关于python：如何解决：’str’对象在keras中没有属性’data_format’

How to Solve: 'str' object has no attribute 'data_format' in keras

我正在尝试制作一个分类器，可以使用 keras 对猫和狗进行分类。
我只是想使用 ImageDataGenerator.flow_from_directory() 从图像中创建张量数据，这些数据被排序并保存在其路径在 train_path、test_path 等中给出的目录中。

这是我的代码：

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import numpy as np

import keras

from keras import backend as K

from keras.models import Sequential

from keras.layers import Activation

train_path = 'cats-and-dogs/train' test_path = 'cats-and-dogs/test' valid_path = 'cats-and-dogs/valid'

train_dir = 'cats-and-dogs/' test_dir = 'cats-and-dogs/' valid_dir = 'cats-and-dogs/'

train_batches = ImageDataGenerator.flow_from_directory(train_path, directory=train_dir, target_size=(200,200), classes=['dog','cat'], batch_size=10)

test_batches = ImageDataGenerator.flow_from_directory(test_path, directory=test_dir, target_size=(200,200), classes=['dog','cat'], batch_size=5)

valid_batches = ImageDataGenerator.flow_from_directory(valid_path, directory=valid_dir, target_size=(200,200), classes=['dog','cat'], batch_size=10)

但我在使用 python 3.5 时遇到以下错误：

/usr/local/lib/python3.5/site-packages/h5py/init.py:36:
FutureWarning: Conversion of the second argument of issubdtype from
float to np.floating is deprecated. In future, it will be treated
as np.float64 == np.dtype(float).type. from ._conv import
register_converters as _register_converters Using TensorFlow backend.
Traceback (most recent call last): File"CNNFromScratch.py", line
29, in
train_batches = ImageDataGenerator.flow_from_directory(train_path, directory=train_dir, target_size=(200,200), classes=['dog','cat'],
batch_size=10) File
"/usr/local/lib/python3.5/site-packages/keras/preprocessing/image.py",
line 565, in flow_from_directory
data_format=self.data_format,

AttributeError: 'str' object has no attribute 'data_format'

我能做些什么来解决这个问题？

ImageDataGenerator 的

方法 flow_from_directory 不是静态的。因此，您首先必须初始化类 ImageDataGenerator 的实例，然后调用此方法。

这应该可以工作：

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import numpy as np

import keras

from keras import backend as K

from keras.models import Sequential
from keras.layers import Activation
from keras.preprocessing.image import ImageDataGenerator

train_path = 'cats-and-dogs/train'
test_path = 'cats-and-dogs/test'
valid_path = 'cats-and-dogs/valid'

my_generator = ImageDataGenerator()

train_batches = my_generator.flow_from_directory(directory=train_path, target_size=(200,200), classes=['dog','cat'], batch_size=10)

test_batches = my_generator.flow_from_directory(directory=test_path, target_size=(200,200), classes=['dog','cat'], batch_size=5)

valid_batches = my_generator.flow_from_directory(directory=valid_path, target_size=(200,200), classes=['dog','cat'], batch_size=10)

查看文档以添加更多参数。