找不到可以处理输入的数据适配器：<class 'tensorflow.python.keras.preprocessing.image.ImageDataGenerator'>, <class 'NoneType'>答案

【问题标题】：Failed to find data adapter that can handle input: <class 'tensorflow.python.keras.preprocessing.image.ImageDataGenerator'>, <class 'NoneType'>找不到可以处理输入的数据适配器：<class 'tensorflow.python.keras.preprocessing.image.ImageDataGenerator'>, <class 'NoneType'>
【发布时间】：2021-07-23 18:33:40
【问题描述】：

我尝试为 CNN 进行数据扩充，但收到错误消息“无法找到可以处理输入的数据适配器：, ”。谁能帮帮我？

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
(x_train, y_train) , (x_test, y_test) = datasets.cifar10.load_data()
img_width, img_height, img_num_channels = 32, 32, 3
x_train = x_train.astype('float32')       
x_test = x_test.astype('float32')
x_train /= 255.0              
x_test /= 255.0
input_shape = (img_width, img_height, img_num_channels)

CNN used
train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=45, shear_range=0.2, 
                                   zoom_range=0.2, horizontal_flip=True)

validation_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow(x_train, y_train, batch_size=100)

batch_size=100

model.fit_generator(train_generator,
                    steps_per_epoch = 2000 //batch_size, epochs =2,
                    validation_data = validation_datagen,
                    validation_steps = 800 //batch_size)
score = model.evaluate(x_test, y_test) 
print('Test loss:', score[0]) 
print('Test accuracy:', score[1])
predictions = model.predict([x_test])
#print(predictions)

print(np.argmax(predictions[0]))

img_path = x_test[0]
print(img_path.shape)
if(len(img_path.shape) == 3):
    plt.imshow(np.squeeze(img_path))
elif(len(img_path.shape) == 2):
    plt.imshow(img_path)
else:
    print("Image cannot be shown")

【问题讨论】：

标签： python tensorflow machine-learning keras deep-learning

【解决方案1】：

您没有定义验证生成器。添加这个：

validation_generator = validation_datagen.flow(x_test, y_test, batch_size=100)

在训练中：

model.fit_generator(train_generator,
                steps_per_epoch = 2000 //batch_size, epochs =2,
                validation_data = validation_generator, #change this
                validation_steps = 800 //batch_size)

【讨论】：

【解决方案2】：

按照 Kaveh 的回答中的说明进行操作。此外，您将像素重新缩放两次。所以删除

x_train /= 255.0              
x_test /= 255.0

【讨论】：