我在进行猫狗图像分类项目的机器学习模型训练时，遇到一个错误，我认为这可能是由于输入数据类型或模型结构的缺陷所导致的。
创建目录、预处理和数据集变量：
# Get project files!wget https://cdn.freecodecamp.org/project-data/cats-and-dogs/cats_and_dogs.zip!unzip cats_and_dogs.zipPATH = 'cats_and_dogs'train_dir = os.path.join(PATH, 'train')validation_dir = os.path.join(PATH, 'validation')test_dir = os.path.join(PATH, 'test')# Get number of files in each directory. The train and validation directories# each have the subdirecories "dogs" and "cats".total_train = sum([len(files) for r, d, files in os.walk(train_dir)])total_val = sum([len(files) for r, d, files in os.walk(validation_dir)])total_test = len(os.listdir(test_dir))# Variables for pre-processing and training.batch_size = 128epochs = 15IMG_HEIGHT = 150IMG_WIDTH = 150train_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=0.00392156862)validation_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=0.00392156862)test_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=0.00392156862)train_data_gen = train_image_generator.flow_from_directory(directory=train_dir, target_size=(IMG_HEIGHT,IMG_WIDTH),class_mode='binary',batch_size=batch_size,shuffle=True,color_mode='rgb')val_data_gen = validation_image_generator.flow_from_directory(directory=validation_dir, target_size=(IMG_HEIGHT,IMG_WIDTH),class_mode='binary',batch_size=batch_size,shuffle=True,color_mode='rgb')test_data_gen = test_image_generator.flow_from_directory(directory=PATH,target_size=(IMG_HEIGHT,IMG_WIDTH),classes=['test'],class_mode='input',batch_size=batch_size,shuffle=False,color_mode='rgb')构建和训练模型：
model = tf.keras.models.Sequential()model.add(tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(32,32,3)))model.add(tf.keras.layers.MaxPooling2D((2,2)))model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))model.add(tf.keras.layers.MaxPooling2D((2,2)))model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))model.add(tf.keras.layers.Flatten())model.add(tf.keras.layers.Dense(32))model.add(tf.keras.layers.Dense(16))model.add(tf.keras.layers.Dense(1, activation='sigmoid'))model.summary()model.compile(optimizer="Adam",loss='binary_crossentropy',metrics=['accuracy'])#traininghistory = model.fit(train_data_gen,steps_per_epoch=round(total_train)//batch_size,epochs=epochs,batch_size=batch_size)错误截图：InvalidArgumentError
关于可能导致问题的想法有哪些？
回答：
在您的生成器中，您指定了IMG_HEIGHT = 150和IMG_WIDTH = 150。然而在您的模型中，您有以下代码
model.add(tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(32,32,3)))请将输入形状更改为(150, 150, 3)。另外，您的代码
train_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=0.00392156862)validation_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=0.00392156862)test_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=0.00392156862)因为它们是相同的，您可以只使用
gen=tf.keras.preprocessing.image.ImageDataGenerator(rescale=0.00392156862)然后
train_data_gen = gen.flow_from_directory(directory=train_dir, target_size=(IMG_HEIGHT,IMG_WIDTH),class_mode='binary',batch_size=batch_size,shuffle=True,color_mode='rgb')val_data_gen = gen.flow_from_directory(directory=validation_dir, target_size=(IMG_HEIGHT,IMG_WIDTH),class_mode='binary',batch_size=batch_size,shuffle=True,color_mode='rgb')test_data_gen = gen.flow_from_directory(directory=PATH,target_size=(IMG_HEIGHT,IMG_WIDTH),classes=['test'],class_mode='input',batch_size=batch_size,shuffle=False,color_mode='rgb')此外，在model.fit中，由于您使用的是生成器，不应包含batch_size参数，因为它已经在生成器中指定了。我也不建议包含steps_per_epoch参数。如果您不包含它，model.fit将会在内部正确计算它。
