我正在构建一个用于多类分类的简单CNN模型。训练和测试数据按照flow_from_directory函数的要求，根据类别子目录存放在data_path中。
这是我在数据上构建和训练模型的代码:
from tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Dropout, Flatten, Dense, Conv2D, MaxPooling2Dfrom tensorflow.keras.preprocessing.image import ImageDataGenerator# 构建模型model = Sequential()model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(40, 24, 1)))model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))model.add(Flatten())model.add(Dense(128, activation='relu'))model.add(Dropout(0.5))model.add(Dense(12, activation='softmax'))model.compile('binary_crossentropy', 'SGD', ['accuracy'])# 初始化生成器generator = ImageDataGenerator(rescale=1./255, horizontal_flip=True, fill_mode='nearest', validation_split=0.2)def get_train_images(): train_images = generator.flow_from_directory(os.path.join(data_path, 'train'), target_size=(40, 24, 1), batch_size=32, color_mode='grayscale', class_mode='categorical', subset='training', shuffle=True)def get_validation_images(): validation_images = generator.flow_from_directory(os.path.join(data_path, 'train'), target_size=(40, 24, 1), batch_size=32, color_mode='grayscale', class_mode='categorical', subset='validation', shuffle=True)# 训练模型model.fit(get_train_images, validation_data=get_validation_images, epochs=20)fit函数给出了以下错误:
File "C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py", line 108, in _method_wrapper return method(self, *args, **kwargs) File "C:\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1049, in fit data_handler = data_adapter.DataHandler( File "C:\Python38\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py", line 1104, in __init__ adapter_cls = select_data_adapter(x, y) File "C:\Python38\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py", line 968, in select_data_adapter raise ValueError(ValueError: Failed to find data adapter that can handle input: <class 'method'>, <class 'NoneType'>看起来是某种兼容性问题。我使用的是tensorflow版本2.3.1。能有人指出我做错了什么并帮助我解决这个问题吗？
谢谢！
回答：
为了解决这个问题，我需要更改两件事:
flow_from_directory的目标尺寸应该是(40, 24)，而不是(40, 24, 1)- 我有用于获取
flow_from_directory生成器的函数包装器，并且我将这些函数作为参数传递给fit函数。相反，我需要将这些包装器的返回值传递给fit函数
正确的方法应该是:
model.fit(get_train_images(), validation_data=get_validation_images(), epochs=20)