model.fit_generator（）中的错误。作为一个Python的初级程序员，我不确定这个错误指示什么。
我正在尝试用python迁移学习来用imagenet训练VGG19，我在回调中遇到了一个值错误。有人能建议我应该对这个代码做些什么修改吗？
我尝试在colab中执行以下代码，但遇到错误

!apt-get install -y -qq software-properties-common python-software-properties module-init-tools
!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null
!apt-get update -qq 2>&1 > /dev/null
!apt-get -y install -qq google-drive-ocamlfuse fuse
from google.colab import auth
auth.authenticate_user()
from oauth2client.client import GoogleCredentials
creds = GoogleCredentials.get_application_default()
import getpass
!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL
vcode = getpass.getpass()
!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}
!mkdir -p drive
!google-drive-ocamlfuse drive
!pip install opencv-python
!pip install opencv-contrib-python
!apt update && apt install -y libsm6 libxext6
!pip install -q keras
from glob import glob

import cv2
import numpy as np

from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split

from keras.utils import to_categorical
from keras.models import Model
from keras.layers import Dropout, Dense, Flatten
from keras.optimizers import SGD
from keras.losses import categorical_crossentropy
from keras.regularizers import l2
from keras.applications.vgg19 import VGG19
model = VGG19(include_top=False, weights='imagenet', pooling='avg')
for layer in model.layers:
    layer.trainable = False
x = model.output
predictions = Dense(7, activation='softmax')(x)
model_final = Model(input=model.input, output=predictions)
from keras.callbacks import ReduceLROnPlateau
lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=4, verbose=1)
model_final.compile(loss=categorical_crossentropy,
                  optimizer=SGD(lr=0.001, momentum=0.9, nesterov=True),
                  metrics=['accuracy'])
model_final.fit(np.array(X_train), np.array(y_train),
              batch_size=32,
              epochs=10,
              verbose=1,
              validation_split=0.1,
              shuffle=True)
for layer in model_final.layers[7:]:
  layer.trainable = True
model_final.compile(loss=categorical_crossentropy,
                  optimizer=SGD(lr=0.001, momentum=0.9, nesterov=True),
                  metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator
train_generator = ImageDataGenerator(
    featurewise_center = True,
    featurewise_std_normalization = True,
    rotation_range=30,
    shear_range=0.2,
    zoom_range=0.2,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True)

train_generator.fit(np.array(X_train))

test_generator = ImageDataGenerator(
    featurewise_center = True,
    featurewise_std_normalization = True)

test_generator.fit(np.array(X_train))

model_final.fit_generator(train_generator.flow(np.array(X_train), np.array(y_train), batch_size=32),
                          validation_data=test_generator.flow(np.array(X_test), np.array(y_test)),
                          steps_per_epoch=len(X_train)/32, 
                          epochs=50)

ValueError                                Traceback (most recent call last)
<ipython-input-39-f9af6d0d8994> in <module>()
      2                           validation_data=test_generator.flow(np.array(X_test), np.array(y_test)),
      3                           steps_per_epoch=len(X_train)/32,
----> 4                           epochs=50)

2 frames
/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
     66     if (val_gen and not isinstance(validation_data, Sequence) and
     67             not validation_steps):
---> 68         raise ValueError('`validation_steps=None` is only valid for a'
     69                          ' generator based on the `keras.utils.Sequence`'
     70                          ' class. Please specify `validation_steps` or use'

ValueError: `validation_steps=None` is only valid for a generator based on the `keras.utils.Sequence` class. Please specify `validation_steps` or use the `keras.utils.Sequence` class.