATION = 100
image_size = IMAGE_RESIZE
WEIGHTS_PATH = "C:\\Users\\Desktop\\RESNET  \\resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5"
model = Sequential()
train_data_dir = "C:\\Users\\Desktop\\RESNET"
model = ResNet50(include_top=True, weights='imagenet')
model.layers.pop()
model = Model(input=model.input,output=model.layers[-1].output)
model.summary()
model.compile(loss='binary_crossentropy', optimizer=SGD(lr=0.01, momentum=0.9), metrics=['binary_accuracy'])

data_dir = "C:\\Users\\Desktop\\RESNET"
data_generator = ImageDataGenerator(preprocessing_function=preprocess_input)

train_datagenerator = ImageDataGenerator(rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    validation_split=0.2)
train_generator = train_datagenerator.flow_from_directory(
    train_data_dir,
    target_size=(image_size, image_size), 
    batch_size=BATCH_SIZE_TRAINING,
    class_mode='categorical', shuffle=False, subset='training') # set as training data


validation_generator = train_datagenerator.flow_from_directory(
    train_data_dir, # same directory as training data kifkif
    target_size=(image_size, image_size), 
    batch_size=BATCH_SIZE_TRAINING,
    class_mode='categorical', shuffle=False, subset='validation') # set as validation data

generator = data_generator.flow(batch_size=batch_size)
batch_size = 32
X_train = np.zeros((len(train_generator.images_ids_in_subset),2048))
Y_train = np.zeros((len(train_generator.images_ids_in_subset),2))
nb_batches = int(len(train_generator.images_ids_in_subset) / batch_size) + 1

如果您对这个问题有任何疑问，请告诉我

感谢您的帮助

删除此行

generator = data_generator.flow(batch_size=batch_size)

如果您的代码到此结束，它将不会起任何作用

flow

方法用于转换ram数据中已有的数据，但您的代码没有