如何解决ImageDataGenerator with TypeError: 不支持的操作数类型 /: ''tuple'' 和 ''int''？

我正在使用 ImageDataGenerator 为图像分割执行数据增强。 这是我的 ImageDataGenerator 函数代码：

def train_generator(data_aug_dict,train_img_path,train_mask_path,height,width,batch_size,seed,img_dir,mask_dir,img_save_prefix,mask_save_prefix):
    # seed = 1

    image_datagen = ImageDataGenerator(**data_aug_dict)
    mask_datagen = ImageDataGenerator(**data_aug_dict)

    image_generator = image_datagen.flow_from_directory(
            directory = train_img_path,color_mode=''grayscale'',target_size=(height,width),batch_size=batch_size,save_to_dir=img_dir,save_prefix=img_save_prefix,seed=seed)

    mask_generator = mask_datagen.flow_from_directory(
            directory = train_mask_path,save_to_dir=mask_dir,save_prefix=mask_save_prefix,seed=seed)

    train_gen = zip(image_generator,mask_generator)

    for (img,mask) in train_gen:
          img,mask = data_adjust(img,mask)
          yield (img,mask)
        
def data_adjust(img,mask):
      # normalization: to range 0-1
      img = img / 255
      mask = mask / 255

      mask[mask > 0.5] = 1.0
      mask[mask <= 0.5] = 0.0
      return (img,mask)

定义函数后，我是这样调用函数的：

width = 256
height = 256

train_generator_args = dict(rotation_range=0.15,width_shift_range=0.05,height_shift_range=0.05,shear_range=0.05,zoom_range=0.05,horizontal_flip=True,fill_mode=''nearest'')

batch_size = 10
epochs = 100
seed = 1

gen_img_dir = ''/my_data_path''
gen_mask_dir = ''/my_data_path''
gen_img_prefix = "img"
gen_mask_prefix = "mask"


gen_images,gen_masks = train_generator(train_generator_args,gen_img_dir,gen_mask_dir,gen_img_prefix,gen_mask_prefix)

但是，我收到错误 TypeError: unsupported operand type(s) for /: ''tuple'' and ''int'' 我真的不明白这意味着什么，我该如何解决。 谁能帮忙解释一下？非常感谢！

解决方法

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inputs = keras.Input(shape=(None, 3))  # variable number of timesteps each with length 3
inputs = keras.Input(shape=(4, 3))     # 4 timesteps each with length 3
inputs = keras.Input(shape=(4, None))  # this is WRONG! you can''t do this. Number of features must be fixed

label = keras.layers.SimpleRNN(units=5, activation=''softmax'')(inputs)

inputs = keras.Input(shape=(3,))

cell = keras.layers.SimpleRNNCell(units=5,activation=''softmax'')
label = keras.layers.RNN(cell)(inputs)

model = keras.models.Model(inputs=inputs,outputs=label)
model.compile(optimizer=''rmsprop'',loss=''mae'',metrics=[''acc''])

data = np.array([[1,2,3],[3,4,5]])
labels = np.array([1,2])
model.fit(x=data,y=labels)

Traceback (most recent call last):
    File "/Users/david/Documents/code/python/Tensorflow/test.py",line 27,in <module>
        run()
    File "/Users/david/Documents/code/python/Tensorflow/test.py",line 21,in run
        label = keras.layers.RNN(cell)(inputs)
    File "/Users/david/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/layers/recurrent.py",line 619,in __call__
...
    File "/Users/david/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py",line 473,in __call__
        scale /= max(1.,(fan_in + fan_out) / 2.)
TypeError: unsupported operand type(s) for +: ''NoneType'' and ''int''

import mathdef f(x):    f=math.sqrt(1+(6*x+4)^2)    return flbound=int(input("Input lower bound for the integral"))ubound=int(input("Input upper bound for the integral"))n=int(input("Input number of intervals"))dx=((ubound-lbound)/n)integral=0for i in range(1,n):    integral=integral+dx*f(i*dx)print (integral)

Traceback (most recent call last):  File "C:\Users\******\Desktop\integrals.py", line 13, in <module>    integral=integral+dx*f(n*dx)  File "C:\Users\******\Desktop\integrals.py", line 3, in f    f=math.sqrt(1+(6*x+4)^2)TypeError: unsupported operand type(s) for ^: ''float'' and ''int''

f=math.sqrt(1+(6*x+4)**2)