keras モジュールインポート時のエラー

MNISTを用いた認識をwebの資料などを見ながら試してみました
そこで自分が書いた文字も認識できるのかをやってみようと思い、以下のサイトを見ながら
動作するかどうか試してみました

下部に記載している②のコードを実行しようとすると以下のようにエラーとなります

ImportError                               Traceback (most recent call last)
<ipython-input-82-c08a6616303b> in <module>()
      2 import numpy as np
      3 from keras.models import load_model
----> 4 from keras.preprocessing.image import array_to_img, img_to_array,list_pictures, load_img
      5 
      6 

ImportError: cannot import name 'list_pictures'

from keras.preprocessing.image import array_to_img, img_to_array,list_pictures, load_img
↑からlist_picturesだけを取り除くとエラーは発生しないのですが
kerasのリファレンス等を見てもlist_picturesというものは発見できず行き詰っています
kerasのバージョンは2.2.0です
更新などで使えなくなったのでしょうか？
初歩的な質問ですがアドバイス宜しくお願い致します

①kerasのサンプルコード

python
'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 10

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

model.save('mnist_model.h5')

②自分で書いた文字を評価するコード

python
# coding:utf-8
import keras
import numpy as np
from keras.models import load_model
from keras.preprocessing.image import array_to_img, img_to_array,list_pictures, load_img


model = load_model('mnist_model.h5')

for picture in list_pictures(r'C:\Users\Desktop\pic'):
    X = []
    img = img_to_array(
        load_img(picture, target_size=(28, 28), grayscale=True))
    X.append(img)

    X = np.asarray(X)
    X = X.astype('float32')
    X = X / 255.0

    features = model.predict(X)

    print('----------')
    print(picture)
    print(features.argmax())
    print('----------')