ZeroDivisionError：使用ImageDataGenerator和.flow_from_directory时整数除法或模数为零

Question

我正在尝试运行使用VGG16的代码https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/5.3-using-a-pretrained-convnet.ipynb。我的数据集来自https://www.kaggle.com/c/dogs-vs-cats/data。我在一个名为sample的文件夹中有三个文件夹测试，训练，验证。我使用train文件夹中未使用的图像并将其存储在validate中。

我查看了Solution 1，Solution 2或Solution 3错误 ZeroDivisionError：整数除法或模数为零，但我似乎无法解决我面临的问题。错误发生在features[i * batch_size : (i + 1) * batch_size] = features_batch。

  import keras
    keras.__version__
    from keras.applications import VGG16

    conv_base = VGG16(weights='imagenet',
                      include_top=False,
                      input_shape=(150, 150, 3))

    conv_base.summary()
    import os
    import numpy as np
    from keras.preprocessing.image import ImageDataGenerator

    base_dir = 'C:\\Users\\K\\Documents\\Code\\sample'
    test_dir = os.path.join(base_dir, 'test')
    train_dir = os.path.join(base_dir, 'train')
    validation_dir = os.path.join(base_dir, 'validation')


    datagen = ImageDataGenerator(rescale=1./255)
    batch_size = 20

    def extract_features(directory, sample_count):
        features = np.zeros(shape=(sample_count, 4, 4, 512))
        labels = np.zeros(shape=(sample_count))
        generator = datagen.flow_from_directory(directory,target_size=(150, 150),batch_size=batch_size,class_mode='binary')
        i = 0
        for inputs_batch, labels_batch in generator:
            features_batch = conv_base.predict(inputs_batch)
            features[i * batch_size : (i + 1) * batch_size] = features_batch
            labels[i * batch_size : (i + 1) * batch_size] = labels_batch
            i += 1
            if i * batch_size >= sample_count:
                # Note that since generators yield data indefinitely in a loop,
                # we must `break` after every image has been seen once.
                break

        return features, labels

    train_features, train_labels = extract_features(train_dir, 2000)
    validation_features, validation_labels = extract_features(validation_dir, 1000)
    test_features, test_labels = extract_features(test_dir, 1000)