CNN - I know in advance which class my input cannot be

Question

I have a CNN built with Keras, based on a multilayer perceptron. The last layer is softmax.

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 248, 248, 32)      896       
_________________________________________________________________
activation_1 (Activation)    (None, 248, 248, 32)      0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 124, 124, 32)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 122, 122, 32)      9248      
_________________________________________________________________
activation_2 (Activation)    (None, 122, 122, 32)      0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 61, 61, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 59, 59, 64)        18496     
_________________________________________________________________
activation_3 (Activation)    (None, 59, 59, 64)        0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 29, 29, 64)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 53824)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 64)                3444800   
_________________________________________________________________
activation_4 (Activation)    (None, 64)                0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 9)                 585       
_________________________________________________________________
activation_5 (Activation)    (None, 9)                 0         
=================================================================
Total params: 3,474,025
Trainable params: 3,474,025
Non-trainable params: 0
_________________________________________________________________

Now, during classification, there are some cases in which I know in advance my input is not class A, or B, or C (because of some specific business logic).

Example : I have an image X that I want to classify. My CNN can classify between 10 different classes. I know, without doing anything, that my input is not class A or class B.

Is there any way I could theoretically take advantage of this knowledge to help the classification process ?

Since the last layer is in softmax, I was thinking of setting the values of the output neurons I know are not the right answer to 0 and redistribute this value among potentially right neurons.

I hope my question was clear enough.

Answer 1

Typically you have $P(y|x) = \text{softmax} (f(x;\theta))$, where $f$ is your network. But given a mask $m$ of invalid classes, you can simply say $P(y|x,m) = \text{softmax}(f(x;\theta) - km)$, where $k$ is set to some very large constant. And this works fine both at train time and at test time.

Since the last layer is in softmax, I was thinking of setting the values of the output neurons I know are not the right answer to 0 and redistribute this value among potentially right neurons.

The approach described above is basically this, except there is no need to redistribute the pre-softmax scores at all. The post-softmax probabilities would be automatically "redistributed" by virtue of softmax's normalizing properties.