如下组织目录

main_dir
-train_dir
``person1_fake_dir
```person1 fake image
```person1 fake image
---etc
``person1_real_dir
---person1 real image
---person1 real image
--- etc
--person2_fake_dir
--- person2 fake image
--- person2 fake image
--- etc
--person2_real_dir
---person2 real image
---person2 real image
---etc
.
.
.
--personN_fake_dir
---personN fake image
---personN fake image
---etc
--personN_real_dir
---personN real image
---personN real image
--- etc

-test_dir
same structure as train_dir but put test images here

-valid_dir
same structure as train_dir but put validation images here

如果你有N个人，那么你将有2个XN班级

然后，你可以使用tf.keras.preprocessing.image.ImageDataGenerator（）。flow_from_directory（）输入数据。文档在这里。你不必担心裁剪图像，只需将流中的图像大小设置为（256,256）。下面的代码显示了你需要的其余代码

data_gen=tf.keras.preprocessing.image.ImageDataGenerator(resize=1/255)
train_gen=data_gen.flow_from_directory(train_dir, target_size=(224,224), color-mode='grayscale')
valid_gen=data_gen.flow_from_directory(valid_dir, target_size=(224,224), color-mode='grayscale', shuffle=False)
test_gen=data_gen.flow_from_directory(test_dir, target_size=(224,224), color-mode='grayscale', shuffle=False)
model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.CategoricalCrossentropy(), metrics='accuracy')
history=model.fit(train_gen, epochs=20, verbose=1)
accuracy=model.evaluate (test_gen)[1]*100
print ('Model accuracy is ', accuracy)

请注意，在一般情况下，你的模型将无法辨别真伪。它应该适用于从1到N的人员。你可以尝试将所有伪图像放在一个类目录中，并将所有真实图像放在另一个类目录中并对其进行训练，但我怀疑在一般情况下，从假冒中分辨出真假效果不佳。