how to calculate the confidence of a softmax layer - tensorflow

Question

I am working on a multi-class computer vision classification task and using a CNN with FC layers stacked on top using softmax activation, the problem is that lets say im classifying animals categories, if i predicted what a rock image is it will return a high probability for the most similar category of animals due to using softmax activation that returns a probabilistic distribution compressed between 0 and 1. what can i use to determine the confidence of my models probability output to say whether i can rely on these probabilities or not.
PS:I dont want to add a no_label class
Is it possible using keras functional api to have 2 outputs of the model the pre_softmax and the softmax output without updating the weights according to a linear activation which is the pre_softmax layer since the training would be affected

Answer 1

Is it possible using keras functional api to have 2 outputs of the model the pre_softmax and the softmax output without updating the weights according to a linear activation which is the pre_softmax layer since the training would be affected
Yes. You can do it like this
input = tf.keras.layers.Input((128,128,3))
x = tf.keras.layers.Conv2D(32,3)(input)
x = tf.keras.layers.MaxPooling2D()(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(128)(x)
non_softmax_output = tf.keras.layers.Dense(10)(x)
softmax_output = tf.keras.layers.Softmax()(non_softmax_output)
model = tf.keras.models.Model(inputs=input,outputs=[non_softmax_output,softmax_output])
model.summary()
>>>
Model: "model_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_3 (InputLayer) [(None, 128, 128, 3)] 0
conv2d_1 (Conv2D) (None, 126, 126, 32) 896
max_pooling2d_1 (MaxPooling (None, 63, 63, 32) 0
2D)
flatten_1 (Flatten) (None, 127008) 0
dense_23 (Dense) (None, 128) 16257152
dense_24 (Dense) (None, 10) 1290
softmax (Softmax) (None, 10) 0
=================================================================
Total params: 16,259,338
Trainable params: 16,259,338
Non-trainable params: 0
_________________________________________________________________
The easier alternative is to just work with the predictions from the softmax layer. You don't gather much from the linear layer without the activation. Those weights by themselves do not mean much. You could instead define a function outside the model that changes the predictions based on some threshold value
Assume you define only 1 output in the above model with a softmax layer. You can define a function like this to get predictions based on some threshold value you choose
def modify_predict(test_images,threshold):
predictions = model.predict(test_images)
max_values = np.max(predictions,axis=1)
labels = np.argmax(predictions,axis=1)
new_predictions = np.where(max_values > threshold, labels, 999) #You can use any indicator here instead of 999 for your no_label class
return new_predictions

Answer 2

On the first part of your question, the only way you can know how your
model will behave on non-animal pictures is by having non-animal pictures
in your data.
There are two options
The first is to include non-animal pictures in the training set (and dev and test sets), and to train the model to distinguish between animal / non-animal.
You could either build a separate binary classification model to distinguish animal/non-animal (as alrady suggesetd in comments), or you could integrate it into one model by having a
'non-animal' class. (Although I recognise you indicate this last option is
not something you want to do).
The second is to include non-animal pictures in the dev and test sets, but not in the training set. You can't then train the model to distinguish between animal and non-animal, but you can at least measure how it behaves on
non-animal pictures, and perhaps create some sort of heuristic for selecting only some of your model's predictions. This seems like a worse option to me, even though it's generally accepted that dev and test sets can come from a different distribution to the training set. It's something one might do if there were only a small number of non-animal pictures available, but that surely can't be the case here.
There is, for example, a large labelled image database
available at https://www.image-net.org/index.php