Keras CNN具有不同的图像大小

from keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

# 假设我们希望所有图像都具有224x224的大小
target_size = (224, 224)

train_generator = datagen.flow_from_directory(
    'data/train',
    target_size=target_size,
    batch_size=32,
    class_mode='binary')

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, Dense

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(None, None, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(GlobalAveragePooling2D())
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

from keras.layers import AdaptiveMaxPooling2D

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(None, None, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(AdaptiveMaxPooling2D((7, 7)))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    width_shift_range=0.1,
    height_shift_range=0.1)

train_generator = datagen.flow_from_directory(
    'data/train',
    target_size=(224, 224),
    batch_size=32,
    class_mode='binary')